Original Research

Patient flow throughout the hospital has been shown to be adversely affected by discharge delays.¹ When hospitals are operating at peak capacity, these delays impact throughput, length of stay (LOS), and cost of care and block patients from the emergency department (ED), postanesthesia recovery unit (PACU), or home awaiting inpatient beds.^2-5 As patients wait in locations not ideal for inpatient care, they may suffer from adverse events and poor satisfaction.^3,6 Several studies have analyzed discharge timing as it relates to ED boarding of admitted patients and demonstrated that early discharges (EDCs) can impact boarding times.^7-9 A number of recent improvement efforts directed at moving discharges earlier in the day have been published.^10-15 However, these improvements are often targeted at specific units or teams within a larger hospital setting and only one is in the pediatric setting.

Lucile Packard Children’s Hospital Stanford (LPCHS) is a 311-bed quaternary care academic women and children’s hospital in Northern California. As our organization expanded, the demand for hospital beds often exceeded capacity. The challenge of overall demand was regularly compounded by a mismatch in bed availability timing – bed demand is early in the day and bed availability is later. This mismatch results in delays for admitted patients waiting in the ED and PACU. Organization leaders identified increasing early discharges (EDCs) as one initiative to contribute to improved patient flow.

Our organization aimed to increase the number of discharges before 11 am across the acute care units from an average of 8% in the 17 months prior to May 2015 to 25% by December 2016. Based on the average number and timing of planned admissions, they hypothesized that 25% of EDCs would decrease ED and PACU wait times.

METHODS

Setting

We focused our EDC interventions on the 87 acute care beds at LPCHS. All patients discharged from these beds were included in the study. We excluded patients discharged from intensive care, maternity, and nursery. Acute care includes five units, one focused on hematology/oncology (Unit A), one focused on cardiology (Unit B), and the others with a surgical and medical pediatric patient mix (Units C, D, and E). Although physician teams have primary units, due to unit size, patients on teams other than cardiology and hematology/oncology are often spread across multiple units wherever there is a bed (including Units A and B). Most of the frontline care physicians are residents supervised by attendings; however, a minority of patients are cared for by nurse practitioners (NPs) or physician assistants (PAs).

 

Improvement Team

In early 2015, we formed a multidisciplinary group inclusive of a case manager, frontline nurses, nurse management, pediatric residents, and hospitalist physicians with support from performance improvement. We periodically included physician leaders from other specialties to help initiate changes within their own clinical areas. Our group used Lean A3 thinking¹⁶ to gather information about the current state, formulate the problem statement, analyze the problem, and consider interventions implemented in three Plan–Do-Check-Act (PDCA) cycles. The A3 is a structured tool to analyze problems before jumping to solutions and communicate with stakeholders. We interviewed leaders, nurses, residents, case managers, etc. and observed work processes around discharge. We met weekly to follow data, assess results of interventions, and problem solve.

Barriers and Interventions

The first barrier we identified and addressed was poor identification and shared team mental model of potential EDC patients and lack of preparation when an EDC was identified. In intervention one starting May 2015, charge nurses on Units C, D, and E were each asked to identify one EDC for the following day. The identified patient was discussed at the previously existing afternoon daily unit huddle¹⁷ attended by nurse management, case management, and hospitalist leaders. Following the huddle, the resident, NP, or PA responsible for the patient was paged regarding the EDC plan and tasked with medication reconciliation and discharge paperwork. Others were asked to address their specific area of patient care for discharge (eg, case manager–supplies, nursing–education). The patient was identified on the unit white board with a yellow magnet (use of a visual control¹⁸), so that all would be aware of the EDC. An e-mail was sent to case management, nurse leaders, and patient placement coordinators regarding the planned EDCs. Finally, the EDCs were discussed during regularly scheduled huddles throughout the evening and into the next day.¹⁷

Despite this first intervention, we noted that progress toward increased EDCs was slow. Thus, we spent approximately seven days (spread over one month) further observing the work processes.¹⁹ Over five days, we asked each unit’s charge nurse every hour which patients were waiting to be discharged and the primary reason for waiting. From this information, we created a pareto chart demonstrating that rounds were the highest contributor to waiting (Appendix A). Thus, our second intervention was a daily physician morning huddle that the four nonsurgical physician teams (excluding cardiology, hematology/oncology) implemented one team at a time between November 2015 and February 2016. At the huddle, previously identified EDCs (located on any of the five units) were confirmed and preparatory work was completed (inclusive of the discharge order) before rounds. Further, the attending and resident physicians were to see the patient before or at the start of rounds.

Our working group still observed slow EDC improvement and sought feedback from all providers. EDC was described as “extra” work, apart from routine practices and culture. In addition, our interventions had not addressed most discharges on Units A and B. Consequently, our third intervention in February 2016 aimed to recognize and incentivize teams, units, and individuals for EDC successes. Units and/or physician teams that met 25% of EDCs the previous week were acknowledged through hospital-wide screensavers and certificates of appreciation signed by the Chief Nursing Officer. Units and/or physician teams that met 25% of EDC the previous month were acknowledged with a trophy. Residents received coffee cards for each EDC (though not without controversy among the improvement group as we acknowledged that all providers contributed to EDCs). Finally, weekly, we shared an EDC dashboard displaying unit, team, and organization performance at the hospital-wide leader huddle. We also e-mailed the dashboard regularly to division chiefs, medical directors, and nursing leaders.

 

Measures

Our primary outcome was percentage of EDCs (based on the time the patient left the room) across acute care. Secondary outcome measures were median wait times for an inpatient bed from the ED (time bed requested to the time patient left the ED) and the average PACU wait time (time the patient is ready to leave the PACU to time the patient left the PACU) per admitted patient. We also assessed balancing measures, including discharge satisfaction, seven-day readmission rates, and LOS. We obtained the mean discharge satisfaction score from the organization’s Press Ganey survey results across acute care (the three discharge questions’ mean – “degree … you felt ready to have your child discharged,” “speed of discharge process …,” and “instructions… to care for your child…”). We obtained seven-day readmission rates from acute care discharges using the hospital’s regularly reported data. We assessed patient characteristics, including sex, age, case mix index (CMI; >2 vs <2), insurance type (nongovernment vs government), day of discharge (weekend vs weekday), and LOS from those patients categorized as inpatients. Complete patient characteristics were not available for observation (InterQual^® criteria) status patients.

Analysis

We used descriptive statistics to describe the inpatient population characteristics by analyzing differences when EDC did and did not occur using chi-square and the Mann–Whitney U tests. Patients with missing data were removed from analyses that incorporated patient factors.

To assess our primary outcome, we used an interrupted time series analysis assessing the percentage of EDC in the total population before any intervention (May 2015) and after the last intervention (March 2016). We used the Durbin–Watson statistic to assess autocorrelation of errors in our regression models. As we had only patient characteristics for the inpatient population, we repeated the analysis including only inpatients and accounting for patient factors significantly associated with EDC.

As units and physician teams had differential exposure to the interventions, we performed a subanalysis (using interrupted time series) creating groups based on the combination of interventions to which a patient’s discharge was exposed (based on unit and physician team at discharge). Patient discharges from group 1 (medical patients on Units C, D, and E) were exposed to all three interventions, group 2 patient discharges (medical patients on Units A and B) were exposed to interventions 2 and 3, group 3 (cardiology, hematology/oncology, surgical patients on Units A and B) were exposed to intervention 3, and group 4 (surgical, cardiology, hematology/oncology patients on Units C, D, and E) were exposed to interventions 1 and 3 (Figure 1). Interrupted time series models were fit using the R Statistical Software Package.²⁰

Because of seasonal variation in admissions, we compared secondary outcomes and balancing measures over similar time frames in the calendar year (January to September 2015 vs January to September 2016) using the Mann–Whitney U test and the unpaired t-test, respectively.

The project’s primary purpose was to implement a practice to improve the quality of care, and therefore, the Stanford Institutional Review Board determined it to be nonresearch.

RESULTS

 

There were 16,175 discharges on acute care from January 2014 through December 2016. Across all acute care units, EDCs increased from an average of 8.8% before the start of interventions (May 2015) to 15.8% after all interventions (February 2016). From the estimated trend in the preintervention period, there was a jump of 3.9% to the start of the postintervention trend (P = .02; Figure 2). Furthermore, there was an increase of 0.48% (95% CI 0.15-0.82%; P < .01) per month in the trend of the slope between the pre- and postintervention. The autocorrelation function and the Durbin–Watson test did not show evidence of autocorrelation (P = .85). Lack of evidence for autocorrelation in this and each of our subsequent fitted models led to excluding an autocorrelation parameter from our models.

From 16,175 discharges, 1,764 (11%) were assigned to observation status. Among inpatients (14,411), patients with missing values (CMI, insurance status) were also excluded (n = 66, 0.5%). Among the remaining 14,345 inpatients, 54% were males, 50% were government-insured, and 1,645 (11.5%) were discharged early. The average age was 8.5 years, the average LOS was seven days, and the median CMI was 2.2. Children who were younger, had shorter LOS, CMI <2, and nongovernment insurance were more likely to be discharged early (P < .01 for all). For each of these variables, F-tests were performed to determine whether there was a statistically significant reduction in variation by adding the variable to our initial model. None of the variables alone or in combination led to a statistically significant reduction in variation. Including these factors in the interrupted time series did not change the significance of the results (jump at postintervention start 3.6%, 95% CI 0.7%-7.2%; P = .02, slope increased by 0.59% per month, 95% CI 0.29-0.89%; P < .01).

In the subgroup analysis, we did not account for patient factors as they did not change the results in the analysis of total population. Though each group had a greater percentage of EDCs in the postintervention period, the changes in slopes and jumps were primarily nonsignificant (Figure 3). Only the change in slope in group 4 was significant (1.1%, 95% CI 0.3-1.9%; P = .01).

Between January to September 2015 and 2016, ED wait times decreased by 88 minutes (P <.01) and PACU wait times decreased by 20 minutes per patient admitted (P < .01; Table). There was no statistically significant change in seven-day readmissions (P = .19) or in families feeling ready to discharge (P = .11) or in general discharge satisfaction (P = .48) as measured by Press Ganey survey. Among all discharges (inpatient and observation), the average LOS significantly decreased by 0.6 days (P = .02).

DISCUSSION

The percentage of patients who left the hospital prior to 11 am significantly improved after a number of interventions aimed at emphasizing EDC and discharge task completion earlier within the hospital stay. Our EDC improvement was associated with improved ED and PACU wait times without negatively impacting discharge satisfaction, seven-day readmissions, or LOS.

 

It is difficult to compare our EDC improvements to those of previous studies, as we are unaware of published data on pediatric EDC efforts across an entire hospital. In addition, studies have reported discharges prior to different times in the day (noon, 1 pm, etc).^{12, 13} Our interventions were similar to those of Wertheimer et al.,¹¹ including the use of interdisciplinary rounds, identification of potential EDCs the afternoon before discharge, and “reward and recognition.” Wertheimer also sent an e-mail about EDCs to a multidisciplinary group, which was then updated as conditions changed. Unlike Wertheimer, we did not include physicians in our e-mail due to the large number and frequently changing physician teams. Our EDC rate prior to 11 am was lower than their achieved rate of 35% prior to 12 pm. When we assessed our discharges using 12 pm, our rate was still lower (22%-28%), but a direct comparison was complicated by different patient populations. Still, our study adds to the evidence that interdisciplinary rounds and reward and recognition lead to earlier discharge. In addition, this study builds upon Wertheimer’s results as although they later assessed the timing of ED admissions as a result of their EDC improvements, they did not directly assess inpatient bed wait times as we did in our study.¹⁴

As providers of all types were aware of the constant push for beds due to canceled surgeries, delayed admissions and intensive care transfers, and the inability to accept admission, it is difficult to compare the subgroups directly. Furthermore, although physician teams and units are distinct, individuals (nurses, case managers, trainees) may rotate through different units and teams and we cannot account for individual influences on EDCs depending on exposure to interventions over time. Although all groups improved, the improvement in slope in group 4 (exposed to interventions 1 and 3) was the only significant change. As group 4 contained a large number of surgical patients who often have more predictable hospital stays, perhaps this group was more responsive to the interventions.

Our EDC improvements were associated with a decrease in ED and PACU bed wait times. Importantly, we did not address potential confounding factors impacting these times such as total hospital admission volumes, ED and PACU patient complexity, and distribution of ED and PACU admission requests throughout the day. Modeling has suggested that EDCs could also improve ED flow,⁷ but studies implementing EDC have not necessarily assessed this outcome.^10-15 One study retrospectively evaluated ED boarding times in the context of an EDC improvement effort and found a decrease in boarding times.²¹ This decrease is important as ED boarders may be at a higher risk for adverse events, a longer LOS, and more readmissions.^3,7 Less is known about prolonged PACU wait times; however, studies have reported delays in receiving patients from the operating room (OR), which could presumably impact timeliness of other scheduled procedures and patient satisfaction.^22-24 It is worth noting that OR holds as a result of PACU backups happened more frequently at our institution before our EDC work.

Our limitations include that individual providers in the various groups were not completely blind to the interventions and groups often comprised distinct patient populations. Second, LPCHS has a high CMI and LOS relative to most other children’s hospitals, complicating comparison with patient populations at other children’s hospitals. In addition, our work was done at this single institution. However, since a higher CMI was associated with a lower probability of EDC, hospitals with a lower CMI may have a greater opportunity for EDC improvements. Third, hospital systems are more impacted by low EDCs when operating at high occupancy (as we were at LPCHS); thus, improvements in ED and PACU wait times for inpatient beds might not be noted for hospitals operating with a >10% inventory of beds.²⁵ Importantly, our hospital had multiple daily management structures in place, which we harnessed for our interventions, and better patient flow was a key hospital initiative garnering improvement of resources. Hospitals without these resources may have more difficulty implementing similar interventions. Finally, other work to improve patient flow was concurrently implemented, including matching numbers of scheduled OR admissions with anticipated capacity, which probably also contributed to the decrease in ED and PACU wait times.

 

CONCLUSIONS

We found that a multimodal intervention was associated with more EDCs and improved ED and PACU bed wait times. We observed no impact on discharge satisfaction or readmissions. Our EDC improvement efforts may guide institutions operating at high capacity and aiming to improve EDCs to improve patient flow.

Acknowledgments

The authors would like to acknowledge all those engaged in the early discharge work at LPCHS. They would like to particularly acknowledge Ava Rezvani for her engagement and work in helping to implement the interventions.

Disclosures

The authors have no conflicts of interest relevant to this article to disclose. The authors have no financial relationships relevant to this article to disclose.

Funding

This project was accomplished without specific funding. Funding for incentives was provided by the Lucile Packard Children’s Hospital Stanford.

 

Patient flow throughout the hospital has been shown to be adversely affected by discharge delays.¹ When hospitals are operating at peak capacity, these delays impact throughput, length of stay (LOS), and cost of care and block patients from the emergency department (ED), postanesthesia recovery unit (PACU), or home awaiting inpatient beds.^2-5 As patients wait in locations not ideal for inpatient care, they may suffer from adverse events and poor satisfaction.^3,6 Several studies have analyzed discharge timing as it relates to ED boarding of admitted patients and demonstrated that early discharges (EDCs) can impact boarding times.^7-9 A number of recent improvement efforts directed at moving discharges earlier in the day have been published.^10-15 However, these improvements are often targeted at specific units or teams within a larger hospital setting and only one is in the pediatric setting.

Lucile Packard Children’s Hospital Stanford (LPCHS) is a 311-bed quaternary care academic women and children’s hospital in Northern California. As our organization expanded, the demand for hospital beds often exceeded capacity. The challenge of overall demand was regularly compounded by a mismatch in bed availability timing – bed demand is early in the day and bed availability is later. This mismatch results in delays for admitted patients waiting in the ED and PACU. Organization leaders identified increasing early discharges (EDCs) as one initiative to contribute to improved patient flow.

Our organization aimed to increase the number of discharges before 11 am across the acute care units from an average of 8% in the 17 months prior to May 2015 to 25% by December 2016. Based on the average number and timing of planned admissions, they hypothesized that 25% of EDCs would decrease ED and PACU wait times.

METHODS

Setting

We focused our EDC interventions on the 87 acute care beds at LPCHS. All patients discharged from these beds were included in the study. We excluded patients discharged from intensive care, maternity, and nursery. Acute care includes five units, one focused on hematology/oncology (Unit A), one focused on cardiology (Unit B), and the others with a surgical and medical pediatric patient mix (Units C, D, and E). Although physician teams have primary units, due to unit size, patients on teams other than cardiology and hematology/oncology are often spread across multiple units wherever there is a bed (including Units A and B). Most of the frontline care physicians are residents supervised by attendings; however, a minority of patients are cared for by nurse practitioners (NPs) or physician assistants (PAs).

 

Improvement Team

In early 2015, we formed a multidisciplinary group inclusive of a case manager, frontline nurses, nurse management, pediatric residents, and hospitalist physicians with support from performance improvement. We periodically included physician leaders from other specialties to help initiate changes within their own clinical areas. Our group used Lean A3 thinking¹⁶ to gather information about the current state, formulate the problem statement, analyze the problem, and consider interventions implemented in three Plan–Do-Check-Act (PDCA) cycles. The A3 is a structured tool to analyze problems before jumping to solutions and communicate with stakeholders. We interviewed leaders, nurses, residents, case managers, etc. and observed work processes around discharge. We met weekly to follow data, assess results of interventions, and problem solve.

Barriers and Interventions

The first barrier we identified and addressed was poor identification and shared team mental model of potential EDC patients and lack of preparation when an EDC was identified. In intervention one starting May 2015, charge nurses on Units C, D, and E were each asked to identify one EDC for the following day. The identified patient was discussed at the previously existing afternoon daily unit huddle¹⁷ attended by nurse management, case management, and hospitalist leaders. Following the huddle, the resident, NP, or PA responsible for the patient was paged regarding the EDC plan and tasked with medication reconciliation and discharge paperwork. Others were asked to address their specific area of patient care for discharge (eg, case manager–supplies, nursing–education). The patient was identified on the unit white board with a yellow magnet (use of a visual control¹⁸), so that all would be aware of the EDC. An e-mail was sent to case management, nurse leaders, and patient placement coordinators regarding the planned EDCs. Finally, the EDCs were discussed during regularly scheduled huddles throughout the evening and into the next day.¹⁷

Despite this first intervention, we noted that progress toward increased EDCs was slow. Thus, we spent approximately seven days (spread over one month) further observing the work processes.¹⁹ Over five days, we asked each unit’s charge nurse every hour which patients were waiting to be discharged and the primary reason for waiting. From this information, we created a pareto chart demonstrating that rounds were the highest contributor to waiting (Appendix A). Thus, our second intervention was a daily physician morning huddle that the four nonsurgical physician teams (excluding cardiology, hematology/oncology) implemented one team at a time between November 2015 and February 2016. At the huddle, previously identified EDCs (located on any of the five units) were confirmed and preparatory work was completed (inclusive of the discharge order) before rounds. Further, the attending and resident physicians were to see the patient before or at the start of rounds.

Our working group still observed slow EDC improvement and sought feedback from all providers. EDC was described as “extra” work, apart from routine practices and culture. In addition, our interventions had not addressed most discharges on Units A and B. Consequently, our third intervention in February 2016 aimed to recognize and incentivize teams, units, and individuals for EDC successes. Units and/or physician teams that met 25% of EDCs the previous week were acknowledged through hospital-wide screensavers and certificates of appreciation signed by the Chief Nursing Officer. Units and/or physician teams that met 25% of EDC the previous month were acknowledged with a trophy. Residents received coffee cards for each EDC (though not without controversy among the improvement group as we acknowledged that all providers contributed to EDCs). Finally, weekly, we shared an EDC dashboard displaying unit, team, and organization performance at the hospital-wide leader huddle. We also e-mailed the dashboard regularly to division chiefs, medical directors, and nursing leaders.

 

Measures

Our primary outcome was percentage of EDCs (based on the time the patient left the room) across acute care. Secondary outcome measures were median wait times for an inpatient bed from the ED (time bed requested to the time patient left the ED) and the average PACU wait time (time the patient is ready to leave the PACU to time the patient left the PACU) per admitted patient. We also assessed balancing measures, including discharge satisfaction, seven-day readmission rates, and LOS. We obtained the mean discharge satisfaction score from the organization’s Press Ganey survey results across acute care (the three discharge questions’ mean – “degree … you felt ready to have your child discharged,” “speed of discharge process …,” and “instructions… to care for your child…”). We obtained seven-day readmission rates from acute care discharges using the hospital’s regularly reported data. We assessed patient characteristics, including sex, age, case mix index (CMI; >2 vs <2), insurance type (nongovernment vs government), day of discharge (weekend vs weekday), and LOS from those patients categorized as inpatients. Complete patient characteristics were not available for observation (InterQual^® criteria) status patients.

Analysis

We used descriptive statistics to describe the inpatient population characteristics by analyzing differences when EDC did and did not occur using chi-square and the Mann–Whitney U tests. Patients with missing data were removed from analyses that incorporated patient factors.

To assess our primary outcome, we used an interrupted time series analysis assessing the percentage of EDC in the total population before any intervention (May 2015) and after the last intervention (March 2016). We used the Durbin–Watson statistic to assess autocorrelation of errors in our regression models. As we had only patient characteristics for the inpatient population, we repeated the analysis including only inpatients and accounting for patient factors significantly associated with EDC.

As units and physician teams had differential exposure to the interventions, we performed a subanalysis (using interrupted time series) creating groups based on the combination of interventions to which a patient’s discharge was exposed (based on unit and physician team at discharge). Patient discharges from group 1 (medical patients on Units C, D, and E) were exposed to all three interventions, group 2 patient discharges (medical patients on Units A and B) were exposed to interventions 2 and 3, group 3 (cardiology, hematology/oncology, surgical patients on Units A and B) were exposed to intervention 3, and group 4 (surgical, cardiology, hematology/oncology patients on Units C, D, and E) were exposed to interventions 1 and 3 (Figure 1). Interrupted time series models were fit using the R Statistical Software Package.²⁰

Because of seasonal variation in admissions, we compared secondary outcomes and balancing measures over similar time frames in the calendar year (January to September 2015 vs January to September 2016) using the Mann–Whitney U test and the unpaired t-test, respectively.

The project’s primary purpose was to implement a practice to improve the quality of care, and therefore, the Stanford Institutional Review Board determined it to be nonresearch.

RESULTS

 

There were 16,175 discharges on acute care from January 2014 through December 2016. Across all acute care units, EDCs increased from an average of 8.8% before the start of interventions (May 2015) to 15.8% after all interventions (February 2016). From the estimated trend in the preintervention period, there was a jump of 3.9% to the start of the postintervention trend (P = .02; Figure 2). Furthermore, there was an increase of 0.48% (95% CI 0.15-0.82%; P < .01) per month in the trend of the slope between the pre- and postintervention. The autocorrelation function and the Durbin–Watson test did not show evidence of autocorrelation (P = .85). Lack of evidence for autocorrelation in this and each of our subsequent fitted models led to excluding an autocorrelation parameter from our models.

From 16,175 discharges, 1,764 (11%) were assigned to observation status. Among inpatients (14,411), patients with missing values (CMI, insurance status) were also excluded (n = 66, 0.5%). Among the remaining 14,345 inpatients, 54% were males, 50% were government-insured, and 1,645 (11.5%) were discharged early. The average age was 8.5 years, the average LOS was seven days, and the median CMI was 2.2. Children who were younger, had shorter LOS, CMI <2, and nongovernment insurance were more likely to be discharged early (P < .01 for all). For each of these variables, F-tests were performed to determine whether there was a statistically significant reduction in variation by adding the variable to our initial model. None of the variables alone or in combination led to a statistically significant reduction in variation. Including these factors in the interrupted time series did not change the significance of the results (jump at postintervention start 3.6%, 95% CI 0.7%-7.2%; P = .02, slope increased by 0.59% per month, 95% CI 0.29-0.89%; P < .01).

In the subgroup analysis, we did not account for patient factors as they did not change the results in the analysis of total population. Though each group had a greater percentage of EDCs in the postintervention period, the changes in slopes and jumps were primarily nonsignificant (Figure 3). Only the change in slope in group 4 was significant (1.1%, 95% CI 0.3-1.9%; P = .01).

Between January to September 2015 and 2016, ED wait times decreased by 88 minutes (P <.01) and PACU wait times decreased by 20 minutes per patient admitted (P < .01; Table). There was no statistically significant change in seven-day readmissions (P = .19) or in families feeling ready to discharge (P = .11) or in general discharge satisfaction (P = .48) as measured by Press Ganey survey. Among all discharges (inpatient and observation), the average LOS significantly decreased by 0.6 days (P = .02).

DISCUSSION

The percentage of patients who left the hospital prior to 11 am significantly improved after a number of interventions aimed at emphasizing EDC and discharge task completion earlier within the hospital stay. Our EDC improvement was associated with improved ED and PACU wait times without negatively impacting discharge satisfaction, seven-day readmissions, or LOS.

 

It is difficult to compare our EDC improvements to those of previous studies, as we are unaware of published data on pediatric EDC efforts across an entire hospital. In addition, studies have reported discharges prior to different times in the day (noon, 1 pm, etc).^{12, 13} Our interventions were similar to those of Wertheimer et al.,¹¹ including the use of interdisciplinary rounds, identification of potential EDCs the afternoon before discharge, and “reward and recognition.” Wertheimer also sent an e-mail about EDCs to a multidisciplinary group, which was then updated as conditions changed. Unlike Wertheimer, we did not include physicians in our e-mail due to the large number and frequently changing physician teams. Our EDC rate prior to 11 am was lower than their achieved rate of 35% prior to 12 pm. When we assessed our discharges using 12 pm, our rate was still lower (22%-28%), but a direct comparison was complicated by different patient populations. Still, our study adds to the evidence that interdisciplinary rounds and reward and recognition lead to earlier discharge. In addition, this study builds upon Wertheimer’s results as although they later assessed the timing of ED admissions as a result of their EDC improvements, they did not directly assess inpatient bed wait times as we did in our study.¹⁴

As providers of all types were aware of the constant push for beds due to canceled surgeries, delayed admissions and intensive care transfers, and the inability to accept admission, it is difficult to compare the subgroups directly. Furthermore, although physician teams and units are distinct, individuals (nurses, case managers, trainees) may rotate through different units and teams and we cannot account for individual influences on EDCs depending on exposure to interventions over time. Although all groups improved, the improvement in slope in group 4 (exposed to interventions 1 and 3) was the only significant change. As group 4 contained a large number of surgical patients who often have more predictable hospital stays, perhaps this group was more responsive to the interventions.

Our EDC improvements were associated with a decrease in ED and PACU bed wait times. Importantly, we did not address potential confounding factors impacting these times such as total hospital admission volumes, ED and PACU patient complexity, and distribution of ED and PACU admission requests throughout the day. Modeling has suggested that EDCs could also improve ED flow,⁷ but studies implementing EDC have not necessarily assessed this outcome.^10-15 One study retrospectively evaluated ED boarding times in the context of an EDC improvement effort and found a decrease in boarding times.²¹ This decrease is important as ED boarders may be at a higher risk for adverse events, a longer LOS, and more readmissions.^3,7 Less is known about prolonged PACU wait times; however, studies have reported delays in receiving patients from the operating room (OR), which could presumably impact timeliness of other scheduled procedures and patient satisfaction.^22-24 It is worth noting that OR holds as a result of PACU backups happened more frequently at our institution before our EDC work.

Our limitations include that individual providers in the various groups were not completely blind to the interventions and groups often comprised distinct patient populations. Second, LPCHS has a high CMI and LOS relative to most other children’s hospitals, complicating comparison with patient populations at other children’s hospitals. In addition, our work was done at this single institution. However, since a higher CMI was associated with a lower probability of EDC, hospitals with a lower CMI may have a greater opportunity for EDC improvements. Third, hospital systems are more impacted by low EDCs when operating at high occupancy (as we were at LPCHS); thus, improvements in ED and PACU wait times for inpatient beds might not be noted for hospitals operating with a >10% inventory of beds.²⁵ Importantly, our hospital had multiple daily management structures in place, which we harnessed for our interventions, and better patient flow was a key hospital initiative garnering improvement of resources. Hospitals without these resources may have more difficulty implementing similar interventions. Finally, other work to improve patient flow was concurrently implemented, including matching numbers of scheduled OR admissions with anticipated capacity, which probably also contributed to the decrease in ED and PACU wait times.

 

CONCLUSIONS

We found that a multimodal intervention was associated with more EDCs and improved ED and PACU bed wait times. We observed no impact on discharge satisfaction or readmissions. Our EDC improvement efforts may guide institutions operating at high capacity and aiming to improve EDCs to improve patient flow.

Acknowledgments

The authors would like to acknowledge all those engaged in the early discharge work at LPCHS. They would like to particularly acknowledge Ava Rezvani for her engagement and work in helping to implement the interventions.

Disclosures

The authors have no conflicts of interest relevant to this article to disclose. The authors have no financial relationships relevant to this article to disclose.

Funding

This project was accomplished without specific funding. Funding for incentives was provided by the Lucile Packard Children’s Hospital Stanford.

 

Patient flow throughout the hospital has been shown to be adversely affected by discharge delays.¹ When hospitals are operating at peak capacity, these delays impact throughput, length of stay (LOS), and cost of care and block patients from the emergency department (ED), postanesthesia recovery unit (PACU), or home awaiting inpatient beds.^2-5 As patients wait in locations not ideal for inpatient care, they may suffer from adverse events and poor satisfaction.^3,6 Several studies have analyzed discharge timing as it relates to ED boarding of admitted patients and demonstrated that early discharges (EDCs) can impact boarding times.^7-9 A number of recent improvement efforts directed at moving discharges earlier in the day have been published.^10-15 However, these improvements are often targeted at specific units or teams within a larger hospital setting and only one is in the pediatric setting.

Lucile Packard Children’s Hospital Stanford (LPCHS) is a 311-bed quaternary care academic women and children’s hospital in Northern California. As our organization expanded, the demand for hospital beds often exceeded capacity. The challenge of overall demand was regularly compounded by a mismatch in bed availability timing – bed demand is early in the day and bed availability is later. This mismatch results in delays for admitted patients waiting in the ED and PACU. Organization leaders identified increasing early discharges (EDCs) as one initiative to contribute to improved patient flow.

Our organization aimed to increase the number of discharges before 11 am across the acute care units from an average of 8% in the 17 months prior to May 2015 to 25% by December 2016. Based on the average number and timing of planned admissions, they hypothesized that 25% of EDCs would decrease ED and PACU wait times.

METHODS

Setting

We focused our EDC interventions on the 87 acute care beds at LPCHS. All patients discharged from these beds were included in the study. We excluded patients discharged from intensive care, maternity, and nursery. Acute care includes five units, one focused on hematology/oncology (Unit A), one focused on cardiology (Unit B), and the others with a surgical and medical pediatric patient mix (Units C, D, and E). Although physician teams have primary units, due to unit size, patients on teams other than cardiology and hematology/oncology are often spread across multiple units wherever there is a bed (including Units A and B). Most of the frontline care physicians are residents supervised by attendings; however, a minority of patients are cared for by nurse practitioners (NPs) or physician assistants (PAs).

 

Improvement Team

In early 2015, we formed a multidisciplinary group inclusive of a case manager, frontline nurses, nurse management, pediatric residents, and hospitalist physicians with support from performance improvement. We periodically included physician leaders from other specialties to help initiate changes within their own clinical areas. Our group used Lean A3 thinking¹⁶ to gather information about the current state, formulate the problem statement, analyze the problem, and consider interventions implemented in three Plan–Do-Check-Act (PDCA) cycles. The A3 is a structured tool to analyze problems before jumping to solutions and communicate with stakeholders. We interviewed leaders, nurses, residents, case managers, etc. and observed work processes around discharge. We met weekly to follow data, assess results of interventions, and problem solve.

Barriers and Interventions

The first barrier we identified and addressed was poor identification and shared team mental model of potential EDC patients and lack of preparation when an EDC was identified. In intervention one starting May 2015, charge nurses on Units C, D, and E were each asked to identify one EDC for the following day. The identified patient was discussed at the previously existing afternoon daily unit huddle¹⁷ attended by nurse management, case management, and hospitalist leaders. Following the huddle, the resident, NP, or PA responsible for the patient was paged regarding the EDC plan and tasked with medication reconciliation and discharge paperwork. Others were asked to address their specific area of patient care for discharge (eg, case manager–supplies, nursing–education). The patient was identified on the unit white board with a yellow magnet (use of a visual control¹⁸), so that all would be aware of the EDC. An e-mail was sent to case management, nurse leaders, and patient placement coordinators regarding the planned EDCs. Finally, the EDCs were discussed during regularly scheduled huddles throughout the evening and into the next day.¹⁷

Despite this first intervention, we noted that progress toward increased EDCs was slow. Thus, we spent approximately seven days (spread over one month) further observing the work processes.¹⁹ Over five days, we asked each unit’s charge nurse every hour which patients were waiting to be discharged and the primary reason for waiting. From this information, we created a pareto chart demonstrating that rounds were the highest contributor to waiting (Appendix A). Thus, our second intervention was a daily physician morning huddle that the four nonsurgical physician teams (excluding cardiology, hematology/oncology) implemented one team at a time between November 2015 and February 2016. At the huddle, previously identified EDCs (located on any of the five units) were confirmed and preparatory work was completed (inclusive of the discharge order) before rounds. Further, the attending and resident physicians were to see the patient before or at the start of rounds.

Our working group still observed slow EDC improvement and sought feedback from all providers. EDC was described as “extra” work, apart from routine practices and culture. In addition, our interventions had not addressed most discharges on Units A and B. Consequently, our third intervention in February 2016 aimed to recognize and incentivize teams, units, and individuals for EDC successes. Units and/or physician teams that met 25% of EDCs the previous week were acknowledged through hospital-wide screensavers and certificates of appreciation signed by the Chief Nursing Officer. Units and/or physician teams that met 25% of EDC the previous month were acknowledged with a trophy. Residents received coffee cards for each EDC (though not without controversy among the improvement group as we acknowledged that all providers contributed to EDCs). Finally, weekly, we shared an EDC dashboard displaying unit, team, and organization performance at the hospital-wide leader huddle. We also e-mailed the dashboard regularly to division chiefs, medical directors, and nursing leaders.

 

Measures

Our primary outcome was percentage of EDCs (based on the time the patient left the room) across acute care. Secondary outcome measures were median wait times for an inpatient bed from the ED (time bed requested to the time patient left the ED) and the average PACU wait time (time the patient is ready to leave the PACU to time the patient left the PACU) per admitted patient. We also assessed balancing measures, including discharge satisfaction, seven-day readmission rates, and LOS. We obtained the mean discharge satisfaction score from the organization’s Press Ganey survey results across acute care (the three discharge questions’ mean – “degree … you felt ready to have your child discharged,” “speed of discharge process …,” and “instructions… to care for your child…”). We obtained seven-day readmission rates from acute care discharges using the hospital’s regularly reported data. We assessed patient characteristics, including sex, age, case mix index (CMI; >2 vs <2), insurance type (nongovernment vs government), day of discharge (weekend vs weekday), and LOS from those patients categorized as inpatients. Complete patient characteristics were not available for observation (InterQual^® criteria) status patients.

Analysis

We used descriptive statistics to describe the inpatient population characteristics by analyzing differences when EDC did and did not occur using chi-square and the Mann–Whitney U tests. Patients with missing data were removed from analyses that incorporated patient factors.

To assess our primary outcome, we used an interrupted time series analysis assessing the percentage of EDC in the total population before any intervention (May 2015) and after the last intervention (March 2016). We used the Durbin–Watson statistic to assess autocorrelation of errors in our regression models. As we had only patient characteristics for the inpatient population, we repeated the analysis including only inpatients and accounting for patient factors significantly associated with EDC.

As units and physician teams had differential exposure to the interventions, we performed a subanalysis (using interrupted time series) creating groups based on the combination of interventions to which a patient’s discharge was exposed (based on unit and physician team at discharge). Patient discharges from group 1 (medical patients on Units C, D, and E) were exposed to all three interventions, group 2 patient discharges (medical patients on Units A and B) were exposed to interventions 2 and 3, group 3 (cardiology, hematology/oncology, surgical patients on Units A and B) were exposed to intervention 3, and group 4 (surgical, cardiology, hematology/oncology patients on Units C, D, and E) were exposed to interventions 1 and 3 (Figure 1). Interrupted time series models were fit using the R Statistical Software Package.²⁰

Because of seasonal variation in admissions, we compared secondary outcomes and balancing measures over similar time frames in the calendar year (January to September 2015 vs January to September 2016) using the Mann–Whitney U test and the unpaired t-test, respectively.

The project’s primary purpose was to implement a practice to improve the quality of care, and therefore, the Stanford Institutional Review Board determined it to be nonresearch.

RESULTS

 

There were 16,175 discharges on acute care from January 2014 through December 2016. Across all acute care units, EDCs increased from an average of 8.8% before the start of interventions (May 2015) to 15.8% after all interventions (February 2016). From the estimated trend in the preintervention period, there was a jump of 3.9% to the start of the postintervention trend (P = .02; Figure 2). Furthermore, there was an increase of 0.48% (95% CI 0.15-0.82%; P < .01) per month in the trend of the slope between the pre- and postintervention. The autocorrelation function and the Durbin–Watson test did not show evidence of autocorrelation (P = .85). Lack of evidence for autocorrelation in this and each of our subsequent fitted models led to excluding an autocorrelation parameter from our models.

From 16,175 discharges, 1,764 (11%) were assigned to observation status. Among inpatients (14,411), patients with missing values (CMI, insurance status) were also excluded (n = 66, 0.5%). Among the remaining 14,345 inpatients, 54% were males, 50% were government-insured, and 1,645 (11.5%) were discharged early. The average age was 8.5 years, the average LOS was seven days, and the median CMI was 2.2. Children who were younger, had shorter LOS, CMI <2, and nongovernment insurance were more likely to be discharged early (P < .01 for all). For each of these variables, F-tests were performed to determine whether there was a statistically significant reduction in variation by adding the variable to our initial model. None of the variables alone or in combination led to a statistically significant reduction in variation. Including these factors in the interrupted time series did not change the significance of the results (jump at postintervention start 3.6%, 95% CI 0.7%-7.2%; P = .02, slope increased by 0.59% per month, 95% CI 0.29-0.89%; P < .01).

In the subgroup analysis, we did not account for patient factors as they did not change the results in the analysis of total population. Though each group had a greater percentage of EDCs in the postintervention period, the changes in slopes and jumps were primarily nonsignificant (Figure 3). Only the change in slope in group 4 was significant (1.1%, 95% CI 0.3-1.9%; P = .01).

Between January to September 2015 and 2016, ED wait times decreased by 88 minutes (P <.01) and PACU wait times decreased by 20 minutes per patient admitted (P < .01; Table). There was no statistically significant change in seven-day readmissions (P = .19) or in families feeling ready to discharge (P = .11) or in general discharge satisfaction (P = .48) as measured by Press Ganey survey. Among all discharges (inpatient and observation), the average LOS significantly decreased by 0.6 days (P = .02).

DISCUSSION

The percentage of patients who left the hospital prior to 11 am significantly improved after a number of interventions aimed at emphasizing EDC and discharge task completion earlier within the hospital stay. Our EDC improvement was associated with improved ED and PACU wait times without negatively impacting discharge satisfaction, seven-day readmissions, or LOS.

 

It is difficult to compare our EDC improvements to those of previous studies, as we are unaware of published data on pediatric EDC efforts across an entire hospital. In addition, studies have reported discharges prior to different times in the day (noon, 1 pm, etc).^{12, 13} Our interventions were similar to those of Wertheimer et al.,¹¹ including the use of interdisciplinary rounds, identification of potential EDCs the afternoon before discharge, and “reward and recognition.” Wertheimer also sent an e-mail about EDCs to a multidisciplinary group, which was then updated as conditions changed. Unlike Wertheimer, we did not include physicians in our e-mail due to the large number and frequently changing physician teams. Our EDC rate prior to 11 am was lower than their achieved rate of 35% prior to 12 pm. When we assessed our discharges using 12 pm, our rate was still lower (22%-28%), but a direct comparison was complicated by different patient populations. Still, our study adds to the evidence that interdisciplinary rounds and reward and recognition lead to earlier discharge. In addition, this study builds upon Wertheimer’s results as although they later assessed the timing of ED admissions as a result of their EDC improvements, they did not directly assess inpatient bed wait times as we did in our study.¹⁴

As providers of all types were aware of the constant push for beds due to canceled surgeries, delayed admissions and intensive care transfers, and the inability to accept admission, it is difficult to compare the subgroups directly. Furthermore, although physician teams and units are distinct, individuals (nurses, case managers, trainees) may rotate through different units and teams and we cannot account for individual influences on EDCs depending on exposure to interventions over time. Although all groups improved, the improvement in slope in group 4 (exposed to interventions 1 and 3) was the only significant change. As group 4 contained a large number of surgical patients who often have more predictable hospital stays, perhaps this group was more responsive to the interventions.

Our EDC improvements were associated with a decrease in ED and PACU bed wait times. Importantly, we did not address potential confounding factors impacting these times such as total hospital admission volumes, ED and PACU patient complexity, and distribution of ED and PACU admission requests throughout the day. Modeling has suggested that EDCs could also improve ED flow,⁷ but studies implementing EDC have not necessarily assessed this outcome.^10-15 One study retrospectively evaluated ED boarding times in the context of an EDC improvement effort and found a decrease in boarding times.²¹ This decrease is important as ED boarders may be at a higher risk for adverse events, a longer LOS, and more readmissions.^3,7 Less is known about prolonged PACU wait times; however, studies have reported delays in receiving patients from the operating room (OR), which could presumably impact timeliness of other scheduled procedures and patient satisfaction.^22-24 It is worth noting that OR holds as a result of PACU backups happened more frequently at our institution before our EDC work.

Our limitations include that individual providers in the various groups were not completely blind to the interventions and groups often comprised distinct patient populations. Second, LPCHS has a high CMI and LOS relative to most other children’s hospitals, complicating comparison with patient populations at other children’s hospitals. In addition, our work was done at this single institution. However, since a higher CMI was associated with a lower probability of EDC, hospitals with a lower CMI may have a greater opportunity for EDC improvements. Third, hospital systems are more impacted by low EDCs when operating at high occupancy (as we were at LPCHS); thus, improvements in ED and PACU wait times for inpatient beds might not be noted for hospitals operating with a >10% inventory of beds.²⁵ Importantly, our hospital had multiple daily management structures in place, which we harnessed for our interventions, and better patient flow was a key hospital initiative garnering improvement of resources. Hospitals without these resources may have more difficulty implementing similar interventions. Finally, other work to improve patient flow was concurrently implemented, including matching numbers of scheduled OR admissions with anticipated capacity, which probably also contributed to the decrease in ED and PACU wait times.

 

CONCLUSIONS

We found that a multimodal intervention was associated with more EDCs and improved ED and PACU bed wait times. We observed no impact on discharge satisfaction or readmissions. Our EDC improvement efforts may guide institutions operating at high capacity and aiming to improve EDCs to improve patient flow.

Acknowledgments

The authors would like to acknowledge all those engaged in the early discharge work at LPCHS. They would like to particularly acknowledge Ava Rezvani for her engagement and work in helping to implement the interventions.

Disclosures

The authors have no conflicts of interest relevant to this article to disclose. The authors have no financial relationships relevant to this article to disclose.

Funding

This project was accomplished without specific funding. Funding for incentives was provided by the Lucile Packard Children’s Hospital Stanford.

 

Many hospitals and emergency departments (EDs) face challenges posed by overcrowding and hospital throughput. Slow ED throughput has been associated with worse patient outcomes.¹ One strategy increasingly employed to improve hospital throughput is to increase the rate of inpatient discharges earlier in the day, which is often defined as discharges before noon (DCBNs). The hypothesis behind DCBN is that earlier hospital discharges will allow for earlier ED admissions and thus mitigate ED overcrowding while optimizing inpatient hospital flow. Previous quality improvement efforts to increase the percentage of DCBNs have been successfully implemented. For example, Wertheimer et al. implemented a process for earlier discharges and reported a 27-percentage point (11% to 38%) increase in DCBN on general medicine units.² In a recent survey among leaders in hospital medicine programs, a majority reported early discharge as an important institutional goal.³

Studies of the effectiveness of DCBN initiatives on improving throughput and shortening length of stay (LOS) in adult patients have had mixed results. Computer modeling has supported the idea that earlier inpatient discharges would shorten ED patient boarding time.⁴Wertheimer et al. performed a retrospective analysis of a DCBN intervention on two inpatient medicine units and reported an association between slightly shorter observed versus expected inpatient LOS² and earlier arrival time of inpatient admissions from the ED.⁵ In contrast, Rajkomar et al. conducted a retrospective analysis of the association of DCBN and LOS in a predominantly surgical services population and reported a longer LOS for DCBN patients when controlling for patient characteristics and comorbidities.⁶ These mixed findings have led some authors to question the value of DCBN initiatives and created concern for the potential of prolonged patient hospitalizations as a result of institutional DCBN goals.⁷ The impact of DCBN in pediatric patients is much less studied.

A question of interest for hospitals is if DCBN is a good indicator of shorter LOS, or is DCBN an arbitrary indicator, as morning discharges might just be the result of a delayed discharge of a patient ready for discharge the prior afternoon/evening. Our study objectives were: (1) to determine whether DCBN is associated with a shorter LOS in a pediatric population at an academic medical center, and (2) to examine separately this association in medical and surgical patients given the different provider workflow and patient clinical characteristics in those groups.

PATIENTS AND METHODS

Patients and Settings

This retrospective cohort analysis included pediatric medical and surgical inpatient admissions from a single academic medical center from May 2014 to April 2017. The University of North Carolina (UNC) Children’s Hospital is a 175-bed tertiary care ‘hospital within a hospital’ in an academic setting with multiple residencies. UNC Children’s Hospital contains three units providing inpatient pediatric care. Each unit occupies a floor of the Children’s hospital and are loosely regionalized, as follows: (1) Unit 7 is focused on surgical patients; (2) Unit 6 is focused on general, neurologic, and renal patients; and (3) Unit 5 is focused on hematology/oncology and pulmonary patients. Extending the entire study period, Unit 6 initiated a quality improvement effort to discharge patients earlier in the day, specifically before 1 pm; however, the initiative did not extend beyond this one unit.

 

We included patients 21 years or younger with an inpatient admission to any of the following pediatric medical or surgical services: cardiac surgery, cardiology, endocrinology, gastroenterology, general services, hematology/oncology, nephrology, orthopedics, otolaryngology, plastic surgery, pulmonology, and urology. Patients whose stay did not extend beyond one midnight were excluded because discharge time of day for these short stays was strongly related to the time of admission. We also excluded patients whose stay extended beyond two standard deviations of the average LOS for the discharge service under the assumption that these patients represented atypical circumstances. Finally, we excluded patients who died or left against medical advice. A consortium diagram of all exclusion criteria can be found in Supplemental Figure 1. Discharge data were extracted from the Carolina Database Warehouse, a data repository of the University of North Carolina Health System. The University of North Carolina Institutional Review Board reviewed and approved this study (IRB 17-0500).

Measures

The outcome of interest was LOS, defined as discharge date and time minus admission date and time, and thus a continuous measure of time in the hospital rather than a number of midnights. Rajkomar et al. used the same definition of LOS.⁶ The independent variable of interest was whether the discharge occurred before noon. Because discharges between midnight and 8:00 am are likely unplanned and not attributable to any particular workflow, we followed a similar definition of DCBN used by Rajkomer et al. and defined DCBN as a patient leaving between 8:00 am and 11:59 am (pre-8:00 am discharges accounted for less than one half of one percent of discharges).⁶

All model covariates were collected at the patient level (Table 1), including demographic characteristics such as age, sex, race, and ethnicity. We also collected covariates describing the patient’s hospitalization as follows: (1) whether the patient was discharged on a weekend versus weekday; (2) hospital service at time of discharge (dichotomized to a surgical or medical service); (3) whether the patient was discharged from the unit that had a DCBN quality improvement initiative; (4) discharge disposition (home with self-care, assisted living or home health, or other); (5) insurance type during hospitalization (commercial, Medicaid, no insurance, or other); and (6) case mix index (CMI), a measure of hospital resource intensity of a patient’s principal diagnosis. Covariate selection was made on the basis of a priori knowledge of causal pathways.⁸

Statistical Analysis

Student t tests and χ² statistics were used to compare baseline characteristics of hospitalizations of patients DCBN and after noon. We used ordinary least squares (OLS) regression models to assess the association between DCBN and LOS. Because DCBN may be correlated with patient characteristics, we used propensity score weighted models. Propensity scores were estimated using a logistic regression predicting DCBN using the variables given in Table 1 (excluding the outcome variable LOS). To estimate the average treatment effect on the entire sample for each model, we weighted each observation by the inverse-probability of treatment as per recent propensity score methods detailed by Garrido et al.⁹ In the inverse-probability weighted models, we clustered on attending physician to adjust for the autocorrelation caused by unobservable similarities of discharges by the same attending. We tested for multicollinearity using the variance inflation factor (VIF). To test our secondary hypothesis that there was a difference in the relationship between DCBN and LOS based on service type (medical versus surgical), we tested if the service type moderated any of the coefficients using a joint Wald test on the 10 coefficients interacted with the service type.

 

For our sensitivity analysis, we reran all surgical and medical discharges models changing the LOS outlier exclusion criteria to greater than three and then four standard deviations. Statistical modeling and analysis were completed using Stata version 14 (StataCorp, College Station, Texas).

RESULTS

Our study sample comprised 8,226 pediatric hospitalizations with a LOS mean of 5.10 and a median of 3.91 days respectively (range, 1.25-32.83 days). There were 1,531 (18.6%) DCBNs. Compared to those discharged after noon, patients with DCBN had a higher probability of being surgical patients, having commercial insurance, discharge home with self-care, discharge on the weekend, and discharge from a nonquality improvement unit (Table 1). Patients with DCBN were also more likely to be white, non-Hispanic, and male.

Our propensity score weighted ordinary least score (OLS) LOS regression results are presented in Table 2. In the bivariate analysis, DCBN was associated with an average 0.40 day, or roughly 10 hours, shorter LOS (P < .001). In the multivariate model of all discharges, we found that DCBN was associated with a mean of 0.27 day (P = .010) shorter LOS when compared to discharge in the afternoon when controlling for age, race, ethnicity, weekend discharge, discharge from quality improvement unit, discharge service type, CMI, insurance type, and discharge disposition. In the multivariate analysis, weekend discharge, surgical discharge, and discharge disposition of home with self-care, compared to assisted living or home health were associated with shorter LOS.

There was no evidence of multicollinearity (mean VIF of 1.14). The Wald test returned an F statistic of 27.50 (P < .001) indicating there was a structural difference in the relationship between LOS and DCBN dependent on discharge service type; thus, we ran separate surgical and medical discharge models to interpret model coefficients for both service types. When we analyzed surgical and medical discharges in separate models, the effect of DCBN on LOS in the medical discharges model was significantly associated with a 0.30 day (P = .017) shorter LOS (Table 2). The association was not significant in the surgical discharges model.

To further test the analysis, we increased the LOS outlier exclusion criteria to three and four standard deviations. Being more inclusive with LOS outliers in the sample resulted in a larger DCBN effect size that was significant in all three multivariate models (Supplemental Table 1).

DISCUSSION

In our study of over 8,000 pediatric discharges during a three-year period, DCBN was associated with shorter LOS for medical pediatric patients, but this finding was not consistent for surgical patients. Among medical discharges, DCBN was associated with shorter LOS, an effect robust enough to include or exclude outliers (for LOS, outliers are an important subset because there are always, in general, a few patients with very long lengths of stay). Discharge before noon showed no association with LOS for surgical patients unless we included outlier values.

The differential effect of DCBN on LOS in surgical and medical discharges suggests that the relationship between DCBN and LOS may be related to provider team workflow. For example, surgical teams may tend to round one time per day early in the morning before spending the entire day in the operating room, and thus completing more early morning discharge orders compared to medical teams. However, if a patient on a surgical service is not ready for discharge first thing in the morning, the patient may be more likely to wait until the following morning for a discharge order. On medical services, physician schedules may allow for more flexibility for rounding and responding with a discharge order when a patient becomes ready; however, medical services may round later in the day compared to surgeons and for a longer period of time, delaying discharges beyond noon that could have been made earlier. Another possibility, given UNC pediatric services are loosely regionalized with surgical patients concentrated more in one unit, is that unit-level differences in how staff processed discharges could have contributed to the difference observed between medical and surgical patients, particularly as there was a unit-level quality improvement effort for decreasing discharge time on one of two medical floors. However, we analyzed for differences based on the discharging unit and found no association. The influence of outliers on the association between DCBN and LOS increases also suggests that this group of children who have extremely long hospital stays might need further exploration.

Our study has some similar and some contrasting results with prior studies in adult patients. Our findings support the modeling literature that suggests DCBN may improve discharge efficiency by shortening patient LOS for some discharges.⁴ These findings contrast with Rajkomar et al., who reported that DCBN was associated with a longer LOS in adult patients.⁶ The contrasting findings could be due to differences in pediatric versus adult patients. Additionally, the population Rajkomar et al. studied was predominantly surgical patients, whose discharges may differ from medical patients’ in many aspects. Another possible explanation is that the Rajkomar et al. study was performed in a setting with clearly set institutional targets for DCBN, whereas, our institution lacked any hospital-wide DCBN initiatives or standards to which providers were held accountable. Some authors have argued setting DCBN as a measure of hospital quality perhaps creates the unintended consequence of providers holding potential afternoon or evening discharges until the next day so that they can be DCBN.^7,10 In that scenario, perhaps there would be a relationship between DCBN and longer LOS compared to patients who are reevaluated in the afternoon or evening and discharged. We did not find evidence of these effects in our analysis, however, understanding the potential for this is important when designing quality improvement efforts aimed at increasing discharge efficiency.

While shorter LOS can be an indicator of high-value care, the relationship between LOS, DCBN, and efficiency of discharge processes remains unclear. Prior studies have found evidence that multidisciplinary care teams with frequent care coordination rounds and integration of electronic admission order sets can be effective in improving discharge efficiency as measured by discharge within two hours of meeting discharge goals.^11,12 Measuring discharge efficiency on an ongoing basis is very difficult; however, easy-to-measure targets such as discharge before noon may be used as a proxy measure of efficiency. These targets also have “face validity,” and because of these two factors, measures like DCBN have been widely implemented even though evidence to support their validity is minimal.

Our study has several limitations. While we controlled for observable characteristics using covariates and propensity score weighted analyses, there are likely unobservable characteristics that confound our analysis. We did not measure other factors that may affect discharge time of day such as high occupancy, staffing levels, patient transportation availability, and patient and family preferences. Given these limitations, we caution against interpreting a causal relationship between independent variables and the outcome. Finally, this analysis was conducted at a single tertiary care, academic medical center. The majority of pediatric admissions at this institution are either transferred from other hospitals or scheduled admissions for medical or surgical care. A smaller proportion of discharges are acute, unplanned admissions through our emergency department in children with or without underlying medical complexity. These factors plus the exclusion of observation, extended recovery, and all the less than two-day stays in this study contribute to a relatively higher average LOS. These factors potentially limit generalizability to other care settings. Additionally, the majority of the care teams involve care by resident physicians, and they are often the primary caregivers and write the majority of orders in patient charts such as discharge orders. While we were not able to control for within resident physician similarities between patients, we did control for autocorrelation at the attending level.

 

CONCLUSION

The results of our study suggest that DCBN is associated with a decreased LOS for medical but not surgical pediatric patients. DCBN may not be an appropriate measure for all services. Further research should be done to identify other feasible but more valid indicators for shorter LOS.

Disclosures

The authors have no financial relationships relative to this article to disclose. The authors have no conflicts of interest relevant to this article to disclose.

Funding

There were no external sources of funding for this work.

 

Many hospitals and emergency departments (EDs) face challenges posed by overcrowding and hospital throughput. Slow ED throughput has been associated with worse patient outcomes.¹ One strategy increasingly employed to improve hospital throughput is to increase the rate of inpatient discharges earlier in the day, which is often defined as discharges before noon (DCBNs). The hypothesis behind DCBN is that earlier hospital discharges will allow for earlier ED admissions and thus mitigate ED overcrowding while optimizing inpatient hospital flow. Previous quality improvement efforts to increase the percentage of DCBNs have been successfully implemented. For example, Wertheimer et al. implemented a process for earlier discharges and reported a 27-percentage point (11% to 38%) increase in DCBN on general medicine units.² In a recent survey among leaders in hospital medicine programs, a majority reported early discharge as an important institutional goal.³

Studies of the effectiveness of DCBN initiatives on improving throughput and shortening length of stay (LOS) in adult patients have had mixed results. Computer modeling has supported the idea that earlier inpatient discharges would shorten ED patient boarding time.⁴Wertheimer et al. performed a retrospective analysis of a DCBN intervention on two inpatient medicine units and reported an association between slightly shorter observed versus expected inpatient LOS² and earlier arrival time of inpatient admissions from the ED.⁵ In contrast, Rajkomar et al. conducted a retrospective analysis of the association of DCBN and LOS in a predominantly surgical services population and reported a longer LOS for DCBN patients when controlling for patient characteristics and comorbidities.⁶ These mixed findings have led some authors to question the value of DCBN initiatives and created concern for the potential of prolonged patient hospitalizations as a result of institutional DCBN goals.⁷ The impact of DCBN in pediatric patients is much less studied.

A question of interest for hospitals is if DCBN is a good indicator of shorter LOS, or is DCBN an arbitrary indicator, as morning discharges might just be the result of a delayed discharge of a patient ready for discharge the prior afternoon/evening. Our study objectives were: (1) to determine whether DCBN is associated with a shorter LOS in a pediatric population at an academic medical center, and (2) to examine separately this association in medical and surgical patients given the different provider workflow and patient clinical characteristics in those groups.

PATIENTS AND METHODS

Patients and Settings

This retrospective cohort analysis included pediatric medical and surgical inpatient admissions from a single academic medical center from May 2014 to April 2017. The University of North Carolina (UNC) Children’s Hospital is a 175-bed tertiary care ‘hospital within a hospital’ in an academic setting with multiple residencies. UNC Children’s Hospital contains three units providing inpatient pediatric care. Each unit occupies a floor of the Children’s hospital and are loosely regionalized, as follows: (1) Unit 7 is focused on surgical patients; (2) Unit 6 is focused on general, neurologic, and renal patients; and (3) Unit 5 is focused on hematology/oncology and pulmonary patients. Extending the entire study period, Unit 6 initiated a quality improvement effort to discharge patients earlier in the day, specifically before 1 pm; however, the initiative did not extend beyond this one unit.

 

We included patients 21 years or younger with an inpatient admission to any of the following pediatric medical or surgical services: cardiac surgery, cardiology, endocrinology, gastroenterology, general services, hematology/oncology, nephrology, orthopedics, otolaryngology, plastic surgery, pulmonology, and urology. Patients whose stay did not extend beyond one midnight were excluded because discharge time of day for these short stays was strongly related to the time of admission. We also excluded patients whose stay extended beyond two standard deviations of the average LOS for the discharge service under the assumption that these patients represented atypical circumstances. Finally, we excluded patients who died or left against medical advice. A consortium diagram of all exclusion criteria can be found in Supplemental Figure 1. Discharge data were extracted from the Carolina Database Warehouse, a data repository of the University of North Carolina Health System. The University of North Carolina Institutional Review Board reviewed and approved this study (IRB 17-0500).

Measures

The outcome of interest was LOS, defined as discharge date and time minus admission date and time, and thus a continuous measure of time in the hospital rather than a number of midnights. Rajkomar et al. used the same definition of LOS.⁶ The independent variable of interest was whether the discharge occurred before noon. Because discharges between midnight and 8:00 am are likely unplanned and not attributable to any particular workflow, we followed a similar definition of DCBN used by Rajkomer et al. and defined DCBN as a patient leaving between 8:00 am and 11:59 am (pre-8:00 am discharges accounted for less than one half of one percent of discharges).⁶

All model covariates were collected at the patient level (Table 1), including demographic characteristics such as age, sex, race, and ethnicity. We also collected covariates describing the patient’s hospitalization as follows: (1) whether the patient was discharged on a weekend versus weekday; (2) hospital service at time of discharge (dichotomized to a surgical or medical service); (3) whether the patient was discharged from the unit that had a DCBN quality improvement initiative; (4) discharge disposition (home with self-care, assisted living or home health, or other); (5) insurance type during hospitalization (commercial, Medicaid, no insurance, or other); and (6) case mix index (CMI), a measure of hospital resource intensity of a patient’s principal diagnosis. Covariate selection was made on the basis of a priori knowledge of causal pathways.⁸

Statistical Analysis

Student t tests and χ² statistics were used to compare baseline characteristics of hospitalizations of patients DCBN and after noon. We used ordinary least squares (OLS) regression models to assess the association between DCBN and LOS. Because DCBN may be correlated with patient characteristics, we used propensity score weighted models. Propensity scores were estimated using a logistic regression predicting DCBN using the variables given in Table 1 (excluding the outcome variable LOS). To estimate the average treatment effect on the entire sample for each model, we weighted each observation by the inverse-probability of treatment as per recent propensity score methods detailed by Garrido et al.⁹ In the inverse-probability weighted models, we clustered on attending physician to adjust for the autocorrelation caused by unobservable similarities of discharges by the same attending. We tested for multicollinearity using the variance inflation factor (VIF). To test our secondary hypothesis that there was a difference in the relationship between DCBN and LOS based on service type (medical versus surgical), we tested if the service type moderated any of the coefficients using a joint Wald test on the 10 coefficients interacted with the service type.

 

For our sensitivity analysis, we reran all surgical and medical discharges models changing the LOS outlier exclusion criteria to greater than three and then four standard deviations. Statistical modeling and analysis were completed using Stata version 14 (StataCorp, College Station, Texas).

RESULTS

Our study sample comprised 8,226 pediatric hospitalizations with a LOS mean of 5.10 and a median of 3.91 days respectively (range, 1.25-32.83 days). There were 1,531 (18.6%) DCBNs. Compared to those discharged after noon, patients with DCBN had a higher probability of being surgical patients, having commercial insurance, discharge home with self-care, discharge on the weekend, and discharge from a nonquality improvement unit (Table 1). Patients with DCBN were also more likely to be white, non-Hispanic, and male.

Our propensity score weighted ordinary least score (OLS) LOS regression results are presented in Table 2. In the bivariate analysis, DCBN was associated with an average 0.40 day, or roughly 10 hours, shorter LOS (P < .001). In the multivariate model of all discharges, we found that DCBN was associated with a mean of 0.27 day (P = .010) shorter LOS when compared to discharge in the afternoon when controlling for age, race, ethnicity, weekend discharge, discharge from quality improvement unit, discharge service type, CMI, insurance type, and discharge disposition. In the multivariate analysis, weekend discharge, surgical discharge, and discharge disposition of home with self-care, compared to assisted living or home health were associated with shorter LOS.

There was no evidence of multicollinearity (mean VIF of 1.14). The Wald test returned an F statistic of 27.50 (P < .001) indicating there was a structural difference in the relationship between LOS and DCBN dependent on discharge service type; thus, we ran separate surgical and medical discharge models to interpret model coefficients for both service types. When we analyzed surgical and medical discharges in separate models, the effect of DCBN on LOS in the medical discharges model was significantly associated with a 0.30 day (P = .017) shorter LOS (Table 2). The association was not significant in the surgical discharges model.

To further test the analysis, we increased the LOS outlier exclusion criteria to three and four standard deviations. Being more inclusive with LOS outliers in the sample resulted in a larger DCBN effect size that was significant in all three multivariate models (Supplemental Table 1).

DISCUSSION

In our study of over 8,000 pediatric discharges during a three-year period, DCBN was associated with shorter LOS for medical pediatric patients, but this finding was not consistent for surgical patients. Among medical discharges, DCBN was associated with shorter LOS, an effect robust enough to include or exclude outliers (for LOS, outliers are an important subset because there are always, in general, a few patients with very long lengths of stay). Discharge before noon showed no association with LOS for surgical patients unless we included outlier values.

The differential effect of DCBN on LOS in surgical and medical discharges suggests that the relationship between DCBN and LOS may be related to provider team workflow. For example, surgical teams may tend to round one time per day early in the morning before spending the entire day in the operating room, and thus completing more early morning discharge orders compared to medical teams. However, if a patient on a surgical service is not ready for discharge first thing in the morning, the patient may be more likely to wait until the following morning for a discharge order. On medical services, physician schedules may allow for more flexibility for rounding and responding with a discharge order when a patient becomes ready; however, medical services may round later in the day compared to surgeons and for a longer period of time, delaying discharges beyond noon that could have been made earlier. Another possibility, given UNC pediatric services are loosely regionalized with surgical patients concentrated more in one unit, is that unit-level differences in how staff processed discharges could have contributed to the difference observed between medical and surgical patients, particularly as there was a unit-level quality improvement effort for decreasing discharge time on one of two medical floors. However, we analyzed for differences based on the discharging unit and found no association. The influence of outliers on the association between DCBN and LOS increases also suggests that this group of children who have extremely long hospital stays might need further exploration.

Our study has some similar and some contrasting results with prior studies in adult patients. Our findings support the modeling literature that suggests DCBN may improve discharge efficiency by shortening patient LOS for some discharges.⁴ These findings contrast with Rajkomar et al., who reported that DCBN was associated with a longer LOS in adult patients.⁶ The contrasting findings could be due to differences in pediatric versus adult patients. Additionally, the population Rajkomar et al. studied was predominantly surgical patients, whose discharges may differ from medical patients’ in many aspects. Another possible explanation is that the Rajkomar et al. study was performed in a setting with clearly set institutional targets for DCBN, whereas, our institution lacked any hospital-wide DCBN initiatives or standards to which providers were held accountable. Some authors have argued setting DCBN as a measure of hospital quality perhaps creates the unintended consequence of providers holding potential afternoon or evening discharges until the next day so that they can be DCBN.^7,10 In that scenario, perhaps there would be a relationship between DCBN and longer LOS compared to patients who are reevaluated in the afternoon or evening and discharged. We did not find evidence of these effects in our analysis, however, understanding the potential for this is important when designing quality improvement efforts aimed at increasing discharge efficiency.

While shorter LOS can be an indicator of high-value care, the relationship between LOS, DCBN, and efficiency of discharge processes remains unclear. Prior studies have found evidence that multidisciplinary care teams with frequent care coordination rounds and integration of electronic admission order sets can be effective in improving discharge efficiency as measured by discharge within two hours of meeting discharge goals.^11,12 Measuring discharge efficiency on an ongoing basis is very difficult; however, easy-to-measure targets such as discharge before noon may be used as a proxy measure of efficiency. These targets also have “face validity,” and because of these two factors, measures like DCBN have been widely implemented even though evidence to support their validity is minimal.

Our study has several limitations. While we controlled for observable characteristics using covariates and propensity score weighted analyses, there are likely unobservable characteristics that confound our analysis. We did not measure other factors that may affect discharge time of day such as high occupancy, staffing levels, patient transportation availability, and patient and family preferences. Given these limitations, we caution against interpreting a causal relationship between independent variables and the outcome. Finally, this analysis was conducted at a single tertiary care, academic medical center. The majority of pediatric admissions at this institution are either transferred from other hospitals or scheduled admissions for medical or surgical care. A smaller proportion of discharges are acute, unplanned admissions through our emergency department in children with or without underlying medical complexity. These factors plus the exclusion of observation, extended recovery, and all the less than two-day stays in this study contribute to a relatively higher average LOS. These factors potentially limit generalizability to other care settings. Additionally, the majority of the care teams involve care by resident physicians, and they are often the primary caregivers and write the majority of orders in patient charts such as discharge orders. While we were not able to control for within resident physician similarities between patients, we did control for autocorrelation at the attending level.

 

CONCLUSION

The results of our study suggest that DCBN is associated with a decreased LOS for medical but not surgical pediatric patients. DCBN may not be an appropriate measure for all services. Further research should be done to identify other feasible but more valid indicators for shorter LOS.

Disclosures

The authors have no financial relationships relative to this article to disclose. The authors have no conflicts of interest relevant to this article to disclose.

Funding

There were no external sources of funding for this work.

 

Many hospitals and emergency departments (EDs) face challenges posed by overcrowding and hospital throughput. Slow ED throughput has been associated with worse patient outcomes.¹ One strategy increasingly employed to improve hospital throughput is to increase the rate of inpatient discharges earlier in the day, which is often defined as discharges before noon (DCBNs). The hypothesis behind DCBN is that earlier hospital discharges will allow for earlier ED admissions and thus mitigate ED overcrowding while optimizing inpatient hospital flow. Previous quality improvement efforts to increase the percentage of DCBNs have been successfully implemented. For example, Wertheimer et al. implemented a process for earlier discharges and reported a 27-percentage point (11% to 38%) increase in DCBN on general medicine units.² In a recent survey among leaders in hospital medicine programs, a majority reported early discharge as an important institutional goal.³

Studies of the effectiveness of DCBN initiatives on improving throughput and shortening length of stay (LOS) in adult patients have had mixed results. Computer modeling has supported the idea that earlier inpatient discharges would shorten ED patient boarding time.⁴Wertheimer et al. performed a retrospective analysis of a DCBN intervention on two inpatient medicine units and reported an association between slightly shorter observed versus expected inpatient LOS² and earlier arrival time of inpatient admissions from the ED.⁵ In contrast, Rajkomar et al. conducted a retrospective analysis of the association of DCBN and LOS in a predominantly surgical services population and reported a longer LOS for DCBN patients when controlling for patient characteristics and comorbidities.⁶ These mixed findings have led some authors to question the value of DCBN initiatives and created concern for the potential of prolonged patient hospitalizations as a result of institutional DCBN goals.⁷ The impact of DCBN in pediatric patients is much less studied.

A question of interest for hospitals is if DCBN is a good indicator of shorter LOS, or is DCBN an arbitrary indicator, as morning discharges might just be the result of a delayed discharge of a patient ready for discharge the prior afternoon/evening. Our study objectives were: (1) to determine whether DCBN is associated with a shorter LOS in a pediatric population at an academic medical center, and (2) to examine separately this association in medical and surgical patients given the different provider workflow and patient clinical characteristics in those groups.

PATIENTS AND METHODS

Patients and Settings

This retrospective cohort analysis included pediatric medical and surgical inpatient admissions from a single academic medical center from May 2014 to April 2017. The University of North Carolina (UNC) Children’s Hospital is a 175-bed tertiary care ‘hospital within a hospital’ in an academic setting with multiple residencies. UNC Children’s Hospital contains three units providing inpatient pediatric care. Each unit occupies a floor of the Children’s hospital and are loosely regionalized, as follows: (1) Unit 7 is focused on surgical patients; (2) Unit 6 is focused on general, neurologic, and renal patients; and (3) Unit 5 is focused on hematology/oncology and pulmonary patients. Extending the entire study period, Unit 6 initiated a quality improvement effort to discharge patients earlier in the day, specifically before 1 pm; however, the initiative did not extend beyond this one unit.

 

We included patients 21 years or younger with an inpatient admission to any of the following pediatric medical or surgical services: cardiac surgery, cardiology, endocrinology, gastroenterology, general services, hematology/oncology, nephrology, orthopedics, otolaryngology, plastic surgery, pulmonology, and urology. Patients whose stay did not extend beyond one midnight were excluded because discharge time of day for these short stays was strongly related to the time of admission. We also excluded patients whose stay extended beyond two standard deviations of the average LOS for the discharge service under the assumption that these patients represented atypical circumstances. Finally, we excluded patients who died or left against medical advice. A consortium diagram of all exclusion criteria can be found in Supplemental Figure 1. Discharge data were extracted from the Carolina Database Warehouse, a data repository of the University of North Carolina Health System. The University of North Carolina Institutional Review Board reviewed and approved this study (IRB 17-0500).

Measures

The outcome of interest was LOS, defined as discharge date and time minus admission date and time, and thus a continuous measure of time in the hospital rather than a number of midnights. Rajkomar et al. used the same definition of LOS.⁶ The independent variable of interest was whether the discharge occurred before noon. Because discharges between midnight and 8:00 am are likely unplanned and not attributable to any particular workflow, we followed a similar definition of DCBN used by Rajkomer et al. and defined DCBN as a patient leaving between 8:00 am and 11:59 am (pre-8:00 am discharges accounted for less than one half of one percent of discharges).⁶

All model covariates were collected at the patient level (Table 1), including demographic characteristics such as age, sex, race, and ethnicity. We also collected covariates describing the patient’s hospitalization as follows: (1) whether the patient was discharged on a weekend versus weekday; (2) hospital service at time of discharge (dichotomized to a surgical or medical service); (3) whether the patient was discharged from the unit that had a DCBN quality improvement initiative; (4) discharge disposition (home with self-care, assisted living or home health, or other); (5) insurance type during hospitalization (commercial, Medicaid, no insurance, or other); and (6) case mix index (CMI), a measure of hospital resource intensity of a patient’s principal diagnosis. Covariate selection was made on the basis of a priori knowledge of causal pathways.⁸

Statistical Analysis

Student t tests and χ² statistics were used to compare baseline characteristics of hospitalizations of patients DCBN and after noon. We used ordinary least squares (OLS) regression models to assess the association between DCBN and LOS. Because DCBN may be correlated with patient characteristics, we used propensity score weighted models. Propensity scores were estimated using a logistic regression predicting DCBN using the variables given in Table 1 (excluding the outcome variable LOS). To estimate the average treatment effect on the entire sample for each model, we weighted each observation by the inverse-probability of treatment as per recent propensity score methods detailed by Garrido et al.⁹ In the inverse-probability weighted models, we clustered on attending physician to adjust for the autocorrelation caused by unobservable similarities of discharges by the same attending. We tested for multicollinearity using the variance inflation factor (VIF). To test our secondary hypothesis that there was a difference in the relationship between DCBN and LOS based on service type (medical versus surgical), we tested if the service type moderated any of the coefficients using a joint Wald test on the 10 coefficients interacted with the service type.

 

For our sensitivity analysis, we reran all surgical and medical discharges models changing the LOS outlier exclusion criteria to greater than three and then four standard deviations. Statistical modeling and analysis were completed using Stata version 14 (StataCorp, College Station, Texas).

RESULTS

Our study sample comprised 8,226 pediatric hospitalizations with a LOS mean of 5.10 and a median of 3.91 days respectively (range, 1.25-32.83 days). There were 1,531 (18.6%) DCBNs. Compared to those discharged after noon, patients with DCBN had a higher probability of being surgical patients, having commercial insurance, discharge home with self-care, discharge on the weekend, and discharge from a nonquality improvement unit (Table 1). Patients with DCBN were also more likely to be white, non-Hispanic, and male.

Our propensity score weighted ordinary least score (OLS) LOS regression results are presented in Table 2. In the bivariate analysis, DCBN was associated with an average 0.40 day, or roughly 10 hours, shorter LOS (P < .001). In the multivariate model of all discharges, we found that DCBN was associated with a mean of 0.27 day (P = .010) shorter LOS when compared to discharge in the afternoon when controlling for age, race, ethnicity, weekend discharge, discharge from quality improvement unit, discharge service type, CMI, insurance type, and discharge disposition. In the multivariate analysis, weekend discharge, surgical discharge, and discharge disposition of home with self-care, compared to assisted living or home health were associated with shorter LOS.

There was no evidence of multicollinearity (mean VIF of 1.14). The Wald test returned an F statistic of 27.50 (P < .001) indicating there was a structural difference in the relationship between LOS and DCBN dependent on discharge service type; thus, we ran separate surgical and medical discharge models to interpret model coefficients for both service types. When we analyzed surgical and medical discharges in separate models, the effect of DCBN on LOS in the medical discharges model was significantly associated with a 0.30 day (P = .017) shorter LOS (Table 2). The association was not significant in the surgical discharges model.

To further test the analysis, we increased the LOS outlier exclusion criteria to three and four standard deviations. Being more inclusive with LOS outliers in the sample resulted in a larger DCBN effect size that was significant in all three multivariate models (Supplemental Table 1).

DISCUSSION

In our study of over 8,000 pediatric discharges during a three-year period, DCBN was associated with shorter LOS for medical pediatric patients, but this finding was not consistent for surgical patients. Among medical discharges, DCBN was associated with shorter LOS, an effect robust enough to include or exclude outliers (for LOS, outliers are an important subset because there are always, in general, a few patients with very long lengths of stay). Discharge before noon showed no association with LOS for surgical patients unless we included outlier values.

The differential effect of DCBN on LOS in surgical and medical discharges suggests that the relationship between DCBN and LOS may be related to provider team workflow. For example, surgical teams may tend to round one time per day early in the morning before spending the entire day in the operating room, and thus completing more early morning discharge orders compared to medical teams. However, if a patient on a surgical service is not ready for discharge first thing in the morning, the patient may be more likely to wait until the following morning for a discharge order. On medical services, physician schedules may allow for more flexibility for rounding and responding with a discharge order when a patient becomes ready; however, medical services may round later in the day compared to surgeons and for a longer period of time, delaying discharges beyond noon that could have been made earlier. Another possibility, given UNC pediatric services are loosely regionalized with surgical patients concentrated more in one unit, is that unit-level differences in how staff processed discharges could have contributed to the difference observed between medical and surgical patients, particularly as there was a unit-level quality improvement effort for decreasing discharge time on one of two medical floors. However, we analyzed for differences based on the discharging unit and found no association. The influence of outliers on the association between DCBN and LOS increases also suggests that this group of children who have extremely long hospital stays might need further exploration.

Our study has some similar and some contrasting results with prior studies in adult patients. Our findings support the modeling literature that suggests DCBN may improve discharge efficiency by shortening patient LOS for some discharges.⁴ These findings contrast with Rajkomar et al., who reported that DCBN was associated with a longer LOS in adult patients.⁶ The contrasting findings could be due to differences in pediatric versus adult patients. Additionally, the population Rajkomar et al. studied was predominantly surgical patients, whose discharges may differ from medical patients’ in many aspects. Another possible explanation is that the Rajkomar et al. study was performed in a setting with clearly set institutional targets for DCBN, whereas, our institution lacked any hospital-wide DCBN initiatives or standards to which providers were held accountable. Some authors have argued setting DCBN as a measure of hospital quality perhaps creates the unintended consequence of providers holding potential afternoon or evening discharges until the next day so that they can be DCBN.^7,10 In that scenario, perhaps there would be a relationship between DCBN and longer LOS compared to patients who are reevaluated in the afternoon or evening and discharged. We did not find evidence of these effects in our analysis, however, understanding the potential for this is important when designing quality improvement efforts aimed at increasing discharge efficiency.

While shorter LOS can be an indicator of high-value care, the relationship between LOS, DCBN, and efficiency of discharge processes remains unclear. Prior studies have found evidence that multidisciplinary care teams with frequent care coordination rounds and integration of electronic admission order sets can be effective in improving discharge efficiency as measured by discharge within two hours of meeting discharge goals.^11,12 Measuring discharge efficiency on an ongoing basis is very difficult; however, easy-to-measure targets such as discharge before noon may be used as a proxy measure of efficiency. These targets also have “face validity,” and because of these two factors, measures like DCBN have been widely implemented even though evidence to support their validity is minimal.

Our study has several limitations. While we controlled for observable characteristics using covariates and propensity score weighted analyses, there are likely unobservable characteristics that confound our analysis. We did not measure other factors that may affect discharge time of day such as high occupancy, staffing levels, patient transportation availability, and patient and family preferences. Given these limitations, we caution against interpreting a causal relationship between independent variables and the outcome. Finally, this analysis was conducted at a single tertiary care, academic medical center. The majority of pediatric admissions at this institution are either transferred from other hospitals or scheduled admissions for medical or surgical care. A smaller proportion of discharges are acute, unplanned admissions through our emergency department in children with or without underlying medical complexity. These factors plus the exclusion of observation, extended recovery, and all the less than two-day stays in this study contribute to a relatively higher average LOS. These factors potentially limit generalizability to other care settings. Additionally, the majority of the care teams involve care by resident physicians, and they are often the primary caregivers and write the majority of orders in patient charts such as discharge orders. While we were not able to control for within resident physician similarities between patients, we did control for autocorrelation at the attending level.

 

CONCLUSION

The results of our study suggest that DCBN is associated with a decreased LOS for medical but not surgical pediatric patients. DCBN may not be an appropriate measure for all services. Further research should be done to identify other feasible but more valid indicators for shorter LOS.

Disclosures

The authors have no financial relationships relative to this article to disclose. The authors have no conflicts of interest relevant to this article to disclose.

Funding

There were no external sources of funding for this work.

 

Community-acquired pneumonia (CAP) is the most common infection in hospitalized patients and the eighth most common cause of death in the United States.¹ Mortality from CAP is estimated to be 5.1% in the outpatient population,13.6% in hospitalized patients, and 35.1% in patients admitted to the intensive care unit.^2,3 CAP accounts for more than 50,000 deaths annually in the United States.² There are multiple risk factors for CAP, including tobacco use, malnutrition, chronic obstructive pulmonary disease (COPD), bronchiectasis, cystic fibrosis, and mechanical bronchial obstruction. Underlying immunodeficiency, specifically humoral immunodeficiency, is also a risk factor for CAP.

Primary immunodeficiency (PIDD) is estimated to affect one in 1,800 individuals in the United States.⁴ The National Institutes of Health (NIH) estimates that only one out of three individuals with PIDD are appropriately diagnosed. Based on probability calculations on known PIDD patients versus incidence of disease, the NIH estimates that more than 500,000 individuals with PIDD remain undiagnosed in the United States.⁴ Further, there exists an average diagnostic delay of at least five years. This delay increases both morbidity and mortality and leads to increased healthcare utilization.^5,6

The most common form of primary immunodeficiency is due to humoral immunodeficiency, including selective IgA deficiency, specific antibody deficiency, and common variable immunodeficiency. Specific antibody deficiency is defined as a lack of response to polysaccharide antigens in the setting of low to normal Ig levels and an intact response to peptide antigens.⁷ Selective IgA deficiency is defined as the isolated deficiency of serum IgA in the setting of normal serum levels of IgG and IgM in an individual older than four years in whom other causes of hypogammaglobinemia have been excluded.⁸ Common variable immunodeficiency (CVID) is defined as a decreased serum concentration of IgG in combination with low levels of IgA and/or IgM with a poor or absent response to immunization in the absence of other defined immunodeficiency state.⁹ In addition to experiencing recurrent infections—namely bronchitis, sinusitis, otitis, and pneumonia—patients with CVID are also at increased risk of autoimmunity and malignancy. In adults, secondary immunodeficiency is more common than primary immunodeficiency. Secondary immunodeficiency occurs commonly with disease states like HIV infection, diabetes, cirrhosis, malnutrition, and autoimmune conditions.¹⁰ Additional causes of secondary immune defects due to humoral immunodeficiency include immune-modulating drugs—such as rituximab and ibrutinib—and hematologic malignancies, including chronic lymphocytic leukemia and multiple myeloma. Recurrent infections remain the leading cause of morbidity and mortality in patients with both primary and secondary immunodeficiency.^11,12

Evaluation of the humoral immune system begins with measurement of serum immunoglobulin (Ig) levels. Although abnormal Ig levels are not diagnostic of immunodeficiency, abnormal results may prompt additional evaluation. Screening strategies may assist in making an earlier diagnoses, potentially decreasing morbidity and mortality in patients with immunodeficiency.^13-15 To date, there have been no studies evaluating the utility of screening Ig levels to evaluate for underlying humoral immunodeficiency in patients hospitalized for CAP.

 

METHODS

Study Design

This was a prospective cohort study conducted at Rochester General Hospital, a 528-bed tertiary care medical center, from February 2017 to April 2017. We enrolled 100 consecutive patients admitted to the inpatient internal medicine service with a physician diagnosis of CAP. Written consent was obtained from each patient. The study was approved by the institutional review board at Rochester General Hospital.

Case Definition

The following criteria were used to diagnose CAP: (1) Respiratory symptoms of productive cough or pleuritic chest pain, (2) Fever >38°C before or at the time of admission, and (3) chest imaging with infiltrate. Exclusion criteria included a diagnosis of hospital-acquired pneumonia, prior diagnosis of primary immunodeficiency, immunosuppression due to an underlying condition, such as HIV or malignancy, therapy with immunosuppressive medications including chemotherapy, Ig replacement within the past six months, or treatment with >10 mg prednisone for greater than 14 days before hospital admission.

Patients underwent an additional evaluation by a clinical immunologist if they met one of the following criteria: any hypergammaglobinemia (elevated IgG, IgM, or IgA), IgG hypogammaglobinemia <550 mg/dL, undetectable IgM or IgA, or if IgG, IgM, and IgA were all below the lower limit of normal.

CURB-65 was used for estimation of the severity of illness with CAP. The components of the score include age ≥65, confusion, BUN >19 mg/dl, respiratory rate ≥30 breaths per minute and systolic blood pressure <90 mm Hg or diastolic blood pressure ≤60 mm Hg. Each component is scored zero if absent or one if present. Predicted mortality ranges from 0.6% for a score of zero to 27.8% for a score of 5.

Data Collection

Patient health information including age, race, gender, medical history, admission notes, results of chest imaging studies, and relevant laboratory studies including serum levels of IgG, IgM, IgA, IgE on admission was obtained from the electronic medical health record. An additional evaluation by the immunologist occurred within three months of hospital discharge and included repeat Ig levels, pre- and postvaccination titers of polysaccharide and peptide antigens, serum protein electrophoresis, and B & T cell panels.

Description of Normal Levels

The normal levels of immunoglobulins were defined based on standard reference ranges at the laboratory at Rochester General Hospital; IgG (700-1,600 mg/dl), IgM (50-300 mg/dl), IgA (70-400 mg/dl), and IgE (0-378 IU/ml). Although there is no established classification regarding the degree of IgG hypogammaglobinemia,¹⁶ clinical immunologists commonly classify the severity of IgG hypogammaglobinemia as follows: mild (550-699 mg/dL), moderate (400-549 mg/dL), and severe (<400 mg/dL) IgG hypogammaglobinemia.

Statistical Analysis

Statistical analysis was performed using STATA software (StataCorp LLC, College Station, Texas). We conducted a Wilcoxon rank-sum test to compare the median difference in length of stay between groups with a low versus normal range of immunoglobulins. A Kruskal–Wallis test was performed to check for the median difference in IgG levels across degrees of illness severity (CURB-65 score categories). We conducted a simple linear regression analysis using the logarithmic data of the length of stay and IgG level variables. A chi-square test was used to determine the association between comorbidities and Ig levels.

 

RESULTS

Baseline Characteristics

There were 100 patients with CAP enrolled in this study with a median age of 65.04 ± 18.8, and 53% were female. Forty-seven patients reported a previous history of pneumonia and 18 reported a history of recurrent sinusitis or otitis media. Of the 100 enrolled patients, 46 had received pneumococcal polysaccharide vaccine (PPV23), 26 had received the 13-valent pneumococcal conjugate vaccine (PCV13), and 22 had received both (Table 1). The mean white blood cell count on admission was 12.9 ± 7 × 10³/uL with 75 ± 12.5% neutrophils. Total protein (6.5 ± 0.8) and albumin (3.7 ± 0.5) were within the normal range for the study population.

Immunoglobulin Analyses

The prevalence of hypogammaglobinemia in the study was 38% (95% CI: 28.47% to 48.25%). The median values of Ig levels for the entire study population and in patients with hypogammaglobinemia are summarized in Table 2.

• IgG hypogammaglobinemia (<700 mg/dl) was found in 27/100 patients, with a median level of 598 mg/dL, IQ range: 459-654. The median age in this group was 76.5 years, and 13 were female. Of these 27 patients, 10 had low IgM, four had low IgA, and four had an elevated IgE. In this group, 11 patients had received PPSV23, nine had received PCV13, and six had received both PPV23 and PCV13 before the index hospital admission.
• IgG hypergammaglobinemia (>1,600 mg/dl) was found in 9/100 patients, with a median level of 1,381 mg/dL, IQ range: 1,237-1,627. The median age was 61 years, and six were female. Of these nine patients, three had low IgM, one had low IgA, and four had elevated IgE.
• IgM hypogammaglobinemia (<50 mg/dl) was found in 23/100 patients with a median level of 38 mg/dL, IQ range: 25-43. In this group, the median age was 69 years, and 10 were female. Of these 23 patients, 10 had low IgG, and three had an elevated IgG.
• IgM hypergammaglobinemia (>300 mg/dl) was noted in two patients, with a median level of 491 mg/dL, IQ range: 418-564. Both patients were female, and one had elevated IgG.
• IgA hypogammaglobinemia (<70 mg/dl) was discovered in six patients, with a median level of 36 mg/dL, IQ range: 18-50. In this group, four patients had low IgG, four had low IgM, one had elevated IgE, and one had elevated IgG.
• IgA hypergammaglobinemia (>400 mg/dl) was noted in five patients, with a median level of 561 mg/dL, IQ range: 442-565: Two patients were female. Of these five patients, one had high IgG, and one had low IgG.

Length of Stay and Severity of Pneumonia

The median length of stay in the hospital for the entire study population was three days (IQ range: 2-5.5 days). Among patients with IgG hypogammaglobinemia, the median length of stay was two days longer as compared with patients who had IgG levels in the normal range (5 days, IQ range [3-10] vs 3days, IQ range [2-5], P = .0085).

 

The median CURB-65 score for the entire study population was two (IQ range: 1-3). The median CURB-65 score did not differ between patients with low and normal ranges of IgG levels (Median: 2, IQ range [1-3] vs Median: 1, IQ range [0-3], P = .2922). The CURB-65 score was not correlated with IgG levels (ρ = −0.0776, P = .4428). Length of stay, however, was positively correlated with CURB-65 score (ρ = .4673, P = .000)

A simple linear regression analysis using the logarithmic transformation of both length of stay and IgG level revealed a linear relationship between serum IgG levels and hospital length of stay (P = .0335, [R² = .0453]).

Comorbidities and New Diagnoses

No significant association was found between smoking status, obesity, COPD, asthma, diabetes mellitus, and hypogammaglobinemia.

Fourteen patients with abnormal Ig levels as defined by (1) the presence of hypergammaglobinemia (elevated IgG, IgM, or IgA), (2) IgG levels <550, (3) undetectable IgA or IgM, and (4) either IgG or both IgM and IgA below the lower limit of normal underwent further evaluation. Of these 14 patients, one was diagnosed with multiple myeloma, one with selective IgA deficiency, and three with specific antibody deficiency (Table 3).

DISCUSSION

Previous research has evaluated the humoral immune system during an episode of CAP.^17-20 Studies on Ig levels in patients with CAP have shown hypogammaglobinemia to be associated with ICU admission and increased ICU mortality.^17,20 Additionally, patients with CAP have been shown to have lower IgG₂ levels than healthy controls. The goal of our study was to evaluate patients with CAP for humoral immunodeficiency.

 

In our study, the prevalence of low Ig levels in CAP was 38%, with IgG hypogammaglobinemia being the most common class of hypogammaglobinemia. This rate is slightly higher than that found in a previous work by de la Torri et al.,²¹ who reported a prevalence of 28.9% in the inpatient population. The lower prevalence in the de la Torri et al. study was likely secondary to the exclusion of patients who did not have recorded Ig levels.²¹ Additionally, de la Torri et al. noted an inverse relationship between serum IgG levels and CURB-65. These results were not replicated in our analysis. This is likely due to the relatively low number of patients in each category of CURB-65 score in our study focusing only on inpatients. However, low IgG levels were associated with increased length of stay (5 days, IQ range [3-10] vs 3 days, IQ range [2-5]).

Sepsis can cause hypogammaglobinemia.^22,23 The mechanism behind this phenomenon remain unclear, but several theories have been proposed. Sepsis results in endothelial dysfunction, vascular leakage, lymphopenia, and quantitative and qualitative defects in T and B cells.²³ This potentially leads to impaired production and increased catabolism of immunoglobulins. Immunoglobulins play an essential role in recovery from sepsis, and there may be increased consumption during acute illness.^24-28 Regardless of the mechanism, hypogammaglobinemia with SIRS, sepsis, and septic shock has been shown to be a risk factor for increased mortality in these patients.^22,23 There is currently no consensus on the optimal time to screen for humoral immunodeficiency or evaluate the immune system after infection, such as CAP. Some would argue that Ig levels are lower during an active illness and, therefore, this may not be an appropriate time to evaluate Ig levels. However, we believe that inpatient hospitalization for CAP provides a window of opportunity to selectively screen these patients at higher risk for PIDD for underlying immune defects. A hospital-based approach as demonstrated in this study may be more productive than relying on an outpatient evaluation, which often may not occur due to patient recall and/or fragmentation of care, thus leading to the well-recognized delay in diagnosis of immunodeficiency.^5,6In our study, one patient was diagnosed with multiple myeloma, three were diagnosed with specific antibody deficiency, and one was diagnosed with selective IgA deficiency. The patient with multiple myeloma was a 79-year old male who presented with his first ever episode of CAP, along with modest anemia and a creatinine of 1.6. His only other infectious history included an episode of sinusitis and one episode of pharyngitis. Additional evaluation included serum and urine electrophoresis, followed by bone marrow biopsy. This patient’s multiple myeloma diagnoses may have been missed if Ig levels had not been evaluated. Three patients were diagnosed with specific antibody deficiency. All these patients were above 50 years of age; two out of the three patients in this group had experienced a previous episode of pneumonia, and one had a history of recurrent sinusitis. Lastly, one patient was diagnosed with selective IgA deficiency as defined by undetectable IgA in the setting of normal IgG and IgM. This 56-year-old patient had a history of multiple episodes of sinusitis and three previous episodes of pneumonia, one requiring inpatient hospitalization. Earlier diagnosis of patients with specific antibody deficiency and selective IgA deficiency can guide management, which focuses on appropriate vaccination, the use of prophylactic antibiotics, and the possible role of Ig replacement in patients with specific antibody deficiency.

Of the 100 patients who underwent screening for immunodeficiency in the setting of CAP, five were found to have clinically significant humoral immunodeficiency, resulting in a number needed to screen of 20 to detect a clinically meaningful immunodeficiency in the setting of CAP. The number needed to screen by colonoscopy to detect one large bowel neoplasm in patients >50 years of age is 23.²⁹ The number needed to screen to diagnose one occult cancer after an unprovoked DVT is 91.³⁰ Based on this information, we feel that future, larger studies are required to evaluate the utility and cost-effectiveness of routine Ig screening for CAP requiring inpatient hospital admission.

We acknowledge limitations to this study. First, this study only evaluated adults in the inpatient floor setting, and therefore the results cannot be applied to the pediatric population or patients in the outpatient or ICU setting. Second, rather than completing a follow-up evaluation in all patients with abnormal immunoglobulins, we selected patients for additional evaluation based on criteria predefined by an immunologist. Although our rationale was to minimize additional diagnostic testing in individuals with mild hypogammaglobinemia, we acknowledge that this could have led to missing subtler humoral defects, such as a patient with near-normal Ig levels but a suboptimal response to vaccination. Third, due to the design of the study, we did not have a healthy matched control group. Despite these limitations, we believe our results are clinically meaningful and warrant future, larger scale investigation.

In conclusion, there is a high prevalence of hypogammaglobinemia in patients admitted with the diagnosis of CAP. IgG hypogammaglobinemia is the most commonly decreased class of Ig, and hospital length of stay is significantly longer in patients with low levels of IgG during admission for CAP. Additional immune evaluation of patients with CAP and abnormal Ig levels may also result in the identification of underlying antibody deficiency or immunoproliferative disorders.

 

 

Disclosures

The authors have nothing to disclose

 

Community-acquired pneumonia (CAP) is the most common infection in hospitalized patients and the eighth most common cause of death in the United States.¹ Mortality from CAP is estimated to be 5.1% in the outpatient population,13.6% in hospitalized patients, and 35.1% in patients admitted to the intensive care unit.^2,3 CAP accounts for more than 50,000 deaths annually in the United States.² There are multiple risk factors for CAP, including tobacco use, malnutrition, chronic obstructive pulmonary disease (COPD), bronchiectasis, cystic fibrosis, and mechanical bronchial obstruction. Underlying immunodeficiency, specifically humoral immunodeficiency, is also a risk factor for CAP.

Primary immunodeficiency (PIDD) is estimated to affect one in 1,800 individuals in the United States.⁴ The National Institutes of Health (NIH) estimates that only one out of three individuals with PIDD are appropriately diagnosed. Based on probability calculations on known PIDD patients versus incidence of disease, the NIH estimates that more than 500,000 individuals with PIDD remain undiagnosed in the United States.⁴ Further, there exists an average diagnostic delay of at least five years. This delay increases both morbidity and mortality and leads to increased healthcare utilization.^5,6

The most common form of primary immunodeficiency is due to humoral immunodeficiency, including selective IgA deficiency, specific antibody deficiency, and common variable immunodeficiency. Specific antibody deficiency is defined as a lack of response to polysaccharide antigens in the setting of low to normal Ig levels and an intact response to peptide antigens.⁷ Selective IgA deficiency is defined as the isolated deficiency of serum IgA in the setting of normal serum levels of IgG and IgM in an individual older than four years in whom other causes of hypogammaglobinemia have been excluded.⁸ Common variable immunodeficiency (CVID) is defined as a decreased serum concentration of IgG in combination with low levels of IgA and/or IgM with a poor or absent response to immunization in the absence of other defined immunodeficiency state.⁹ In addition to experiencing recurrent infections—namely bronchitis, sinusitis, otitis, and pneumonia—patients with CVID are also at increased risk of autoimmunity and malignancy. In adults, secondary immunodeficiency is more common than primary immunodeficiency. Secondary immunodeficiency occurs commonly with disease states like HIV infection, diabetes, cirrhosis, malnutrition, and autoimmune conditions.¹⁰ Additional causes of secondary immune defects due to humoral immunodeficiency include immune-modulating drugs—such as rituximab and ibrutinib—and hematologic malignancies, including chronic lymphocytic leukemia and multiple myeloma. Recurrent infections remain the leading cause of morbidity and mortality in patients with both primary and secondary immunodeficiency.^11,12

Evaluation of the humoral immune system begins with measurement of serum immunoglobulin (Ig) levels. Although abnormal Ig levels are not diagnostic of immunodeficiency, abnormal results may prompt additional evaluation. Screening strategies may assist in making an earlier diagnoses, potentially decreasing morbidity and mortality in patients with immunodeficiency.^13-15 To date, there have been no studies evaluating the utility of screening Ig levels to evaluate for underlying humoral immunodeficiency in patients hospitalized for CAP.

 

METHODS

Study Design

This was a prospective cohort study conducted at Rochester General Hospital, a 528-bed tertiary care medical center, from February 2017 to April 2017. We enrolled 100 consecutive patients admitted to the inpatient internal medicine service with a physician diagnosis of CAP. Written consent was obtained from each patient. The study was approved by the institutional review board at Rochester General Hospital.

Case Definition

The following criteria were used to diagnose CAP: (1) Respiratory symptoms of productive cough or pleuritic chest pain, (2) Fever >38°C before or at the time of admission, and (3) chest imaging with infiltrate. Exclusion criteria included a diagnosis of hospital-acquired pneumonia, prior diagnosis of primary immunodeficiency, immunosuppression due to an underlying condition, such as HIV or malignancy, therapy with immunosuppressive medications including chemotherapy, Ig replacement within the past six months, or treatment with >10 mg prednisone for greater than 14 days before hospital admission.

Patients underwent an additional evaluation by a clinical immunologist if they met one of the following criteria: any hypergammaglobinemia (elevated IgG, IgM, or IgA), IgG hypogammaglobinemia <550 mg/dL, undetectable IgM or IgA, or if IgG, IgM, and IgA were all below the lower limit of normal.

CURB-65 was used for estimation of the severity of illness with CAP. The components of the score include age ≥65, confusion, BUN >19 mg/dl, respiratory rate ≥30 breaths per minute and systolic blood pressure <90 mm Hg or diastolic blood pressure ≤60 mm Hg. Each component is scored zero if absent or one if present. Predicted mortality ranges from 0.6% for a score of zero to 27.8% for a score of 5.

Data Collection

Patient health information including age, race, gender, medical history, admission notes, results of chest imaging studies, and relevant laboratory studies including serum levels of IgG, IgM, IgA, IgE on admission was obtained from the electronic medical health record. An additional evaluation by the immunologist occurred within three months of hospital discharge and included repeat Ig levels, pre- and postvaccination titers of polysaccharide and peptide antigens, serum protein electrophoresis, and B & T cell panels.

Description of Normal Levels

The normal levels of immunoglobulins were defined based on standard reference ranges at the laboratory at Rochester General Hospital; IgG (700-1,600 mg/dl), IgM (50-300 mg/dl), IgA (70-400 mg/dl), and IgE (0-378 IU/ml). Although there is no established classification regarding the degree of IgG hypogammaglobinemia,¹⁶ clinical immunologists commonly classify the severity of IgG hypogammaglobinemia as follows: mild (550-699 mg/dL), moderate (400-549 mg/dL), and severe (<400 mg/dL) IgG hypogammaglobinemia.

Statistical Analysis

Statistical analysis was performed using STATA software (StataCorp LLC, College Station, Texas). We conducted a Wilcoxon rank-sum test to compare the median difference in length of stay between groups with a low versus normal range of immunoglobulins. A Kruskal–Wallis test was performed to check for the median difference in IgG levels across degrees of illness severity (CURB-65 score categories). We conducted a simple linear regression analysis using the logarithmic data of the length of stay and IgG level variables. A chi-square test was used to determine the association between comorbidities and Ig levels.

 

RESULTS

Baseline Characteristics

There were 100 patients with CAP enrolled in this study with a median age of 65.04 ± 18.8, and 53% were female. Forty-seven patients reported a previous history of pneumonia and 18 reported a history of recurrent sinusitis or otitis media. Of the 100 enrolled patients, 46 had received pneumococcal polysaccharide vaccine (PPV23), 26 had received the 13-valent pneumococcal conjugate vaccine (PCV13), and 22 had received both (Table 1). The mean white blood cell count on admission was 12.9 ± 7 × 10³/uL with 75 ± 12.5% neutrophils. Total protein (6.5 ± 0.8) and albumin (3.7 ± 0.5) were within the normal range for the study population.

Immunoglobulin Analyses

The prevalence of hypogammaglobinemia in the study was 38% (95% CI: 28.47% to 48.25%). The median values of Ig levels for the entire study population and in patients with hypogammaglobinemia are summarized in Table 2.

• IgG hypogammaglobinemia (<700 mg/dl) was found in 27/100 patients, with a median level of 598 mg/dL, IQ range: 459-654. The median age in this group was 76.5 years, and 13 were female. Of these 27 patients, 10 had low IgM, four had low IgA, and four had an elevated IgE. In this group, 11 patients had received PPSV23, nine had received PCV13, and six had received both PPV23 and PCV13 before the index hospital admission.
• IgG hypergammaglobinemia (>1,600 mg/dl) was found in 9/100 patients, with a median level of 1,381 mg/dL, IQ range: 1,237-1,627. The median age was 61 years, and six were female. Of these nine patients, three had low IgM, one had low IgA, and four had elevated IgE.
• IgM hypogammaglobinemia (<50 mg/dl) was found in 23/100 patients with a median level of 38 mg/dL, IQ range: 25-43. In this group, the median age was 69 years, and 10 were female. Of these 23 patients, 10 had low IgG, and three had an elevated IgG.
• IgM hypergammaglobinemia (>300 mg/dl) was noted in two patients, with a median level of 491 mg/dL, IQ range: 418-564. Both patients were female, and one had elevated IgG.
• IgA hypogammaglobinemia (<70 mg/dl) was discovered in six patients, with a median level of 36 mg/dL, IQ range: 18-50. In this group, four patients had low IgG, four had low IgM, one had elevated IgE, and one had elevated IgG.
• IgA hypergammaglobinemia (>400 mg/dl) was noted in five patients, with a median level of 561 mg/dL, IQ range: 442-565: Two patients were female. Of these five patients, one had high IgG, and one had low IgG.

Length of Stay and Severity of Pneumonia

The median length of stay in the hospital for the entire study population was three days (IQ range: 2-5.5 days). Among patients with IgG hypogammaglobinemia, the median length of stay was two days longer as compared with patients who had IgG levels in the normal range (5 days, IQ range [3-10] vs 3days, IQ range [2-5], P = .0085).

 

The median CURB-65 score for the entire study population was two (IQ range: 1-3). The median CURB-65 score did not differ between patients with low and normal ranges of IgG levels (Median: 2, IQ range [1-3] vs Median: 1, IQ range [0-3], P = .2922). The CURB-65 score was not correlated with IgG levels (ρ = −0.0776, P = .4428). Length of stay, however, was positively correlated with CURB-65 score (ρ = .4673, P = .000)

A simple linear regression analysis using the logarithmic transformation of both length of stay and IgG level revealed a linear relationship between serum IgG levels and hospital length of stay (P = .0335, [R² = .0453]).

Comorbidities and New Diagnoses

No significant association was found between smoking status, obesity, COPD, asthma, diabetes mellitus, and hypogammaglobinemia.

Fourteen patients with abnormal Ig levels as defined by (1) the presence of hypergammaglobinemia (elevated IgG, IgM, or IgA), (2) IgG levels <550, (3) undetectable IgA or IgM, and (4) either IgG or both IgM and IgA below the lower limit of normal underwent further evaluation. Of these 14 patients, one was diagnosed with multiple myeloma, one with selective IgA deficiency, and three with specific antibody deficiency (Table 3).

DISCUSSION

Previous research has evaluated the humoral immune system during an episode of CAP.^17-20 Studies on Ig levels in patients with CAP have shown hypogammaglobinemia to be associated with ICU admission and increased ICU mortality.^17,20 Additionally, patients with CAP have been shown to have lower IgG₂ levels than healthy controls. The goal of our study was to evaluate patients with CAP for humoral immunodeficiency.

 

In our study, the prevalence of low Ig levels in CAP was 38%, with IgG hypogammaglobinemia being the most common class of hypogammaglobinemia. This rate is slightly higher than that found in a previous work by de la Torri et al.,²¹ who reported a prevalence of 28.9% in the inpatient population. The lower prevalence in the de la Torri et al. study was likely secondary to the exclusion of patients who did not have recorded Ig levels.²¹ Additionally, de la Torri et al. noted an inverse relationship between serum IgG levels and CURB-65. These results were not replicated in our analysis. This is likely due to the relatively low number of patients in each category of CURB-65 score in our study focusing only on inpatients. However, low IgG levels were associated with increased length of stay (5 days, IQ range [3-10] vs 3 days, IQ range [2-5]).

Sepsis can cause hypogammaglobinemia.^22,23 The mechanism behind this phenomenon remain unclear, but several theories have been proposed. Sepsis results in endothelial dysfunction, vascular leakage, lymphopenia, and quantitative and qualitative defects in T and B cells.²³ This potentially leads to impaired production and increased catabolism of immunoglobulins. Immunoglobulins play an essential role in recovery from sepsis, and there may be increased consumption during acute illness.^24-28 Regardless of the mechanism, hypogammaglobinemia with SIRS, sepsis, and septic shock has been shown to be a risk factor for increased mortality in these patients.^22,23 There is currently no consensus on the optimal time to screen for humoral immunodeficiency or evaluate the immune system after infection, such as CAP. Some would argue that Ig levels are lower during an active illness and, therefore, this may not be an appropriate time to evaluate Ig levels. However, we believe that inpatient hospitalization for CAP provides a window of opportunity to selectively screen these patients at higher risk for PIDD for underlying immune defects. A hospital-based approach as demonstrated in this study may be more productive than relying on an outpatient evaluation, which often may not occur due to patient recall and/or fragmentation of care, thus leading to the well-recognized delay in diagnosis of immunodeficiency.^5,6In our study, one patient was diagnosed with multiple myeloma, three were diagnosed with specific antibody deficiency, and one was diagnosed with selective IgA deficiency. The patient with multiple myeloma was a 79-year old male who presented with his first ever episode of CAP, along with modest anemia and a creatinine of 1.6. His only other infectious history included an episode of sinusitis and one episode of pharyngitis. Additional evaluation included serum and urine electrophoresis, followed by bone marrow biopsy. This patient’s multiple myeloma diagnoses may have been missed if Ig levels had not been evaluated. Three patients were diagnosed with specific antibody deficiency. All these patients were above 50 years of age; two out of the three patients in this group had experienced a previous episode of pneumonia, and one had a history of recurrent sinusitis. Lastly, one patient was diagnosed with selective IgA deficiency as defined by undetectable IgA in the setting of normal IgG and IgM. This 56-year-old patient had a history of multiple episodes of sinusitis and three previous episodes of pneumonia, one requiring inpatient hospitalization. Earlier diagnosis of patients with specific antibody deficiency and selective IgA deficiency can guide management, which focuses on appropriate vaccination, the use of prophylactic antibiotics, and the possible role of Ig replacement in patients with specific antibody deficiency.

Of the 100 patients who underwent screening for immunodeficiency in the setting of CAP, five were found to have clinically significant humoral immunodeficiency, resulting in a number needed to screen of 20 to detect a clinically meaningful immunodeficiency in the setting of CAP. The number needed to screen by colonoscopy to detect one large bowel neoplasm in patients >50 years of age is 23.²⁹ The number needed to screen to diagnose one occult cancer after an unprovoked DVT is 91.³⁰ Based on this information, we feel that future, larger studies are required to evaluate the utility and cost-effectiveness of routine Ig screening for CAP requiring inpatient hospital admission.

We acknowledge limitations to this study. First, this study only evaluated adults in the inpatient floor setting, and therefore the results cannot be applied to the pediatric population or patients in the outpatient or ICU setting. Second, rather than completing a follow-up evaluation in all patients with abnormal immunoglobulins, we selected patients for additional evaluation based on criteria predefined by an immunologist. Although our rationale was to minimize additional diagnostic testing in individuals with mild hypogammaglobinemia, we acknowledge that this could have led to missing subtler humoral defects, such as a patient with near-normal Ig levels but a suboptimal response to vaccination. Third, due to the design of the study, we did not have a healthy matched control group. Despite these limitations, we believe our results are clinically meaningful and warrant future, larger scale investigation.

In conclusion, there is a high prevalence of hypogammaglobinemia in patients admitted with the diagnosis of CAP. IgG hypogammaglobinemia is the most commonly decreased class of Ig, and hospital length of stay is significantly longer in patients with low levels of IgG during admission for CAP. Additional immune evaluation of patients with CAP and abnormal Ig levels may also result in the identification of underlying antibody deficiency or immunoproliferative disorders.

 

 

Disclosures

The authors have nothing to disclose

 

Community-acquired pneumonia (CAP) is the most common infection in hospitalized patients and the eighth most common cause of death in the United States.¹ Mortality from CAP is estimated to be 5.1% in the outpatient population,13.6% in hospitalized patients, and 35.1% in patients admitted to the intensive care unit.^2,3 CAP accounts for more than 50,000 deaths annually in the United States.² There are multiple risk factors for CAP, including tobacco use, malnutrition, chronic obstructive pulmonary disease (COPD), bronchiectasis, cystic fibrosis, and mechanical bronchial obstruction. Underlying immunodeficiency, specifically humoral immunodeficiency, is also a risk factor for CAP.

Primary immunodeficiency (PIDD) is estimated to affect one in 1,800 individuals in the United States.⁴ The National Institutes of Health (NIH) estimates that only one out of three individuals with PIDD are appropriately diagnosed. Based on probability calculations on known PIDD patients versus incidence of disease, the NIH estimates that more than 500,000 individuals with PIDD remain undiagnosed in the United States.⁴ Further, there exists an average diagnostic delay of at least five years. This delay increases both morbidity and mortality and leads to increased healthcare utilization.^5,6

The most common form of primary immunodeficiency is due to humoral immunodeficiency, including selective IgA deficiency, specific antibody deficiency, and common variable immunodeficiency. Specific antibody deficiency is defined as a lack of response to polysaccharide antigens in the setting of low to normal Ig levels and an intact response to peptide antigens.⁷ Selective IgA deficiency is defined as the isolated deficiency of serum IgA in the setting of normal serum levels of IgG and IgM in an individual older than four years in whom other causes of hypogammaglobinemia have been excluded.⁸ Common variable immunodeficiency (CVID) is defined as a decreased serum concentration of IgG in combination with low levels of IgA and/or IgM with a poor or absent response to immunization in the absence of other defined immunodeficiency state.⁹ In addition to experiencing recurrent infections—namely bronchitis, sinusitis, otitis, and pneumonia—patients with CVID are also at increased risk of autoimmunity and malignancy. In adults, secondary immunodeficiency is more common than primary immunodeficiency. Secondary immunodeficiency occurs commonly with disease states like HIV infection, diabetes, cirrhosis, malnutrition, and autoimmune conditions.¹⁰ Additional causes of secondary immune defects due to humoral immunodeficiency include immune-modulating drugs—such as rituximab and ibrutinib—and hematologic malignancies, including chronic lymphocytic leukemia and multiple myeloma. Recurrent infections remain the leading cause of morbidity and mortality in patients with both primary and secondary immunodeficiency.^11,12

Evaluation of the humoral immune system begins with measurement of serum immunoglobulin (Ig) levels. Although abnormal Ig levels are not diagnostic of immunodeficiency, abnormal results may prompt additional evaluation. Screening strategies may assist in making an earlier diagnoses, potentially decreasing morbidity and mortality in patients with immunodeficiency.^13-15 To date, there have been no studies evaluating the utility of screening Ig levels to evaluate for underlying humoral immunodeficiency in patients hospitalized for CAP.

 

METHODS

Study Design

This was a prospective cohort study conducted at Rochester General Hospital, a 528-bed tertiary care medical center, from February 2017 to April 2017. We enrolled 100 consecutive patients admitted to the inpatient internal medicine service with a physician diagnosis of CAP. Written consent was obtained from each patient. The study was approved by the institutional review board at Rochester General Hospital.

Case Definition

The following criteria were used to diagnose CAP: (1) Respiratory symptoms of productive cough or pleuritic chest pain, (2) Fever >38°C before or at the time of admission, and (3) chest imaging with infiltrate. Exclusion criteria included a diagnosis of hospital-acquired pneumonia, prior diagnosis of primary immunodeficiency, immunosuppression due to an underlying condition, such as HIV or malignancy, therapy with immunosuppressive medications including chemotherapy, Ig replacement within the past six months, or treatment with >10 mg prednisone for greater than 14 days before hospital admission.

Patients underwent an additional evaluation by a clinical immunologist if they met one of the following criteria: any hypergammaglobinemia (elevated IgG, IgM, or IgA), IgG hypogammaglobinemia <550 mg/dL, undetectable IgM or IgA, or if IgG, IgM, and IgA were all below the lower limit of normal.

CURB-65 was used for estimation of the severity of illness with CAP. The components of the score include age ≥65, confusion, BUN >19 mg/dl, respiratory rate ≥30 breaths per minute and systolic blood pressure <90 mm Hg or diastolic blood pressure ≤60 mm Hg. Each component is scored zero if absent or one if present. Predicted mortality ranges from 0.6% for a score of zero to 27.8% for a score of 5.

Data Collection

Patient health information including age, race, gender, medical history, admission notes, results of chest imaging studies, and relevant laboratory studies including serum levels of IgG, IgM, IgA, IgE on admission was obtained from the electronic medical health record. An additional evaluation by the immunologist occurred within three months of hospital discharge and included repeat Ig levels, pre- and postvaccination titers of polysaccharide and peptide antigens, serum protein electrophoresis, and B & T cell panels.

Description of Normal Levels

The normal levels of immunoglobulins were defined based on standard reference ranges at the laboratory at Rochester General Hospital; IgG (700-1,600 mg/dl), IgM (50-300 mg/dl), IgA (70-400 mg/dl), and IgE (0-378 IU/ml). Although there is no established classification regarding the degree of IgG hypogammaglobinemia,¹⁶ clinical immunologists commonly classify the severity of IgG hypogammaglobinemia as follows: mild (550-699 mg/dL), moderate (400-549 mg/dL), and severe (<400 mg/dL) IgG hypogammaglobinemia.

Statistical Analysis

Statistical analysis was performed using STATA software (StataCorp LLC, College Station, Texas). We conducted a Wilcoxon rank-sum test to compare the median difference in length of stay between groups with a low versus normal range of immunoglobulins. A Kruskal–Wallis test was performed to check for the median difference in IgG levels across degrees of illness severity (CURB-65 score categories). We conducted a simple linear regression analysis using the logarithmic data of the length of stay and IgG level variables. A chi-square test was used to determine the association between comorbidities and Ig levels.

 

RESULTS

Baseline Characteristics

There were 100 patients with CAP enrolled in this study with a median age of 65.04 ± 18.8, and 53% were female. Forty-seven patients reported a previous history of pneumonia and 18 reported a history of recurrent sinusitis or otitis media. Of the 100 enrolled patients, 46 had received pneumococcal polysaccharide vaccine (PPV23), 26 had received the 13-valent pneumococcal conjugate vaccine (PCV13), and 22 had received both (Table 1). The mean white blood cell count on admission was 12.9 ± 7 × 10³/uL with 75 ± 12.5% neutrophils. Total protein (6.5 ± 0.8) and albumin (3.7 ± 0.5) were within the normal range for the study population.

Immunoglobulin Analyses

The prevalence of hypogammaglobinemia in the study was 38% (95% CI: 28.47% to 48.25%). The median values of Ig levels for the entire study population and in patients with hypogammaglobinemia are summarized in Table 2.

• IgG hypogammaglobinemia (<700 mg/dl) was found in 27/100 patients, with a median level of 598 mg/dL, IQ range: 459-654. The median age in this group was 76.5 years, and 13 were female. Of these 27 patients, 10 had low IgM, four had low IgA, and four had an elevated IgE. In this group, 11 patients had received PPSV23, nine had received PCV13, and six had received both PPV23 and PCV13 before the index hospital admission.
• IgG hypergammaglobinemia (>1,600 mg/dl) was found in 9/100 patients, with a median level of 1,381 mg/dL, IQ range: 1,237-1,627. The median age was 61 years, and six were female. Of these nine patients, three had low IgM, one had low IgA, and four had elevated IgE.
• IgM hypogammaglobinemia (<50 mg/dl) was found in 23/100 patients with a median level of 38 mg/dL, IQ range: 25-43. In this group, the median age was 69 years, and 10 were female. Of these 23 patients, 10 had low IgG, and three had an elevated IgG.
• IgM hypergammaglobinemia (>300 mg/dl) was noted in two patients, with a median level of 491 mg/dL, IQ range: 418-564. Both patients were female, and one had elevated IgG.
• IgA hypogammaglobinemia (<70 mg/dl) was discovered in six patients, with a median level of 36 mg/dL, IQ range: 18-50. In this group, four patients had low IgG, four had low IgM, one had elevated IgE, and one had elevated IgG.
• IgA hypergammaglobinemia (>400 mg/dl) was noted in five patients, with a median level of 561 mg/dL, IQ range: 442-565: Two patients were female. Of these five patients, one had high IgG, and one had low IgG.

Length of Stay and Severity of Pneumonia

The median length of stay in the hospital for the entire study population was three days (IQ range: 2-5.5 days). Among patients with IgG hypogammaglobinemia, the median length of stay was two days longer as compared with patients who had IgG levels in the normal range (5 days, IQ range [3-10] vs 3days, IQ range [2-5], P = .0085).

 

The median CURB-65 score for the entire study population was two (IQ range: 1-3). The median CURB-65 score did not differ between patients with low and normal ranges of IgG levels (Median: 2, IQ range [1-3] vs Median: 1, IQ range [0-3], P = .2922). The CURB-65 score was not correlated with IgG levels (ρ = −0.0776, P = .4428). Length of stay, however, was positively correlated with CURB-65 score (ρ = .4673, P = .000)

A simple linear regression analysis using the logarithmic transformation of both length of stay and IgG level revealed a linear relationship between serum IgG levels and hospital length of stay (P = .0335, [R² = .0453]).

Comorbidities and New Diagnoses

No significant association was found between smoking status, obesity, COPD, asthma, diabetes mellitus, and hypogammaglobinemia.

Fourteen patients with abnormal Ig levels as defined by (1) the presence of hypergammaglobinemia (elevated IgG, IgM, or IgA), (2) IgG levels <550, (3) undetectable IgA or IgM, and (4) either IgG or both IgM and IgA below the lower limit of normal underwent further evaluation. Of these 14 patients, one was diagnosed with multiple myeloma, one with selective IgA deficiency, and three with specific antibody deficiency (Table 3).

DISCUSSION

Previous research has evaluated the humoral immune system during an episode of CAP.^17-20 Studies on Ig levels in patients with CAP have shown hypogammaglobinemia to be associated with ICU admission and increased ICU mortality.^17,20 Additionally, patients with CAP have been shown to have lower IgG₂ levels than healthy controls. The goal of our study was to evaluate patients with CAP for humoral immunodeficiency.

 

In our study, the prevalence of low Ig levels in CAP was 38%, with IgG hypogammaglobinemia being the most common class of hypogammaglobinemia. This rate is slightly higher than that found in a previous work by de la Torri et al.,²¹ who reported a prevalence of 28.9% in the inpatient population. The lower prevalence in the de la Torri et al. study was likely secondary to the exclusion of patients who did not have recorded Ig levels.²¹ Additionally, de la Torri et al. noted an inverse relationship between serum IgG levels and CURB-65. These results were not replicated in our analysis. This is likely due to the relatively low number of patients in each category of CURB-65 score in our study focusing only on inpatients. However, low IgG levels were associated with increased length of stay (5 days, IQ range [3-10] vs 3 days, IQ range [2-5]).

Sepsis can cause hypogammaglobinemia.^22,23 The mechanism behind this phenomenon remain unclear, but several theories have been proposed. Sepsis results in endothelial dysfunction, vascular leakage, lymphopenia, and quantitative and qualitative defects in T and B cells.²³ This potentially leads to impaired production and increased catabolism of immunoglobulins. Immunoglobulins play an essential role in recovery from sepsis, and there may be increased consumption during acute illness.^24-28 Regardless of the mechanism, hypogammaglobinemia with SIRS, sepsis, and septic shock has been shown to be a risk factor for increased mortality in these patients.^22,23 There is currently no consensus on the optimal time to screen for humoral immunodeficiency or evaluate the immune system after infection, such as CAP. Some would argue that Ig levels are lower during an active illness and, therefore, this may not be an appropriate time to evaluate Ig levels. However, we believe that inpatient hospitalization for CAP provides a window of opportunity to selectively screen these patients at higher risk for PIDD for underlying immune defects. A hospital-based approach as demonstrated in this study may be more productive than relying on an outpatient evaluation, which often may not occur due to patient recall and/or fragmentation of care, thus leading to the well-recognized delay in diagnosis of immunodeficiency.^5,6In our study, one patient was diagnosed with multiple myeloma, three were diagnosed with specific antibody deficiency, and one was diagnosed with selective IgA deficiency. The patient with multiple myeloma was a 79-year old male who presented with his first ever episode of CAP, along with modest anemia and a creatinine of 1.6. His only other infectious history included an episode of sinusitis and one episode of pharyngitis. Additional evaluation included serum and urine electrophoresis, followed by bone marrow biopsy. This patient’s multiple myeloma diagnoses may have been missed if Ig levels had not been evaluated. Three patients were diagnosed with specific antibody deficiency. All these patients were above 50 years of age; two out of the three patients in this group had experienced a previous episode of pneumonia, and one had a history of recurrent sinusitis. Lastly, one patient was diagnosed with selective IgA deficiency as defined by undetectable IgA in the setting of normal IgG and IgM. This 56-year-old patient had a history of multiple episodes of sinusitis and three previous episodes of pneumonia, one requiring inpatient hospitalization. Earlier diagnosis of patients with specific antibody deficiency and selective IgA deficiency can guide management, which focuses on appropriate vaccination, the use of prophylactic antibiotics, and the possible role of Ig replacement in patients with specific antibody deficiency.

Of the 100 patients who underwent screening for immunodeficiency in the setting of CAP, five were found to have clinically significant humoral immunodeficiency, resulting in a number needed to screen of 20 to detect a clinically meaningful immunodeficiency in the setting of CAP. The number needed to screen by colonoscopy to detect one large bowel neoplasm in patients >50 years of age is 23.²⁹ The number needed to screen to diagnose one occult cancer after an unprovoked DVT is 91.³⁰ Based on this information, we feel that future, larger studies are required to evaluate the utility and cost-effectiveness of routine Ig screening for CAP requiring inpatient hospital admission.

We acknowledge limitations to this study. First, this study only evaluated adults in the inpatient floor setting, and therefore the results cannot be applied to the pediatric population or patients in the outpatient or ICU setting. Second, rather than completing a follow-up evaluation in all patients with abnormal immunoglobulins, we selected patients for additional evaluation based on criteria predefined by an immunologist. Although our rationale was to minimize additional diagnostic testing in individuals with mild hypogammaglobinemia, we acknowledge that this could have led to missing subtler humoral defects, such as a patient with near-normal Ig levels but a suboptimal response to vaccination. Third, due to the design of the study, we did not have a healthy matched control group. Despite these limitations, we believe our results are clinically meaningful and warrant future, larger scale investigation.

In conclusion, there is a high prevalence of hypogammaglobinemia in patients admitted with the diagnosis of CAP. IgG hypogammaglobinemia is the most commonly decreased class of Ig, and hospital length of stay is significantly longer in patients with low levels of IgG during admission for CAP. Additional immune evaluation of patients with CAP and abnormal Ig levels may also result in the identification of underlying antibody deficiency or immunoproliferative disorders.

 

 

Disclosures

The authors have nothing to disclose

 

Hospitals are complex adaptive systems within which practitioners, technology, physical resources, and other components adapt interdependently to attempt to best meet the needs of patients.¹ Hospitals must provide a stable, dependable level of care while also surging to respond to times of high demand, such as patient emergencies or swells in patient volume. Given the critical and resource-intensive nature of this work, optimizing the system is essential; however, because of the complexity of the system, making changes can result in unexpected and possibly deleterious effects. We need to approach change in hospital processes carefully and thoughtfully.

The Institute of Medicine, the National Academy of Engineering, and the President’s Council of Advisors on Science and Technology have recommended the application of systems engineering approaches to improve health care delivery.^2,3 Systems engineering seeks to coordinate, synchronize, and integrate complex systems of people, information, materials, technology, and financial resources.^4,5 To determine how complex systems can be improved, engineers apply analytic methods to describe how such systems operate and what the impact of changes might be. These methodologies have improved patient care and reduced costs at several hospitals.⁶ For example, a decision support system that combined simulation, optimization, and machine learning methods in an emergency department (ED) resulted in a 33% reduction in length of stay (LOS) and a 28% decrease in ED readmissions.⁷ Other strategies to improve patient flow include shaping demand (decreasing variation in surgical scheduling, relocating low acuity care ED visit to primary care, etc.), redesigning systems (early discharges, improving efficiency, and coordination of hospital discharge process, decreasing care variation, etc.), or aligning capacity and demand. Another approach, real-time demand capacity (RTDC), is based on management principles and queuing and constraint theory and has been implemented successfully in a variety of health care organizations. RTDC represents a promising approach to improve hospitalwide patient flow and can be integrated into current bed management processes.⁸ Unfortunately, many of these approaches are not well known to clinicians and would benefit from greater awareness and input from healthcare practitioners.

One systems engineering tool that can be used to describe, analyze, and evaluate proposed changes in care is simulation.⁹ Simulation creates a model within which what-if scenarios (ie, adjusting various inputs into the simulation) allow researchers to define the likelihood of consequences from various courses of action and determine the optimal change to a system. Such analyses can predict the impact of a proposed change on patients and healthcare practitioners.^10-13

A critical concern for hospitals that simulation may help address is managing the volume of inpatients. A high inpatient census is necessary for financial solvency, yet too high a census of inpatients or an unexpected surge in acuity can overwhelm hospital resources. Many hospitals, pressured by growing numbers of increasingly complex patients, have seen medical inpatients spread across multiple nonmedical nursing units (NUs) of their institution such that a particular medical team may have only a couple patients assigned to each nursing unit.¹⁴ This dispersion may hinder communication between physicians and nurses and limits the time physicians have to interact with patients.¹⁵ Additionally, coordination of care may become more challenging for discharge planning.¹⁶ Aligning medical teams with NUs may benefit the quality and efficiency of care or may create a barrier to patient flow, which worsens these problems.^15,17 Alternatively, hospitals might meet the increasing demands for care by choosing to add capacity by opening new NUs or hiring additional healthcare providers. We identified no studies in the literature that applied simulation modeling to general medicine inpatients to evaluate the impact of these different decisions.

This article describes the application of simulation to model the interconnected variables and subsequent future states created by several possible strategic decisions around the care of general medicine inpatients. Through the application of systems engineering techniques, we modeled four future states that illustrate the following: (1) the complexities of a large health delivery system, (2) the intended and unintended consequences of implementing different changes in the process of care delivery, and (3) how the simulation modeling might be used to inform decision making.

 

METHODS

Setting and Present State

Virginia Commonwealth University (VCU) is a 865-bed tertiary academic medical center, with inpatient care activities spread between four connected buildings and 50 different NUs. The occupancy rate had been over 92% during the time period of this project with admission volume limited primarily by the capacity of the facility. Three of the NUs were primarily allocated to general medicine (GIM) patients. However, over the years, GIM inpatients grew to over 7500 admissions annually, resulting in nearly 50% of GIM patients being admitted to a non-GIM nursing unit.

Additionally, patients on each medical team had a high degree of spread across NUs due to several factors. Admissions and discharges from the hospital did not align across the day. While discharges clumped in the late afternoon, admission occurred throughout the day with a surge in the later afternoon. This mismatch frequently led to patients waiting in the ED for a bed, medical team, or both, and patients were typically assigned to the first available bed and team. For medical team assignments, newly admitted patients were distributed relatively equally across five hospitalist teams and five housestaff teams (that include residents, interns, and medical students). This steady distribution of patients through the day supported meeting housestaff work-hour restrictions of 80 hours each week.¹⁸ Yet, as a result of the high occupancy rate, the patterns of patient admissions and discharges, and the distribution of patients among medical teams and across NUs, medical teams and NUs rarely shared more than a few patients.

Leaders at our institution outlined several possible options to address these challenges, including aligning medical teams with NU, adding an additional hospitalist team, or adding an additional nursing unit. In addition, institutional leaders were concerned about the impact of continued growth in admission volume and the impact of patient dispersion on trainees and students. The overall goal of creating a simulation model was to determine the impact of an increased volume of patients and these possible strategic decisions on operational metrics, including number of patients waiting in the ED, ED boarding time per patient, time in system per patient (ED boarding time plus inpatient LOS), team utilization, and rounding travel time.

Simulation Modeling

To model the impact of some possible system changes on patient care, we applied Kelton and Law’s simulation study framework,¹⁹ including data collection; model building and validation; and what-if scenario testing (Figure 1).

Data Collection

Process Flow Map

We created a complex process flow map of patient care activities on medical teams. The map was developed by four general medicine physicians (R.C., H.M., V.M., and S.P.T.) who all provided medical care on the hospital-based services and ensured expert input on the patient care activities captured by the simulation modeling.

Time and Motion Studies

Time and motion study is a well-established technique used to evaluate the efficiency of work processes.^20,21 Originally applied to increase productivity in manufacturing, this technique uses first-hand observations to measure the time allotted to different work tasks to systematically analyze workflow.²² Workflow in healthcare, like manufacturing tasks, tends to have a repetitive pattern, making time and motion studies a highly applicable tool.

 

A research assistant observed a total of 30 hospitalist work cycles to describe the work of our inpatient clinicians. A work cycle, defined as one complete process flow,²³ began when the hospitalist started a daytime shift of patient care and concluded after the physician “signed out” to the physician who was assuming responsibility for ongoing medical care of the patients (ie, cross-coverage). Time spent on different activities identified by the process flow map was captured throughout the cycle. These activities included time spent traveling to evaluate patients located on different NUs. To minimize disruptions in patient care and adhere to privacy standards, no observations were conducted in patient rooms, and details of computer work were not recorded. To ensure stable estimates of the mean and standard deviation of the time spent at each step, at least 30 cycles of observation are recommended. Thus, 300 hours of observations over the course of 30 separate days were collected.

Hospital Data

We extracted admission and discharge data from the electronic health records (EHR) for general medicine patients admitted from the ED for the calendar year 2013. These records were used to establish means and standard deviations for admission date and time, distribution of patients across NUs, and LOS.

Model Building and Internal Validation

On the basis of these data inputs and using SIMIO® Simulation Software version 7, we constructed a discrete event simulation (DES) model representing the patient care activities of general medicine teams. Each patient was assigned a bed on a nursing unit through a probability distribution based on prior EHR data and then randomly assigned to a general medicine team. We replicated the model 200 times, and each model ran for 365 days. Each team was limited to 16 assigned patients, the maximum number of patients per housestaff team allowed by VCU protocol; henceforth, this number is referred to as team-patient capacity. The model assumed patients remained on the assigned nursing unit and medical team for the entirety of their hospital stay and that each patient was seen by their assigned medical team every day. The results of the present state model, including mean number of patients on each nursing unit, mean team census, patient dispersion (ie, the number of NUs on which each medical team had patients), and team utilization (ie, mean team census divided by team patient capacity), were compared with actual data from 2013 to internally validate the model.

What-If Scenario Testing

We constructed four what-if scenarios based on possible strategic directions identified by leadership. These models evaluated:

• constraining patients on housestaff (but not hospitalist) teams to the three general medicine NUs (Future State 1),
• increasing bed capacity for general medicine patients by adding one additional nursing unit of 26 beds (Future State 2),
• increasing the number of general medicine teams by adding one additional hospitalist team of up to 16 patients (Future State 3),
• modeling the impact of increased patient admissions from 21 per day to 25 per day while also adding a nursing unit and an additional medical team (Future State 4).

 

For Future States 1-3, admission volume was held constant. The model generated nursing unit LOS using a random continuous exponential probability distribution with a mean of 133 hours to match the LOS distribution derived from health system data. As patients entered the system for admission, the model assigned a bed to the patient, but the patient could not move to the assigned bed until a bed and care team were both available. We were only interested in the steady-state behavior of the system, so collecting performance statistics only after the model had been populated and steady state had been achieved was important.

Table 1 summarizes the input data, fixed, and dynamic variable for each future state model.

We examined the impact of these scenarios on the following variables (Table 2): (1) average time in system; (2) average number of patients waiting for a bed; (3) average ED boarding time; (4) total daily general medicine census; (5) average housestaff team census per team; (6) average hospitalist team census per team; (7) average combined housestaff and hospitalist team census per team; (8) average housestaff team utilization (ie, mean team census divided by team patient capacity of 16); (9) average hospitalist team utilization (ie, mean team census divided by team patient capacity of 16); (10) average nursing unit utilization (ie, mean nursing unit census divided by maximum number of patients that can be cared for on each nursing unit); (11) patient dispersion to NUs (ie, average number of NUs on which each general medicine team has patients); 12) estimated average rounding time per general medicine team.

Of note, the average time in the system included time patients spent waiting for bed and team assignments (ED boarding time) in addition to the time they spent in the assigned nursing unit (nursing LOS). The difference between the nursing LOS (ie, time on the nursing unit) and total time in the system is one indicator of system efficiency around hospital admission.

The Institutional Review Board of Virginia Commonwealth University approved this study.

RESULTS

Time and Motion Data

The mean time spent with each patient was nine minutes. The mean time traveling between NUs Healthcare Quality for Children and Adolescents with Suicidality Admitted to Acute Care Hospitals in the United States was five minutes. Average rounding time was noted to be two hours, 53 minutes. Thirty-seven minutes, about ~21% of the time, was wasted in traveling. Each team, on average, traveled to seven different NUs to round on their daily census, averaging 1.6 patients in each nursing unit.

Hospital Data

Between January 1, 2011 to December 31, 2013, a total of 7,902 patients were admitted to the general medicine teams, spanning 23 NU. The average number of admissions per day was 21.6, and the average nursing unit LOS was 133 hours. Average team census was derived from historical data across all GIM team for 2013 and was noted to be 11.5 patients per team, and these patients were spread over seven NU.

 

Model Validation

The mean number of patients admitted to different NUs was estimated from the simulation model then compared with the EHR data from 2013. None were statistically different (P > .05), which signified that the validated simulation model is similar to the EHR data from 2013 despite the underlying assumptions.

Model Outputs

Analysis of the models indicated that steady-state (based upon hospital census) was realized at approximately 800 hours or after 680 patients were admitted to the GIM teams. Statistics collection, therefore, was started after 800 hours of simulated time and reflected the admission of the remaining 7222 patients in the model validation sample (Table 2).

In the model, the total daily general medicine patient census was 119.26. Average time in the system per patient was noted to be 147.37 hours, which was 14.37 hours more than the average nursing unit LOS of 133 hours. Average number of patients waiting for a bed was noted to be 11.31, while the average wait time for a patient to get a bed was 12.39 hours.

Average housestaff team and hospitalist team utilization were 76.06% and 73.02%, respectively, with average team utilization of 74.54% (range: 72.88%-76.19%). Housestaff team and hospitalist team averaged 12.17 and 11.68 patients per care team, respectively. General medicine teams had patients on 7.30 NUs on average. GIM teams rounding travel time was 36.5 minutes.

What-If Scenario Testing

Simulation outputs for the four future states are summarized in Table 2. With Future State 1, through which patients were selectively assigned to housestaff teams aligned with three NUs, the average time in the system per patient increased by 2.35 hours, with 1.87 more patients waiting for a bed and waiting for 2.03 more hours as compared with the present state. A marked disparity was observed in hospitalist and housestaff team utilization of 62.22% and 86.55% respectively. Patient dispersion to various NUs significantly decreased, and rounding time correspondingly decreased by approximately 41%.

Future State 2, adding a nursing unit, decreased average time in the system per patient by 9.86 hours, with 9.32 fewer patients waiting for a bed as compared with the present state. A slight increase in patient dispersion and rounding time was observed. Overall, patients spent 137.51 hours in the system, which demonstrated improved efficiency of the system.

Future State 3, adding an additional medical team, interestingly did not have a significant effect on patients’ average time in system or the number of patients waiting for a bed even though a decrease occurred in average team census, team utilization, and patient dispersion.

Finally, Future State 4, increasing admissions while also adding a nursing unit and a hospitalist team, resulted in an increase in admission volume while maintaining similar utilization rates for teams and NU. Patients spent about 2.48 hours less in the system, while only 9.94 patients were noted to be waiting for a bed as compared with 11.21 patients in the present state model. The total daily general medicine patient census was noted to be 137.19. Average team census and average team utilization were noted to be similar to those of the present state model, while admissions were up by approximately 1,080 per year. Both patient dispersion and rounding were slightly worsened.

 

Sensitivity Analysis

Overall, average time in system was most affected by the number of patient arrivals. This became particularly significant as the volume of patient arrivals approached and exceeded the capacity of the rounding teams. Adding a nursing unit had more impact on decreasing average time in the system than adding a medical team or aligning teams with NUs under the conditions defined by the model. However, under different conditions, such as increasing admission volume, the relative benefit of different approaches may vary.

DISCUSSION

Given that hospitals are large, complex systems,² the impact of system-level changes can have unpredictable and potentially deleterious effects. Simulation provides a technique for modeling the impact of changes to understand the ramifications of these interventions more thoroughly.³ In this study, we describe the process of building a simulation model for the admission and discharge of patients from general medicine services in a tertiary care hospital, internally validating this model, and examining the outcomes from several potential changes to the system.

The outcomes for these what-if scenarios provided some important insights about the secondary effect of system changes and the need for multiple, simultaneous interventions. Given that hospitals often function at near capacity, adding a hospitalist team or nursing unit might be seen as a reasonable strategy to improve the system metrics, number of patient discharges, or average LOS. On the basis of our analysis, adding a nursing unit would have more benefit than adding a hospitalist team. Leaders who want to increase capacity may need to consider both adding a hospitalist team and a nursing unit, and model the impact of each choice as described with a simulation.

Additionally, assigning patients to medical teams aligned with NUs seems theoretically appealing to improve interprofessional communication and decrease the time spent in transit between patients by physicians. While our findings supported a decrease in rounding time and patient dispersion, the teams not aligned with a nursing unit (ie, the hospitalists) exceeded 80% utilization, the threshold at which efficiency is known to decrease.²⁴ Potentially, benefits resulting from teams being aligned with NUs were offset by decrements in performance of the teams not aligned with NU. If medical teams and NUs become aligned, then a higher number of teams may be necessary to maintain patient throughput.

Simulation models identify these unexpected consequences prior to investing resources in a significant change; however, modeling is not simple. Simulation models depend on the characteristics of the model and the quality of the input data. For example, we used an expert approach to map physician workflow as an underpinning of the model, but we may have missed an important variation in physician workflow. Understanding this variation could strengthen the model and provide some testable variables for future study. Likewise, understanding nursing workflow and how variation in physician workflow shapes nursing workflow, and vice versa, is worth exploring.

Other data could also be added to, and help interpret, the outputs of this model. For example, the impact of various levels of team and unit utilization on diversion time for the hospital ED may help determine whether adding team capacity or unit capacity is more beneficial for the system. Likewise, aligning medical teams with NUs seems to hinder patient throughput on this analysis, but benefits in patient satisfaction or decreased readmissions might improve reimbursement and outweigh the revenue lost from throughput. Underpinning each of these types of decisions is a need to model the system well and thoughtfully choose the inputs, processes, and outputs. Pursuing a new strategic decision usually involves cost; simulation modeling provides data to help leaders weigh the benefits in terms of the needed investment.

The major limitations of the study stem from these choices. Our study focused on matching capacity and demand while limiting other changes in the system, such as changes in nursing unit LOS. Future work to quantify the relationship of other variables on parameters, such as the impact of decreased team dispersion on LOS, early discharges, and decreasing care variation, would make future models more robust. This model does not consider other strategies to improve patient flow, such as shaping demand, adaptive team assignment algorithms, or creating surge capacity. We also used only hospitalist time and motion data in our model; housestaff workflow is likely different. In addition, we modeled all patients as having a general level of nursing care and did not account for admissions or transfers to intensive care units or other services. These parameters could be added in future iterations. Finally, the biggest limitation in any simulation is the underlying assumptions made to construct the model. While we validated the model retrospectively, prospective validation and refinement should also be performed with attention to how the model functions under extreme conditions, such as a very high patient load.

 

CONCLUSION

Major system changes are expensive and must be made carefully. Systems engineering techniques, such as DES, provide techniques to estimate the impact of changes on pertinent care delivery variables. Results from this study underscore the complexity of patient care delivery and how simulation models can integrate multiple system components to provide a data-driven approach to inform decision making in a complex system.

Acknowledgments

The simulation software used in this study was awarded as an educational software grant from SIMIO®. We would like to acknowledge support from the Department of Internal Medicine at Virginia Commonwealth University for this project and thank Lena Rivera for her assistance with the manuscript preparation.

Dislosures

Dr. Heim recived a consulting fee for programming guidance from Virginia Commonwealth University. All other authors have nothing to disclose.

Hospitals are complex adaptive systems within which practitioners, technology, physical resources, and other components adapt interdependently to attempt to best meet the needs of patients.¹ Hospitals must provide a stable, dependable level of care while also surging to respond to times of high demand, such as patient emergencies or swells in patient volume. Given the critical and resource-intensive nature of this work, optimizing the system is essential; however, because of the complexity of the system, making changes can result in unexpected and possibly deleterious effects. We need to approach change in hospital processes carefully and thoughtfully.

The Institute of Medicine, the National Academy of Engineering, and the President’s Council of Advisors on Science and Technology have recommended the application of systems engineering approaches to improve health care delivery.^2,3 Systems engineering seeks to coordinate, synchronize, and integrate complex systems of people, information, materials, technology, and financial resources.^4,5 To determine how complex systems can be improved, engineers apply analytic methods to describe how such systems operate and what the impact of changes might be. These methodologies have improved patient care and reduced costs at several hospitals.⁶ For example, a decision support system that combined simulation, optimization, and machine learning methods in an emergency department (ED) resulted in a 33% reduction in length of stay (LOS) and a 28% decrease in ED readmissions.⁷ Other strategies to improve patient flow include shaping demand (decreasing variation in surgical scheduling, relocating low acuity care ED visit to primary care, etc.), redesigning systems (early discharges, improving efficiency, and coordination of hospital discharge process, decreasing care variation, etc.), or aligning capacity and demand. Another approach, real-time demand capacity (RTDC), is based on management principles and queuing and constraint theory and has been implemented successfully in a variety of health care organizations. RTDC represents a promising approach to improve hospitalwide patient flow and can be integrated into current bed management processes.⁸ Unfortunately, many of these approaches are not well known to clinicians and would benefit from greater awareness and input from healthcare practitioners.

One systems engineering tool that can be used to describe, analyze, and evaluate proposed changes in care is simulation.⁹ Simulation creates a model within which what-if scenarios (ie, adjusting various inputs into the simulation) allow researchers to define the likelihood of consequences from various courses of action and determine the optimal change to a system. Such analyses can predict the impact of a proposed change on patients and healthcare practitioners.^10-13

A critical concern for hospitals that simulation may help address is managing the volume of inpatients. A high inpatient census is necessary for financial solvency, yet too high a census of inpatients or an unexpected surge in acuity can overwhelm hospital resources. Many hospitals, pressured by growing numbers of increasingly complex patients, have seen medical inpatients spread across multiple nonmedical nursing units (NUs) of their institution such that a particular medical team may have only a couple patients assigned to each nursing unit.¹⁴ This dispersion may hinder communication between physicians and nurses and limits the time physicians have to interact with patients.¹⁵ Additionally, coordination of care may become more challenging for discharge planning.¹⁶ Aligning medical teams with NUs may benefit the quality and efficiency of care or may create a barrier to patient flow, which worsens these problems.^15,17 Alternatively, hospitals might meet the increasing demands for care by choosing to add capacity by opening new NUs or hiring additional healthcare providers. We identified no studies in the literature that applied simulation modeling to general medicine inpatients to evaluate the impact of these different decisions.

This article describes the application of simulation to model the interconnected variables and subsequent future states created by several possible strategic decisions around the care of general medicine inpatients. Through the application of systems engineering techniques, we modeled four future states that illustrate the following: (1) the complexities of a large health delivery system, (2) the intended and unintended consequences of implementing different changes in the process of care delivery, and (3) how the simulation modeling might be used to inform decision making.

 

METHODS

Setting and Present State

Virginia Commonwealth University (VCU) is a 865-bed tertiary academic medical center, with inpatient care activities spread between four connected buildings and 50 different NUs. The occupancy rate had been over 92% during the time period of this project with admission volume limited primarily by the capacity of the facility. Three of the NUs were primarily allocated to general medicine (GIM) patients. However, over the years, GIM inpatients grew to over 7500 admissions annually, resulting in nearly 50% of GIM patients being admitted to a non-GIM nursing unit.

Additionally, patients on each medical team had a high degree of spread across NUs due to several factors. Admissions and discharges from the hospital did not align across the day. While discharges clumped in the late afternoon, admission occurred throughout the day with a surge in the later afternoon. This mismatch frequently led to patients waiting in the ED for a bed, medical team, or both, and patients were typically assigned to the first available bed and team. For medical team assignments, newly admitted patients were distributed relatively equally across five hospitalist teams and five housestaff teams (that include residents, interns, and medical students). This steady distribution of patients through the day supported meeting housestaff work-hour restrictions of 80 hours each week.¹⁸ Yet, as a result of the high occupancy rate, the patterns of patient admissions and discharges, and the distribution of patients among medical teams and across NUs, medical teams and NUs rarely shared more than a few patients.

Leaders at our institution outlined several possible options to address these challenges, including aligning medical teams with NU, adding an additional hospitalist team, or adding an additional nursing unit. In addition, institutional leaders were concerned about the impact of continued growth in admission volume and the impact of patient dispersion on trainees and students. The overall goal of creating a simulation model was to determine the impact of an increased volume of patients and these possible strategic decisions on operational metrics, including number of patients waiting in the ED, ED boarding time per patient, time in system per patient (ED boarding time plus inpatient LOS), team utilization, and rounding travel time.

Simulation Modeling

To model the impact of some possible system changes on patient care, we applied Kelton and Law’s simulation study framework,¹⁹ including data collection; model building and validation; and what-if scenario testing (Figure 1).

Data Collection

Process Flow Map

We created a complex process flow map of patient care activities on medical teams. The map was developed by four general medicine physicians (R.C., H.M., V.M., and S.P.T.) who all provided medical care on the hospital-based services and ensured expert input on the patient care activities captured by the simulation modeling.

Time and Motion Studies

Time and motion study is a well-established technique used to evaluate the efficiency of work processes.^20,21 Originally applied to increase productivity in manufacturing, this technique uses first-hand observations to measure the time allotted to different work tasks to systematically analyze workflow.²² Workflow in healthcare, like manufacturing tasks, tends to have a repetitive pattern, making time and motion studies a highly applicable tool.

 

A research assistant observed a total of 30 hospitalist work cycles to describe the work of our inpatient clinicians. A work cycle, defined as one complete process flow,²³ began when the hospitalist started a daytime shift of patient care and concluded after the physician “signed out” to the physician who was assuming responsibility for ongoing medical care of the patients (ie, cross-coverage). Time spent on different activities identified by the process flow map was captured throughout the cycle. These activities included time spent traveling to evaluate patients located on different NUs. To minimize disruptions in patient care and adhere to privacy standards, no observations were conducted in patient rooms, and details of computer work were not recorded. To ensure stable estimates of the mean and standard deviation of the time spent at each step, at least 30 cycles of observation are recommended. Thus, 300 hours of observations over the course of 30 separate days were collected.

Hospital Data

We extracted admission and discharge data from the electronic health records (EHR) for general medicine patients admitted from the ED for the calendar year 2013. These records were used to establish means and standard deviations for admission date and time, distribution of patients across NUs, and LOS.

Model Building and Internal Validation

On the basis of these data inputs and using SIMIO® Simulation Software version 7, we constructed a discrete event simulation (DES) model representing the patient care activities of general medicine teams. Each patient was assigned a bed on a nursing unit through a probability distribution based on prior EHR data and then randomly assigned to a general medicine team. We replicated the model 200 times, and each model ran for 365 days. Each team was limited to 16 assigned patients, the maximum number of patients per housestaff team allowed by VCU protocol; henceforth, this number is referred to as team-patient capacity. The model assumed patients remained on the assigned nursing unit and medical team for the entirety of their hospital stay and that each patient was seen by their assigned medical team every day. The results of the present state model, including mean number of patients on each nursing unit, mean team census, patient dispersion (ie, the number of NUs on which each medical team had patients), and team utilization (ie, mean team census divided by team patient capacity), were compared with actual data from 2013 to internally validate the model.

What-If Scenario Testing

We constructed four what-if scenarios based on possible strategic directions identified by leadership. These models evaluated:

• constraining patients on housestaff (but not hospitalist) teams to the three general medicine NUs (Future State 1),
• increasing bed capacity for general medicine patients by adding one additional nursing unit of 26 beds (Future State 2),
• increasing the number of general medicine teams by adding one additional hospitalist team of up to 16 patients (Future State 3),
• modeling the impact of increased patient admissions from 21 per day to 25 per day while also adding a nursing unit and an additional medical team (Future State 4).

 

For Future States 1-3, admission volume was held constant. The model generated nursing unit LOS using a random continuous exponential probability distribution with a mean of 133 hours to match the LOS distribution derived from health system data. As patients entered the system for admission, the model assigned a bed to the patient, but the patient could not move to the assigned bed until a bed and care team were both available. We were only interested in the steady-state behavior of the system, so collecting performance statistics only after the model had been populated and steady state had been achieved was important.

Table 1 summarizes the input data, fixed, and dynamic variable for each future state model.

We examined the impact of these scenarios on the following variables (Table 2): (1) average time in system; (2) average number of patients waiting for a bed; (3) average ED boarding time; (4) total daily general medicine census; (5) average housestaff team census per team; (6) average hospitalist team census per team; (7) average combined housestaff and hospitalist team census per team; (8) average housestaff team utilization (ie, mean team census divided by team patient capacity of 16); (9) average hospitalist team utilization (ie, mean team census divided by team patient capacity of 16); (10) average nursing unit utilization (ie, mean nursing unit census divided by maximum number of patients that can be cared for on each nursing unit); (11) patient dispersion to NUs (ie, average number of NUs on which each general medicine team has patients); 12) estimated average rounding time per general medicine team.

Of note, the average time in the system included time patients spent waiting for bed and team assignments (ED boarding time) in addition to the time they spent in the assigned nursing unit (nursing LOS). The difference between the nursing LOS (ie, time on the nursing unit) and total time in the system is one indicator of system efficiency around hospital admission.

The Institutional Review Board of Virginia Commonwealth University approved this study.

RESULTS

Time and Motion Data

The mean time spent with each patient was nine minutes. The mean time traveling between NUs Healthcare Quality for Children and Adolescents with Suicidality Admitted to Acute Care Hospitals in the United States was five minutes. Average rounding time was noted to be two hours, 53 minutes. Thirty-seven minutes, about ~21% of the time, was wasted in traveling. Each team, on average, traveled to seven different NUs to round on their daily census, averaging 1.6 patients in each nursing unit.

Hospital Data

Between January 1, 2011 to December 31, 2013, a total of 7,902 patients were admitted to the general medicine teams, spanning 23 NU. The average number of admissions per day was 21.6, and the average nursing unit LOS was 133 hours. Average team census was derived from historical data across all GIM team for 2013 and was noted to be 11.5 patients per team, and these patients were spread over seven NU.

 

Model Validation

The mean number of patients admitted to different NUs was estimated from the simulation model then compared with the EHR data from 2013. None were statistically different (P > .05), which signified that the validated simulation model is similar to the EHR data from 2013 despite the underlying assumptions.

Model Outputs

Analysis of the models indicated that steady-state (based upon hospital census) was realized at approximately 800 hours or after 680 patients were admitted to the GIM teams. Statistics collection, therefore, was started after 800 hours of simulated time and reflected the admission of the remaining 7222 patients in the model validation sample (Table 2).

In the model, the total daily general medicine patient census was 119.26. Average time in the system per patient was noted to be 147.37 hours, which was 14.37 hours more than the average nursing unit LOS of 133 hours. Average number of patients waiting for a bed was noted to be 11.31, while the average wait time for a patient to get a bed was 12.39 hours.

Average housestaff team and hospitalist team utilization were 76.06% and 73.02%, respectively, with average team utilization of 74.54% (range: 72.88%-76.19%). Housestaff team and hospitalist team averaged 12.17 and 11.68 patients per care team, respectively. General medicine teams had patients on 7.30 NUs on average. GIM teams rounding travel time was 36.5 minutes.

What-If Scenario Testing

Simulation outputs for the four future states are summarized in Table 2. With Future State 1, through which patients were selectively assigned to housestaff teams aligned with three NUs, the average time in the system per patient increased by 2.35 hours, with 1.87 more patients waiting for a bed and waiting for 2.03 more hours as compared with the present state. A marked disparity was observed in hospitalist and housestaff team utilization of 62.22% and 86.55% respectively. Patient dispersion to various NUs significantly decreased, and rounding time correspondingly decreased by approximately 41%.

Future State 2, adding a nursing unit, decreased average time in the system per patient by 9.86 hours, with 9.32 fewer patients waiting for a bed as compared with the present state. A slight increase in patient dispersion and rounding time was observed. Overall, patients spent 137.51 hours in the system, which demonstrated improved efficiency of the system.

Future State 3, adding an additional medical team, interestingly did not have a significant effect on patients’ average time in system or the number of patients waiting for a bed even though a decrease occurred in average team census, team utilization, and patient dispersion.

Finally, Future State 4, increasing admissions while also adding a nursing unit and a hospitalist team, resulted in an increase in admission volume while maintaining similar utilization rates for teams and NU. Patients spent about 2.48 hours less in the system, while only 9.94 patients were noted to be waiting for a bed as compared with 11.21 patients in the present state model. The total daily general medicine patient census was noted to be 137.19. Average team census and average team utilization were noted to be similar to those of the present state model, while admissions were up by approximately 1,080 per year. Both patient dispersion and rounding were slightly worsened.

 

Sensitivity Analysis

Overall, average time in system was most affected by the number of patient arrivals. This became particularly significant as the volume of patient arrivals approached and exceeded the capacity of the rounding teams. Adding a nursing unit had more impact on decreasing average time in the system than adding a medical team or aligning teams with NUs under the conditions defined by the model. However, under different conditions, such as increasing admission volume, the relative benefit of different approaches may vary.

DISCUSSION

Given that hospitals are large, complex systems,² the impact of system-level changes can have unpredictable and potentially deleterious effects. Simulation provides a technique for modeling the impact of changes to understand the ramifications of these interventions more thoroughly.³ In this study, we describe the process of building a simulation model for the admission and discharge of patients from general medicine services in a tertiary care hospital, internally validating this model, and examining the outcomes from several potential changes to the system.

The outcomes for these what-if scenarios provided some important insights about the secondary effect of system changes and the need for multiple, simultaneous interventions. Given that hospitals often function at near capacity, adding a hospitalist team or nursing unit might be seen as a reasonable strategy to improve the system metrics, number of patient discharges, or average LOS. On the basis of our analysis, adding a nursing unit would have more benefit than adding a hospitalist team. Leaders who want to increase capacity may need to consider both adding a hospitalist team and a nursing unit, and model the impact of each choice as described with a simulation.

Additionally, assigning patients to medical teams aligned with NUs seems theoretically appealing to improve interprofessional communication and decrease the time spent in transit between patients by physicians. While our findings supported a decrease in rounding time and patient dispersion, the teams not aligned with a nursing unit (ie, the hospitalists) exceeded 80% utilization, the threshold at which efficiency is known to decrease.²⁴ Potentially, benefits resulting from teams being aligned with NUs were offset by decrements in performance of the teams not aligned with NU. If medical teams and NUs become aligned, then a higher number of teams may be necessary to maintain patient throughput.

Simulation models identify these unexpected consequences prior to investing resources in a significant change; however, modeling is not simple. Simulation models depend on the characteristics of the model and the quality of the input data. For example, we used an expert approach to map physician workflow as an underpinning of the model, but we may have missed an important variation in physician workflow. Understanding this variation could strengthen the model and provide some testable variables for future study. Likewise, understanding nursing workflow and how variation in physician workflow shapes nursing workflow, and vice versa, is worth exploring.

Other data could also be added to, and help interpret, the outputs of this model. For example, the impact of various levels of team and unit utilization on diversion time for the hospital ED may help determine whether adding team capacity or unit capacity is more beneficial for the system. Likewise, aligning medical teams with NUs seems to hinder patient throughput on this analysis, but benefits in patient satisfaction or decreased readmissions might improve reimbursement and outweigh the revenue lost from throughput. Underpinning each of these types of decisions is a need to model the system well and thoughtfully choose the inputs, processes, and outputs. Pursuing a new strategic decision usually involves cost; simulation modeling provides data to help leaders weigh the benefits in terms of the needed investment.

The major limitations of the study stem from these choices. Our study focused on matching capacity and demand while limiting other changes in the system, such as changes in nursing unit LOS. Future work to quantify the relationship of other variables on parameters, such as the impact of decreased team dispersion on LOS, early discharges, and decreasing care variation, would make future models more robust. This model does not consider other strategies to improve patient flow, such as shaping demand, adaptive team assignment algorithms, or creating surge capacity. We also used only hospitalist time and motion data in our model; housestaff workflow is likely different. In addition, we modeled all patients as having a general level of nursing care and did not account for admissions or transfers to intensive care units or other services. These parameters could be added in future iterations. Finally, the biggest limitation in any simulation is the underlying assumptions made to construct the model. While we validated the model retrospectively, prospective validation and refinement should also be performed with attention to how the model functions under extreme conditions, such as a very high patient load.

 

CONCLUSION

Major system changes are expensive and must be made carefully. Systems engineering techniques, such as DES, provide techniques to estimate the impact of changes on pertinent care delivery variables. Results from this study underscore the complexity of patient care delivery and how simulation models can integrate multiple system components to provide a data-driven approach to inform decision making in a complex system.

Acknowledgments

The simulation software used in this study was awarded as an educational software grant from SIMIO®. We would like to acknowledge support from the Department of Internal Medicine at Virginia Commonwealth University for this project and thank Lena Rivera for her assistance with the manuscript preparation.

Dislosures

Dr. Heim recived a consulting fee for programming guidance from Virginia Commonwealth University. All other authors have nothing to disclose.

Hospitals are complex adaptive systems within which practitioners, technology, physical resources, and other components adapt interdependently to attempt to best meet the needs of patients.¹ Hospitals must provide a stable, dependable level of care while also surging to respond to times of high demand, such as patient emergencies or swells in patient volume. Given the critical and resource-intensive nature of this work, optimizing the system is essential; however, because of the complexity of the system, making changes can result in unexpected and possibly deleterious effects. We need to approach change in hospital processes carefully and thoughtfully.

The Institute of Medicine, the National Academy of Engineering, and the President’s Council of Advisors on Science and Technology have recommended the application of systems engineering approaches to improve health care delivery.^2,3 Systems engineering seeks to coordinate, synchronize, and integrate complex systems of people, information, materials, technology, and financial resources.^4,5 To determine how complex systems can be improved, engineers apply analytic methods to describe how such systems operate and what the impact of changes might be. These methodologies have improved patient care and reduced costs at several hospitals.⁶ For example, a decision support system that combined simulation, optimization, and machine learning methods in an emergency department (ED) resulted in a 33% reduction in length of stay (LOS) and a 28% decrease in ED readmissions.⁷ Other strategies to improve patient flow include shaping demand (decreasing variation in surgical scheduling, relocating low acuity care ED visit to primary care, etc.), redesigning systems (early discharges, improving efficiency, and coordination of hospital discharge process, decreasing care variation, etc.), or aligning capacity and demand. Another approach, real-time demand capacity (RTDC), is based on management principles and queuing and constraint theory and has been implemented successfully in a variety of health care organizations. RTDC represents a promising approach to improve hospitalwide patient flow and can be integrated into current bed management processes.⁸ Unfortunately, many of these approaches are not well known to clinicians and would benefit from greater awareness and input from healthcare practitioners.

One systems engineering tool that can be used to describe, analyze, and evaluate proposed changes in care is simulation.⁹ Simulation creates a model within which what-if scenarios (ie, adjusting various inputs into the simulation) allow researchers to define the likelihood of consequences from various courses of action and determine the optimal change to a system. Such analyses can predict the impact of a proposed change on patients and healthcare practitioners.^10-13

A critical concern for hospitals that simulation may help address is managing the volume of inpatients. A high inpatient census is necessary for financial solvency, yet too high a census of inpatients or an unexpected surge in acuity can overwhelm hospital resources. Many hospitals, pressured by growing numbers of increasingly complex patients, have seen medical inpatients spread across multiple nonmedical nursing units (NUs) of their institution such that a particular medical team may have only a couple patients assigned to each nursing unit.¹⁴ This dispersion may hinder communication between physicians and nurses and limits the time physicians have to interact with patients.¹⁵ Additionally, coordination of care may become more challenging for discharge planning.¹⁶ Aligning medical teams with NUs may benefit the quality and efficiency of care or may create a barrier to patient flow, which worsens these problems.^15,17 Alternatively, hospitals might meet the increasing demands for care by choosing to add capacity by opening new NUs or hiring additional healthcare providers. We identified no studies in the literature that applied simulation modeling to general medicine inpatients to evaluate the impact of these different decisions.

This article describes the application of simulation to model the interconnected variables and subsequent future states created by several possible strategic decisions around the care of general medicine inpatients. Through the application of systems engineering techniques, we modeled four future states that illustrate the following: (1) the complexities of a large health delivery system, (2) the intended and unintended consequences of implementing different changes in the process of care delivery, and (3) how the simulation modeling might be used to inform decision making.

 

METHODS

Setting and Present State

Virginia Commonwealth University (VCU) is a 865-bed tertiary academic medical center, with inpatient care activities spread between four connected buildings and 50 different NUs. The occupancy rate had been over 92% during the time period of this project with admission volume limited primarily by the capacity of the facility. Three of the NUs were primarily allocated to general medicine (GIM) patients. However, over the years, GIM inpatients grew to over 7500 admissions annually, resulting in nearly 50% of GIM patients being admitted to a non-GIM nursing unit.

Additionally, patients on each medical team had a high degree of spread across NUs due to several factors. Admissions and discharges from the hospital did not align across the day. While discharges clumped in the late afternoon, admission occurred throughout the day with a surge in the later afternoon. This mismatch frequently led to patients waiting in the ED for a bed, medical team, or both, and patients were typically assigned to the first available bed and team. For medical team assignments, newly admitted patients were distributed relatively equally across five hospitalist teams and five housestaff teams (that include residents, interns, and medical students). This steady distribution of patients through the day supported meeting housestaff work-hour restrictions of 80 hours each week.¹⁸ Yet, as a result of the high occupancy rate, the patterns of patient admissions and discharges, and the distribution of patients among medical teams and across NUs, medical teams and NUs rarely shared more than a few patients.

Leaders at our institution outlined several possible options to address these challenges, including aligning medical teams with NU, adding an additional hospitalist team, or adding an additional nursing unit. In addition, institutional leaders were concerned about the impact of continued growth in admission volume and the impact of patient dispersion on trainees and students. The overall goal of creating a simulation model was to determine the impact of an increased volume of patients and these possible strategic decisions on operational metrics, including number of patients waiting in the ED, ED boarding time per patient, time in system per patient (ED boarding time plus inpatient LOS), team utilization, and rounding travel time.

Simulation Modeling

To model the impact of some possible system changes on patient care, we applied Kelton and Law’s simulation study framework,¹⁹ including data collection; model building and validation; and what-if scenario testing (Figure 1).

Data Collection

Process Flow Map

We created a complex process flow map of patient care activities on medical teams. The map was developed by four general medicine physicians (R.C., H.M., V.M., and S.P.T.) who all provided medical care on the hospital-based services and ensured expert input on the patient care activities captured by the simulation modeling.

Time and Motion Studies

Time and motion study is a well-established technique used to evaluate the efficiency of work processes.^20,21 Originally applied to increase productivity in manufacturing, this technique uses first-hand observations to measure the time allotted to different work tasks to systematically analyze workflow.²² Workflow in healthcare, like manufacturing tasks, tends to have a repetitive pattern, making time and motion studies a highly applicable tool.

 

A research assistant observed a total of 30 hospitalist work cycles to describe the work of our inpatient clinicians. A work cycle, defined as one complete process flow,²³ began when the hospitalist started a daytime shift of patient care and concluded after the physician “signed out” to the physician who was assuming responsibility for ongoing medical care of the patients (ie, cross-coverage). Time spent on different activities identified by the process flow map was captured throughout the cycle. These activities included time spent traveling to evaluate patients located on different NUs. To minimize disruptions in patient care and adhere to privacy standards, no observations were conducted in patient rooms, and details of computer work were not recorded. To ensure stable estimates of the mean and standard deviation of the time spent at each step, at least 30 cycles of observation are recommended. Thus, 300 hours of observations over the course of 30 separate days were collected.

Hospital Data

We extracted admission and discharge data from the electronic health records (EHR) for general medicine patients admitted from the ED for the calendar year 2013. These records were used to establish means and standard deviations for admission date and time, distribution of patients across NUs, and LOS.

Model Building and Internal Validation

On the basis of these data inputs and using SIMIO® Simulation Software version 7, we constructed a discrete event simulation (DES) model representing the patient care activities of general medicine teams. Each patient was assigned a bed on a nursing unit through a probability distribution based on prior EHR data and then randomly assigned to a general medicine team. We replicated the model 200 times, and each model ran for 365 days. Each team was limited to 16 assigned patients, the maximum number of patients per housestaff team allowed by VCU protocol; henceforth, this number is referred to as team-patient capacity. The model assumed patients remained on the assigned nursing unit and medical team for the entirety of their hospital stay and that each patient was seen by their assigned medical team every day. The results of the present state model, including mean number of patients on each nursing unit, mean team census, patient dispersion (ie, the number of NUs on which each medical team had patients), and team utilization (ie, mean team census divided by team patient capacity), were compared with actual data from 2013 to internally validate the model.

What-If Scenario Testing

We constructed four what-if scenarios based on possible strategic directions identified by leadership. These models evaluated:

• constraining patients on housestaff (but not hospitalist) teams to the three general medicine NUs (Future State 1),
• increasing bed capacity for general medicine patients by adding one additional nursing unit of 26 beds (Future State 2),
• increasing the number of general medicine teams by adding one additional hospitalist team of up to 16 patients (Future State 3),
• modeling the impact of increased patient admissions from 21 per day to 25 per day while also adding a nursing unit and an additional medical team (Future State 4).

 

For Future States 1-3, admission volume was held constant. The model generated nursing unit LOS using a random continuous exponential probability distribution with a mean of 133 hours to match the LOS distribution derived from health system data. As patients entered the system for admission, the model assigned a bed to the patient, but the patient could not move to the assigned bed until a bed and care team were both available. We were only interested in the steady-state behavior of the system, so collecting performance statistics only after the model had been populated and steady state had been achieved was important.

Table 1 summarizes the input data, fixed, and dynamic variable for each future state model.

We examined the impact of these scenarios on the following variables (Table 2): (1) average time in system; (2) average number of patients waiting for a bed; (3) average ED boarding time; (4) total daily general medicine census; (5) average housestaff team census per team; (6) average hospitalist team census per team; (7) average combined housestaff and hospitalist team census per team; (8) average housestaff team utilization (ie, mean team census divided by team patient capacity of 16); (9) average hospitalist team utilization (ie, mean team census divided by team patient capacity of 16); (10) average nursing unit utilization (ie, mean nursing unit census divided by maximum number of patients that can be cared for on each nursing unit); (11) patient dispersion to NUs (ie, average number of NUs on which each general medicine team has patients); 12) estimated average rounding time per general medicine team.

Of note, the average time in the system included time patients spent waiting for bed and team assignments (ED boarding time) in addition to the time they spent in the assigned nursing unit (nursing LOS). The difference between the nursing LOS (ie, time on the nursing unit) and total time in the system is one indicator of system efficiency around hospital admission.

The Institutional Review Board of Virginia Commonwealth University approved this study.

RESULTS

Time and Motion Data

The mean time spent with each patient was nine minutes. The mean time traveling between NUs Healthcare Quality for Children and Adolescents with Suicidality Admitted to Acute Care Hospitals in the United States was five minutes. Average rounding time was noted to be two hours, 53 minutes. Thirty-seven minutes, about ~21% of the time, was wasted in traveling. Each team, on average, traveled to seven different NUs to round on their daily census, averaging 1.6 patients in each nursing unit.

Hospital Data

Between January 1, 2011 to December 31, 2013, a total of 7,902 patients were admitted to the general medicine teams, spanning 23 NU. The average number of admissions per day was 21.6, and the average nursing unit LOS was 133 hours. Average team census was derived from historical data across all GIM team for 2013 and was noted to be 11.5 patients per team, and these patients were spread over seven NU.

 

Model Validation

The mean number of patients admitted to different NUs was estimated from the simulation model then compared with the EHR data from 2013. None were statistically different (P > .05), which signified that the validated simulation model is similar to the EHR data from 2013 despite the underlying assumptions.

Model Outputs

Analysis of the models indicated that steady-state (based upon hospital census) was realized at approximately 800 hours or after 680 patients were admitted to the GIM teams. Statistics collection, therefore, was started after 800 hours of simulated time and reflected the admission of the remaining 7222 patients in the model validation sample (Table 2).

In the model, the total daily general medicine patient census was 119.26. Average time in the system per patient was noted to be 147.37 hours, which was 14.37 hours more than the average nursing unit LOS of 133 hours. Average number of patients waiting for a bed was noted to be 11.31, while the average wait time for a patient to get a bed was 12.39 hours.

Average housestaff team and hospitalist team utilization were 76.06% and 73.02%, respectively, with average team utilization of 74.54% (range: 72.88%-76.19%). Housestaff team and hospitalist team averaged 12.17 and 11.68 patients per care team, respectively. General medicine teams had patients on 7.30 NUs on average. GIM teams rounding travel time was 36.5 minutes.

What-If Scenario Testing

Simulation outputs for the four future states are summarized in Table 2. With Future State 1, through which patients were selectively assigned to housestaff teams aligned with three NUs, the average time in the system per patient increased by 2.35 hours, with 1.87 more patients waiting for a bed and waiting for 2.03 more hours as compared with the present state. A marked disparity was observed in hospitalist and housestaff team utilization of 62.22% and 86.55% respectively. Patient dispersion to various NUs significantly decreased, and rounding time correspondingly decreased by approximately 41%.

Future State 2, adding a nursing unit, decreased average time in the system per patient by 9.86 hours, with 9.32 fewer patients waiting for a bed as compared with the present state. A slight increase in patient dispersion and rounding time was observed. Overall, patients spent 137.51 hours in the system, which demonstrated improved efficiency of the system.

Future State 3, adding an additional medical team, interestingly did not have a significant effect on patients’ average time in system or the number of patients waiting for a bed even though a decrease occurred in average team census, team utilization, and patient dispersion.

Finally, Future State 4, increasing admissions while also adding a nursing unit and a hospitalist team, resulted in an increase in admission volume while maintaining similar utilization rates for teams and NU. Patients spent about 2.48 hours less in the system, while only 9.94 patients were noted to be waiting for a bed as compared with 11.21 patients in the present state model. The total daily general medicine patient census was noted to be 137.19. Average team census and average team utilization were noted to be similar to those of the present state model, while admissions were up by approximately 1,080 per year. Both patient dispersion and rounding were slightly worsened.

 

Sensitivity Analysis

Overall, average time in system was most affected by the number of patient arrivals. This became particularly significant as the volume of patient arrivals approached and exceeded the capacity of the rounding teams. Adding a nursing unit had more impact on decreasing average time in the system than adding a medical team or aligning teams with NUs under the conditions defined by the model. However, under different conditions, such as increasing admission volume, the relative benefit of different approaches may vary.

DISCUSSION

Given that hospitals are large, complex systems,² the impact of system-level changes can have unpredictable and potentially deleterious effects. Simulation provides a technique for modeling the impact of changes to understand the ramifications of these interventions more thoroughly.³ In this study, we describe the process of building a simulation model for the admission and discharge of patients from general medicine services in a tertiary care hospital, internally validating this model, and examining the outcomes from several potential changes to the system.

The outcomes for these what-if scenarios provided some important insights about the secondary effect of system changes and the need for multiple, simultaneous interventions. Given that hospitals often function at near capacity, adding a hospitalist team or nursing unit might be seen as a reasonable strategy to improve the system metrics, number of patient discharges, or average LOS. On the basis of our analysis, adding a nursing unit would have more benefit than adding a hospitalist team. Leaders who want to increase capacity may need to consider both adding a hospitalist team and a nursing unit, and model the impact of each choice as described with a simulation.

Additionally, assigning patients to medical teams aligned with NUs seems theoretically appealing to improve interprofessional communication and decrease the time spent in transit between patients by physicians. While our findings supported a decrease in rounding time and patient dispersion, the teams not aligned with a nursing unit (ie, the hospitalists) exceeded 80% utilization, the threshold at which efficiency is known to decrease.²⁴ Potentially, benefits resulting from teams being aligned with NUs were offset by decrements in performance of the teams not aligned with NU. If medical teams and NUs become aligned, then a higher number of teams may be necessary to maintain patient throughput.

Simulation models identify these unexpected consequences prior to investing resources in a significant change; however, modeling is not simple. Simulation models depend on the characteristics of the model and the quality of the input data. For example, we used an expert approach to map physician workflow as an underpinning of the model, but we may have missed an important variation in physician workflow. Understanding this variation could strengthen the model and provide some testable variables for future study. Likewise, understanding nursing workflow and how variation in physician workflow shapes nursing workflow, and vice versa, is worth exploring.

Other data could also be added to, and help interpret, the outputs of this model. For example, the impact of various levels of team and unit utilization on diversion time for the hospital ED may help determine whether adding team capacity or unit capacity is more beneficial for the system. Likewise, aligning medical teams with NUs seems to hinder patient throughput on this analysis, but benefits in patient satisfaction or decreased readmissions might improve reimbursement and outweigh the revenue lost from throughput. Underpinning each of these types of decisions is a need to model the system well and thoughtfully choose the inputs, processes, and outputs. Pursuing a new strategic decision usually involves cost; simulation modeling provides data to help leaders weigh the benefits in terms of the needed investment.

The major limitations of the study stem from these choices. Our study focused on matching capacity and demand while limiting other changes in the system, such as changes in nursing unit LOS. Future work to quantify the relationship of other variables on parameters, such as the impact of decreased team dispersion on LOS, early discharges, and decreasing care variation, would make future models more robust. This model does not consider other strategies to improve patient flow, such as shaping demand, adaptive team assignment algorithms, or creating surge capacity. We also used only hospitalist time and motion data in our model; housestaff workflow is likely different. In addition, we modeled all patients as having a general level of nursing care and did not account for admissions or transfers to intensive care units or other services. These parameters could be added in future iterations. Finally, the biggest limitation in any simulation is the underlying assumptions made to construct the model. While we validated the model retrospectively, prospective validation and refinement should also be performed with attention to how the model functions under extreme conditions, such as a very high patient load.

 

CONCLUSION

Major system changes are expensive and must be made carefully. Systems engineering techniques, such as DES, provide techniques to estimate the impact of changes on pertinent care delivery variables. Results from this study underscore the complexity of patient care delivery and how simulation models can integrate multiple system components to provide a data-driven approach to inform decision making in a complex system.

Acknowledgments

The simulation software used in this study was awarded as an educational software grant from SIMIO®. We would like to acknowledge support from the Department of Internal Medicine at Virginia Commonwealth University for this project and thank Lena Rivera for her assistance with the manuscript preparation.

Dislosures

Dr. Heim recived a consulting fee for programming guidance from Virginia Commonwealth University. All other authors have nothing to disclose.

ABSTRACT

Systemic corticosteroids are used to treat a number of medical conditions; however, they are associated with numerous adverse effects. The impact of preoperative chronic corticosteroid use on postoperative outcomes following total hip arthroplasty (THA) is unclear. The purpose of this study was to assess the independent effect of chronic systemic preoperative steroid use on short-term perioperative complications and readmissions after THA.

All patients undergoing primary THA in the American College of Surgeons National Surgical Quality Improvement Program registry from 2005 to -–2015 were identified. Patients were considered chronic steroid users if they used any dosage of oral or parenteral steroids for >10 of the preceding 30 days before THA. Two equally sized propensity-matched groups based on preoperative steroid use were generated to account for differences in operative and baseline characteristics between the groups. Thirty-day complications and hospital readmissions rates were compared using bivariate analysis.

Of 101,532 THA patients who underwent primary THA, 3714 (3.7%) were identified as chronic corticosteroid users. Comparison of propensity-matched cohorts identified an increased rate of any complication (odds ratio [OR] 1.30, P = .003), sepsis (OR 2.07, P = .022), urinary tract infection (OR 1.61, P = .020), superficial surgical site infection (OR 1.73, P = .038), and hospital readmission (OR 1.50, P < .001) in patients who used systemic steroids preoperatively. Readmissions in preoperative steroid users were most commonly for infectious reasons.

Patients prescribed chronic corticosteroids are at a significantly increased risk of both 30-day periopative complications and hospital readmissions. This finding has important implications for pre- and postoperative patient counseling as well as preoperative risk stratification.

Continue to: Corticosteroids are powerful...

 

Corticosteroids are powerful anti-inflammatory steroid hormones that have many indications in the treatment of medical diseases, including advanced or poorly controlled asthma, chronic obstructive pulmonary disease (COPD), inflammatory bowel disease, allergic conditions, among other indications.^1-4 In orthopedics and rheumatology, systemic steroids are, at times, used in patients with rheumatoid arthritis, systemic lupus erythematosus, and vasculitides.^5-7 Overman and colleagues,⁸ using data from the National Health and Nutrition Examination Survey between 1999 and 2008 identified both a 1.2% prevalence of chronic corticosteroid usage in the United States across all age groups and a positive correlation between steroid use prevalence and increasing age. In that study, nearly two-thirds of survey respondents reported using corticosteroids chronically for >90 days. Another observational study in the United Kingdom found that long-term steroid prescriptions increased between 1989 to 2008 and that 13.6% of patients with rheumatoid arthritis and 66.5% of patients with polymyalgia rheumatica or giant cell arteritis used long-term steroids.⁹

Enterally- or parenterally-administered corticosteroids have numerous systemic effects that are of particular relevance to orthopedic surgeons. Corticosteroids induce osteoporosis by preferentially inducing osteoclastic activity while inhibiting the differentiation of osteoblasts, ultimately leading to decreased bone quality and mass.¹⁰ As a consequence, patients who have previously used corticosteroids are more than twice as likely to have a hip fracture.¹¹ Steroids also increase the risk of both osteonecrosis and myopathy, among other musculoskeletal effects.¹² In addition to orthopedic complications, steroids have broad inhibitory effects on both acquired and innate immunity, which significantly increases the risk of infections.¹³ This increased risk of infection is dose-dependent¹⁴ and synergistic with other immunosuppressive drugs.¹⁵

Patients with hip pain may receive localized corticosteroid hip joint injections during the nonoperative management of various hip pathologies, including arthritis, bursitis, and labral tears.^16,17 Outcomes of patients who received intra-articular corticosteroid injections before total hip arthroplasty (THA) were evaluated in a systematic review of 9 studies by Pereira and colleagues.¹⁷ These authors found that the infection rate (both superficial and deep surgical site infections [SSI]) after THA in patients who received local steroid injection into the hip before surgery was between 0% and 30%.¹⁷ However, similar studies assessing the impact that systemic steroids have on outcomes after THA are lacking. Patients who undergo THA for conditions associated with higher lifetime steroid usage have worse outcomes than those who do not. For instance, in patients undergoing THA for rheumatoid arthritis, the rates of both postoperative periprosthetic joint infection and hip dislocation are higher, when compared with osteoarthritis.^18,19 However, it is unclear how much of this difference in outcomes is due to the underlying disease, adverse effects of steroids, or both. Given the high prevalence of chronic systemic steroid use, it is essential to elucidate more clearly the impact that these medications have on perioperative outcomes after THA.  

Therefore, the purpose of this study was to characterize short-term perioperative outcomes, including complication and readmission rates in patients undergoing THA while taking chronic preoperative corticosteroids. We also sought to identify the most common reasons for hospital readmission in patients who did and did not use long-term steroids.

MATERIALS AND METHODS

STUDY DESIGN AND SETTING

This investigation was a retrospective cohort study that utilized the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) registry.²⁰ The ACS-NSQIP is a prospectively collected, multi-institutional database that collects demographical information, operative variables, and both postoperative complications and hospital readmission data. Data is collected for up to 30 days after the index procedure, and patients are contacted by telephone if they are discharged before 30 days. Patient data is entered by specially trained surgical clinical reviewers and is routinely audited by the ACS-NSQIP, leading to more accurate data when compared with administrative research databases.^21,22 The ACS-NSQIP has been used in orthopedic surgery outcomes-based studies.^23-25

Continue to: All patients undergoing...

 

All patients undergoing THA between 2005 and 2015 were identified in the registry using primary Current Procedural Terminology code 27130. Patients were split into 2 groups based on whether or not they chronically used corticosteroids preoperatively for a medical condition. A patient was considered a chronic corticosteroid user if he/she used oral or parenteral corticosteroids within 30 days before the index procedure for >10 of the preceding 30 days. Those who received a 1-time steroid pulse or those who used topical or inhaled steroids were not considered as steroid users in this study.

BASELINE CHARACTERISTICS AND PERIOPERATIVE OUTCOMES

Baseline patient and operative characteristics, including patient age, gender, body mass index (BMI), functional status, American Society of Anesthesiologists (ASA) class, anesthesia type, operative duration, and medical comorbidities including hypertension, COPD, diabetes mellitus, and smoking history, were compared between both groups. Perioperative outcomes that were assessed in this study include death, renal, respiratory, and cardiac complications, deep vein thrombosis or pulmonary embolism, stroke, sepsis, return to the operating room, urinary tract infection (UTI), wound dehiscence, superficial and deep SSI, need for a blood transfusion within 72 hours of index surgical procedure, and hospital readmissions. Renal complications were defined as acute or progressive renal insufficiency; respiratory complications were defined as failure to wean from the ventilator, need for intubation after the index procedure, and the occurrence of pneumonia; and cardiac complications were defined as myocardial infarction or cardiac arrest requiring cardiopulmonary resuscitation. Patients were excluded if they had missing baseline or operative characteristic data, an unclean wound classification at the time of admission, or if their THA was considered emergent.

STATISTICAL ANALYSIS

A propensity score-matched comparison was performed to adjust for differences in baseline and operative characteristics between the 2 cohorts in this study. In the current study, the propensity score was defined as the conditional probability that a patient chronically used preoperative corticosteroids for a medical condition, as a function of age, BMI, gender, ASA class, functional status, medical comorbidities, anesthesia type, and operative duration. A 1:1 matching with tight calipers (0.0001), and nearest-neighbor matching was used to generate 2 equally-sized, propensity-matched cohorts based on steroid status.²⁶ Nearest-neighbor matching identifies patients in both cohorts with the closest propensity scores for inclusion in propensity-matched cohorts. This matching is continued until 1 group runs out of patients to match. Baseline patient and operative characteristics for the unadjusted and propensity-matched groups were compared using Pearson’s χ² analysis. Outcomes after THA by steroid status were also compared in both unadjusted and propensity-matched groups. Finally, all patients who were readmitted were identified, and the reason for readmission was determined using the International Classification of Disease Ninth (ICD-9) and Tenth (ICD-10) edition codes. Patients were classified as having an infectious readmission only if the ICD code clearly stated an infectious etiology. For instance, a patient with an intestinal infection due to Clostridium difficile (ICD-9 008.45) was counted as a gastrointestinal infection, whereas diarrhea without a distinctly specified etiology (ICD-9 787.91, ICD-10 R19.7) was counted as a gastrointestinal medical complication. Readmission data was only available in ACS-NSQIP from 2011 to 2015, constituting 92.5% of all patients included in this study. We used SPSS version 23 (IBM Corporation) for all statistical analyses, and defined a significant P value as <.05.

RESULTS

BASELINE PATIENTS AND OPERATIVE CHARACTERISTICS

In total, we identified 101,532 patients who underwent THA (Table 1). O these, 3714 (3.7%) chronically used corticosteroids preoperatively, whereas 97,818 (96.3%) did not. 

When the unadjusted cohorts were compared, patients using corticosteroids were more likely to be female, less likely to obese, more likely to have hypertension, diabetes mellitus, COPD, higher ASA class, undergone THA with general anesthesia, and have a dependent functional status (P < .001 for all comparisons). After propensity matching, 2 equally sized cohorts of 3618 patients each were generated based on steroid status and no differences in baseline and operative characteristics were identified between the 2 groups.

Continue to: CLINICAL OUTCOMES BY STEROID STATUS

 

CLINCIAL OUTCOMES BY STEROID STATUS

A comparison of unadjusted cohorts showed that patients who used preoperative steroids had an increased rate of any complication (7.89%) when compared with those who did not (4.87%) (Table 2). 

Similarly, those who used corticosteroids preoperatively had an increased rate of renal complications, respiratory complications, return to the operating room, sepsis, UTI, superficial and deep SSI, and perioperative blood transfusions. They also were more likely to have a 30-day hospital readmission (P < .05 for all comparisons).

When propensity-matched cohorts were compared, patients who used steroids preoperatively were found to have higher rates of any complication (odds Ratio [OR] 1.30, P = .003), sepsis (OR 2.07, P = .022), UTI (OR 1.61, P = .020), superficial SSI (OR 1.73, P = .038), and hospital readmission (OR 1.50, P < .001; Table 3).

REASONS FOR HOSPITAL READMISSION

In total, 3397 patients were readmitted to the hospital within thirty days. Of these, 226 used steroids preoperatively, and 3171 did not (Table 4). 

The most common reason for hospital readmission in patients who used preoperative corticosteroids was infectious complications (72 patients, 31.9% of all readmitted patients in this cohort), followed by medical complications (59 patients, 26.1%), and hip-related complications (48 patients, 21.2%). In those who did not use steroids preoperatively, the most common reason for hospital readmission was medical complications (932 patients, 29.4% of all readmitted patients in this cohort), followed by infectious complications (792 patients, 25.0%), and hip-related complications (763 patients, 24.1%).

Continue to: DISCUSSION

 

 

DISCUSSION

Nearly 3% of individuals >80 years in the US population chronically use corticosteroids for a medical condition,⁸ and this rate is likely higher in specific subsets of patients, such as those with rheumatoid arthritis.⁹ While some studies have assessed the impact of intra-articular corticosteroid hip injections on perioperative outcomes in THA,¹⁷ similar studies assessing systemic corticosteroid usage are lacking. The purpose of this study was to characterize short-term perioperative outcomes in patients undergoing THA who chronically use systemic steroids when compared with those who do not. We found that the prevalence of preoperative chronic steroid use in this cohort of THA patients was 3.7%. We also identified increased rates of infectious complications, including sepsis, UTI, and superficial SSI, in patients who used preoperative corticosteroids. Furthermore, we found an increased rate of hospital readmissions in corticosteroid users and identified the most common reason for hospital readmission as infectious complications in this cohort.

The primary finding of this study was an increase in postoperative infections in patients who use preoperative steroids chronically for medical conditions. Immunosuppression has previously been identified as a risk factor for developing periprosthetic joint infections. Tannenbaum and colleagues²⁷ performed a retrospective study of 19 patients who underwent either a kidney or liver transplant and were maintained on an induction regimen of either prednisone and azathioprine or cyclosporine. These 19 patients also underwent either a THA or total knee arthroplasty, and 5 of these patients (26.3%) developed a periprosthetic joint infection after an average of 3.4 years following the arthroplasty procedure. In another study of 37 renal transplant and dialysis patients who underwent a total of 45 THA procedures, there were 3 instances of superficial SSI and 2 instances of deep SSI.²⁸ However, reported infection rates in transplant patients undergoing THA vary significantly, and studies have been unable to assess the true impact that chronic immunosuppression has on perioperative infection rates.²⁹ In this study, patients who used preoperative corticosteroids chronically were at increased risk of perioperative infections, including sepsis, UTI, and superficial SSI.

Deep vein thrombosis is another postoperative complication that has been associated with chronic steroid use.³⁰ In a case-control study of 38,765 patients who developed a venous thromboembolism and 387,650 control patients who did not, Johannesdottir and colleagues³⁰ found an increased thromboembolic risk in current users of systemic glucocorticoids, but not former users, as well as an increased risk as the dose of glucocorticoids increased. We were not able to identify a similar increase in DVT/PE in chronic corticosteroid users, perhaps due to our sample size, or because we could not do subgroup analyses based on the type or dosage of steroid that a patient was taking. Future studies that identify the highest risk patients among those using systemic corticosteroids are important because parenteral corticosteroids are being increasingly used in THA to alleviate postoperative pain as an opioid-sparing measure.^31,32

Finally, we also found that patients who use chronic, systemic corticosteroids are at an increased risk for hospital readmission, when compared with those patients who are not using steroids and are most likely to be readmitted for an infectious complication. Schairer and colleagues³³ assessed readmission rates after THA and found 30- and 90-day readmission rate of 4% and 7%, respectively. These authors also found that medical complications accounted for approximately 25% of readmissions, and hip-related complications (eg, dislocation, SSI) accounted for >50%. In our study, we found a 30-day readmission rate in non-steroid users of 3.53% and a rate of 6.52% in chronic steroid users. More than 30% of patients using a steroid were readmitted for infectious complications. As THA is becoming increasingly reimbursed under a bundled payments model by Medicare and Medicaid,^34-36 reducing short-term readmissions is imperative. Therefore, discharge counseling that emphasizes how to recognize both the signs and symptoms of infection as well as how to prevent infections, such as reducing SSIs through appropriate wound care, may be warranted in higher risk chronic steroid users.

This study has a number of limitations that are inherent to ACS-NSQIP. First, we lacked specific information on a patient’s steroid history, including which corticosteroid they were using, dosage, frequency, and the indication for corticosteroid therapy. Therefore, we were unable to establish a dose-dependent relationship between steroid exposure and postoperative complications after THA. Second, we were able to assess only 30-day rates of complications and readmissions, and therefore, we were unable to identify intermediate- and long-term effects of systemic corticosteroid use on THA. Finally, we could not determine orthopedic- or hip-specific postoperative outcomes, such as functional scores and range of motion.

Continue to: CONCLUSION

 

 

CONCLUSION

In conclusion, this study quantified the increased risk for perioperative complications and hospital readmissions in patients who chronically use corticosteroids and are undergoing THA, when compared with those who do not use corticosteroids. These results suggest that patients who are on long-term steroids are at an increased risk for complications, primarily infectious complications. This finding has important implications for patient counseling, preoperative risk stratification, and suggests that higher risk patients, such as chronic steroid users, may benefit from improved discharge care to decrease complication rates.

ABSTRACT

Systemic corticosteroids are used to treat a number of medical conditions; however, they are associated with numerous adverse effects. The impact of preoperative chronic corticosteroid use on postoperative outcomes following total hip arthroplasty (THA) is unclear. The purpose of this study was to assess the independent effect of chronic systemic preoperative steroid use on short-term perioperative complications and readmissions after THA.

All patients undergoing primary THA in the American College of Surgeons National Surgical Quality Improvement Program registry from 2005 to -–2015 were identified. Patients were considered chronic steroid users if they used any dosage of oral or parenteral steroids for >10 of the preceding 30 days before THA. Two equally sized propensity-matched groups based on preoperative steroid use were generated to account for differences in operative and baseline characteristics between the groups. Thirty-day complications and hospital readmissions rates were compared using bivariate analysis.

Of 101,532 THA patients who underwent primary THA, 3714 (3.7%) were identified as chronic corticosteroid users. Comparison of propensity-matched cohorts identified an increased rate of any complication (odds ratio [OR] 1.30, P = .003), sepsis (OR 2.07, P = .022), urinary tract infection (OR 1.61, P = .020), superficial surgical site infection (OR 1.73, P = .038), and hospital readmission (OR 1.50, P < .001) in patients who used systemic steroids preoperatively. Readmissions in preoperative steroid users were most commonly for infectious reasons.

Patients prescribed chronic corticosteroids are at a significantly increased risk of both 30-day periopative complications and hospital readmissions. This finding has important implications for pre- and postoperative patient counseling as well as preoperative risk stratification.

Continue to: Corticosteroids are powerful...

 

Corticosteroids are powerful anti-inflammatory steroid hormones that have many indications in the treatment of medical diseases, including advanced or poorly controlled asthma, chronic obstructive pulmonary disease (COPD), inflammatory bowel disease, allergic conditions, among other indications.^1-4 In orthopedics and rheumatology, systemic steroids are, at times, used in patients with rheumatoid arthritis, systemic lupus erythematosus, and vasculitides.^5-7 Overman and colleagues,⁸ using data from the National Health and Nutrition Examination Survey between 1999 and 2008 identified both a 1.2% prevalence of chronic corticosteroid usage in the United States across all age groups and a positive correlation between steroid use prevalence and increasing age. In that study, nearly two-thirds of survey respondents reported using corticosteroids chronically for >90 days. Another observational study in the United Kingdom found that long-term steroid prescriptions increased between 1989 to 2008 and that 13.6% of patients with rheumatoid arthritis and 66.5% of patients with polymyalgia rheumatica or giant cell arteritis used long-term steroids.⁹

Enterally- or parenterally-administered corticosteroids have numerous systemic effects that are of particular relevance to orthopedic surgeons. Corticosteroids induce osteoporosis by preferentially inducing osteoclastic activity while inhibiting the differentiation of osteoblasts, ultimately leading to decreased bone quality and mass.¹⁰ As a consequence, patients who have previously used corticosteroids are more than twice as likely to have a hip fracture.¹¹ Steroids also increase the risk of both osteonecrosis and myopathy, among other musculoskeletal effects.¹² In addition to orthopedic complications, steroids have broad inhibitory effects on both acquired and innate immunity, which significantly increases the risk of infections.¹³ This increased risk of infection is dose-dependent¹⁴ and synergistic with other immunosuppressive drugs.¹⁵

Patients with hip pain may receive localized corticosteroid hip joint injections during the nonoperative management of various hip pathologies, including arthritis, bursitis, and labral tears.^16,17 Outcomes of patients who received intra-articular corticosteroid injections before total hip arthroplasty (THA) were evaluated in a systematic review of 9 studies by Pereira and colleagues.¹⁷ These authors found that the infection rate (both superficial and deep surgical site infections [SSI]) after THA in patients who received local steroid injection into the hip before surgery was between 0% and 30%.¹⁷ However, similar studies assessing the impact that systemic steroids have on outcomes after THA are lacking. Patients who undergo THA for conditions associated with higher lifetime steroid usage have worse outcomes than those who do not. For instance, in patients undergoing THA for rheumatoid arthritis, the rates of both postoperative periprosthetic joint infection and hip dislocation are higher, when compared with osteoarthritis.^18,19 However, it is unclear how much of this difference in outcomes is due to the underlying disease, adverse effects of steroids, or both. Given the high prevalence of chronic systemic steroid use, it is essential to elucidate more clearly the impact that these medications have on perioperative outcomes after THA.  

Therefore, the purpose of this study was to characterize short-term perioperative outcomes, including complication and readmission rates in patients undergoing THA while taking chronic preoperative corticosteroids. We also sought to identify the most common reasons for hospital readmission in patients who did and did not use long-term steroids.

MATERIALS AND METHODS

STUDY DESIGN AND SETTING

This investigation was a retrospective cohort study that utilized the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) registry.²⁰ The ACS-NSQIP is a prospectively collected, multi-institutional database that collects demographical information, operative variables, and both postoperative complications and hospital readmission data. Data is collected for up to 30 days after the index procedure, and patients are contacted by telephone if they are discharged before 30 days. Patient data is entered by specially trained surgical clinical reviewers and is routinely audited by the ACS-NSQIP, leading to more accurate data when compared with administrative research databases.^21,22 The ACS-NSQIP has been used in orthopedic surgery outcomes-based studies.^23-25

Continue to: All patients undergoing...

 

All patients undergoing THA between 2005 and 2015 were identified in the registry using primary Current Procedural Terminology code 27130. Patients were split into 2 groups based on whether or not they chronically used corticosteroids preoperatively for a medical condition. A patient was considered a chronic corticosteroid user if he/she used oral or parenteral corticosteroids within 30 days before the index procedure for >10 of the preceding 30 days. Those who received a 1-time steroid pulse or those who used topical or inhaled steroids were not considered as steroid users in this study.

BASELINE CHARACTERISTICS AND PERIOPERATIVE OUTCOMES

Baseline patient and operative characteristics, including patient age, gender, body mass index (BMI), functional status, American Society of Anesthesiologists (ASA) class, anesthesia type, operative duration, and medical comorbidities including hypertension, COPD, diabetes mellitus, and smoking history, were compared between both groups. Perioperative outcomes that were assessed in this study include death, renal, respiratory, and cardiac complications, deep vein thrombosis or pulmonary embolism, stroke, sepsis, return to the operating room, urinary tract infection (UTI), wound dehiscence, superficial and deep SSI, need for a blood transfusion within 72 hours of index surgical procedure, and hospital readmissions. Renal complications were defined as acute or progressive renal insufficiency; respiratory complications were defined as failure to wean from the ventilator, need for intubation after the index procedure, and the occurrence of pneumonia; and cardiac complications were defined as myocardial infarction or cardiac arrest requiring cardiopulmonary resuscitation. Patients were excluded if they had missing baseline or operative characteristic data, an unclean wound classification at the time of admission, or if their THA was considered emergent.

STATISTICAL ANALYSIS

A propensity score-matched comparison was performed to adjust for differences in baseline and operative characteristics between the 2 cohorts in this study. In the current study, the propensity score was defined as the conditional probability that a patient chronically used preoperative corticosteroids for a medical condition, as a function of age, BMI, gender, ASA class, functional status, medical comorbidities, anesthesia type, and operative duration. A 1:1 matching with tight calipers (0.0001), and nearest-neighbor matching was used to generate 2 equally-sized, propensity-matched cohorts based on steroid status.²⁶ Nearest-neighbor matching identifies patients in both cohorts with the closest propensity scores for inclusion in propensity-matched cohorts. This matching is continued until 1 group runs out of patients to match. Baseline patient and operative characteristics for the unadjusted and propensity-matched groups were compared using Pearson’s χ² analysis. Outcomes after THA by steroid status were also compared in both unadjusted and propensity-matched groups. Finally, all patients who were readmitted were identified, and the reason for readmission was determined using the International Classification of Disease Ninth (ICD-9) and Tenth (ICD-10) edition codes. Patients were classified as having an infectious readmission only if the ICD code clearly stated an infectious etiology. For instance, a patient with an intestinal infection due to Clostridium difficile (ICD-9 008.45) was counted as a gastrointestinal infection, whereas diarrhea without a distinctly specified etiology (ICD-9 787.91, ICD-10 R19.7) was counted as a gastrointestinal medical complication. Readmission data was only available in ACS-NSQIP from 2011 to 2015, constituting 92.5% of all patients included in this study. We used SPSS version 23 (IBM Corporation) for all statistical analyses, and defined a significant P value as <.05.

RESULTS

BASELINE PATIENTS AND OPERATIVE CHARACTERISTICS

In total, we identified 101,532 patients who underwent THA (Table 1). O these, 3714 (3.7%) chronically used corticosteroids preoperatively, whereas 97,818 (96.3%) did not. 

When the unadjusted cohorts were compared, patients using corticosteroids were more likely to be female, less likely to obese, more likely to have hypertension, diabetes mellitus, COPD, higher ASA class, undergone THA with general anesthesia, and have a dependent functional status (P < .001 for all comparisons). After propensity matching, 2 equally sized cohorts of 3618 patients each were generated based on steroid status and no differences in baseline and operative characteristics were identified between the 2 groups.

Continue to: CLINICAL OUTCOMES BY STEROID STATUS

 

CLINCIAL OUTCOMES BY STEROID STATUS

A comparison of unadjusted cohorts showed that patients who used preoperative steroids had an increased rate of any complication (7.89%) when compared with those who did not (4.87%) (Table 2). 

Similarly, those who used corticosteroids preoperatively had an increased rate of renal complications, respiratory complications, return to the operating room, sepsis, UTI, superficial and deep SSI, and perioperative blood transfusions. They also were more likely to have a 30-day hospital readmission (P < .05 for all comparisons).

When propensity-matched cohorts were compared, patients who used steroids preoperatively were found to have higher rates of any complication (odds Ratio [OR] 1.30, P = .003), sepsis (OR 2.07, P = .022), UTI (OR 1.61, P = .020), superficial SSI (OR 1.73, P = .038), and hospital readmission (OR 1.50, P < .001; Table 3).

REASONS FOR HOSPITAL READMISSION

In total, 3397 patients were readmitted to the hospital within thirty days. Of these, 226 used steroids preoperatively, and 3171 did not (Table 4). 

The most common reason for hospital readmission in patients who used preoperative corticosteroids was infectious complications (72 patients, 31.9% of all readmitted patients in this cohort), followed by medical complications (59 patients, 26.1%), and hip-related complications (48 patients, 21.2%). In those who did not use steroids preoperatively, the most common reason for hospital readmission was medical complications (932 patients, 29.4% of all readmitted patients in this cohort), followed by infectious complications (792 patients, 25.0%), and hip-related complications (763 patients, 24.1%).

Continue to: DISCUSSION

 

 

DISCUSSION

Nearly 3% of individuals >80 years in the US population chronically use corticosteroids for a medical condition,⁸ and this rate is likely higher in specific subsets of patients, such as those with rheumatoid arthritis.⁹ While some studies have assessed the impact of intra-articular corticosteroid hip injections on perioperative outcomes in THA,¹⁷ similar studies assessing systemic corticosteroid usage are lacking. The purpose of this study was to characterize short-term perioperative outcomes in patients undergoing THA who chronically use systemic steroids when compared with those who do not. We found that the prevalence of preoperative chronic steroid use in this cohort of THA patients was 3.7%. We also identified increased rates of infectious complications, including sepsis, UTI, and superficial SSI, in patients who used preoperative corticosteroids. Furthermore, we found an increased rate of hospital readmissions in corticosteroid users and identified the most common reason for hospital readmission as infectious complications in this cohort.

The primary finding of this study was an increase in postoperative infections in patients who use preoperative steroids chronically for medical conditions. Immunosuppression has previously been identified as a risk factor for developing periprosthetic joint infections. Tannenbaum and colleagues²⁷ performed a retrospective study of 19 patients who underwent either a kidney or liver transplant and were maintained on an induction regimen of either prednisone and azathioprine or cyclosporine. These 19 patients also underwent either a THA or total knee arthroplasty, and 5 of these patients (26.3%) developed a periprosthetic joint infection after an average of 3.4 years following the arthroplasty procedure. In another study of 37 renal transplant and dialysis patients who underwent a total of 45 THA procedures, there were 3 instances of superficial SSI and 2 instances of deep SSI.²⁸ However, reported infection rates in transplant patients undergoing THA vary significantly, and studies have been unable to assess the true impact that chronic immunosuppression has on perioperative infection rates.²⁹ In this study, patients who used preoperative corticosteroids chronically were at increased risk of perioperative infections, including sepsis, UTI, and superficial SSI.

Deep vein thrombosis is another postoperative complication that has been associated with chronic steroid use.³⁰ In a case-control study of 38,765 patients who developed a venous thromboembolism and 387,650 control patients who did not, Johannesdottir and colleagues³⁰ found an increased thromboembolic risk in current users of systemic glucocorticoids, but not former users, as well as an increased risk as the dose of glucocorticoids increased. We were not able to identify a similar increase in DVT/PE in chronic corticosteroid users, perhaps due to our sample size, or because we could not do subgroup analyses based on the type or dosage of steroid that a patient was taking. Future studies that identify the highest risk patients among those using systemic corticosteroids are important because parenteral corticosteroids are being increasingly used in THA to alleviate postoperative pain as an opioid-sparing measure.^31,32

Finally, we also found that patients who use chronic, systemic corticosteroids are at an increased risk for hospital readmission, when compared with those patients who are not using steroids and are most likely to be readmitted for an infectious complication. Schairer and colleagues³³ assessed readmission rates after THA and found 30- and 90-day readmission rate of 4% and 7%, respectively. These authors also found that medical complications accounted for approximately 25% of readmissions, and hip-related complications (eg, dislocation, SSI) accounted for >50%. In our study, we found a 30-day readmission rate in non-steroid users of 3.53% and a rate of 6.52% in chronic steroid users. More than 30% of patients using a steroid were readmitted for infectious complications. As THA is becoming increasingly reimbursed under a bundled payments model by Medicare and Medicaid,^34-36 reducing short-term readmissions is imperative. Therefore, discharge counseling that emphasizes how to recognize both the signs and symptoms of infection as well as how to prevent infections, such as reducing SSIs through appropriate wound care, may be warranted in higher risk chronic steroid users.

This study has a number of limitations that are inherent to ACS-NSQIP. First, we lacked specific information on a patient’s steroid history, including which corticosteroid they were using, dosage, frequency, and the indication for corticosteroid therapy. Therefore, we were unable to establish a dose-dependent relationship between steroid exposure and postoperative complications after THA. Second, we were able to assess only 30-day rates of complications and readmissions, and therefore, we were unable to identify intermediate- and long-term effects of systemic corticosteroid use on THA. Finally, we could not determine orthopedic- or hip-specific postoperative outcomes, such as functional scores and range of motion.

Continue to: CONCLUSION

 

 

CONCLUSION

In conclusion, this study quantified the increased risk for perioperative complications and hospital readmissions in patients who chronically use corticosteroids and are undergoing THA, when compared with those who do not use corticosteroids. These results suggest that patients who are on long-term steroids are at an increased risk for complications, primarily infectious complications. This finding has important implications for patient counseling, preoperative risk stratification, and suggests that higher risk patients, such as chronic steroid users, may benefit from improved discharge care to decrease complication rates.

ABSTRACT

Systemic corticosteroids are used to treat a number of medical conditions; however, they are associated with numerous adverse effects. The impact of preoperative chronic corticosteroid use on postoperative outcomes following total hip arthroplasty (THA) is unclear. The purpose of this study was to assess the independent effect of chronic systemic preoperative steroid use on short-term perioperative complications and readmissions after THA.

All patients undergoing primary THA in the American College of Surgeons National Surgical Quality Improvement Program registry from 2005 to -–2015 were identified. Patients were considered chronic steroid users if they used any dosage of oral or parenteral steroids for >10 of the preceding 30 days before THA. Two equally sized propensity-matched groups based on preoperative steroid use were generated to account for differences in operative and baseline characteristics between the groups. Thirty-day complications and hospital readmissions rates were compared using bivariate analysis.

Of 101,532 THA patients who underwent primary THA, 3714 (3.7%) were identified as chronic corticosteroid users. Comparison of propensity-matched cohorts identified an increased rate of any complication (odds ratio [OR] 1.30, P = .003), sepsis (OR 2.07, P = .022), urinary tract infection (OR 1.61, P = .020), superficial surgical site infection (OR 1.73, P = .038), and hospital readmission (OR 1.50, P < .001) in patients who used systemic steroids preoperatively. Readmissions in preoperative steroid users were most commonly for infectious reasons.

Patients prescribed chronic corticosteroids are at a significantly increased risk of both 30-day periopative complications and hospital readmissions. This finding has important implications for pre- and postoperative patient counseling as well as preoperative risk stratification.

Continue to: Corticosteroids are powerful...

 

Corticosteroids are powerful anti-inflammatory steroid hormones that have many indications in the treatment of medical diseases, including advanced or poorly controlled asthma, chronic obstructive pulmonary disease (COPD), inflammatory bowel disease, allergic conditions, among other indications.^1-4 In orthopedics and rheumatology, systemic steroids are, at times, used in patients with rheumatoid arthritis, systemic lupus erythematosus, and vasculitides.^5-7 Overman and colleagues,⁸ using data from the National Health and Nutrition Examination Survey between 1999 and 2008 identified both a 1.2% prevalence of chronic corticosteroid usage in the United States across all age groups and a positive correlation between steroid use prevalence and increasing age. In that study, nearly two-thirds of survey respondents reported using corticosteroids chronically for >90 days. Another observational study in the United Kingdom found that long-term steroid prescriptions increased between 1989 to 2008 and that 13.6% of patients with rheumatoid arthritis and 66.5% of patients with polymyalgia rheumatica or giant cell arteritis used long-term steroids.⁹

Enterally- or parenterally-administered corticosteroids have numerous systemic effects that are of particular relevance to orthopedic surgeons. Corticosteroids induce osteoporosis by preferentially inducing osteoclastic activity while inhibiting the differentiation of osteoblasts, ultimately leading to decreased bone quality and mass.¹⁰ As a consequence, patients who have previously used corticosteroids are more than twice as likely to have a hip fracture.¹¹ Steroids also increase the risk of both osteonecrosis and myopathy, among other musculoskeletal effects.¹² In addition to orthopedic complications, steroids have broad inhibitory effects on both acquired and innate immunity, which significantly increases the risk of infections.¹³ This increased risk of infection is dose-dependent¹⁴ and synergistic with other immunosuppressive drugs.¹⁵

Patients with hip pain may receive localized corticosteroid hip joint injections during the nonoperative management of various hip pathologies, including arthritis, bursitis, and labral tears.^16,17 Outcomes of patients who received intra-articular corticosteroid injections before total hip arthroplasty (THA) were evaluated in a systematic review of 9 studies by Pereira and colleagues.¹⁷ These authors found that the infection rate (both superficial and deep surgical site infections [SSI]) after THA in patients who received local steroid injection into the hip before surgery was between 0% and 30%.¹⁷ However, similar studies assessing the impact that systemic steroids have on outcomes after THA are lacking. Patients who undergo THA for conditions associated with higher lifetime steroid usage have worse outcomes than those who do not. For instance, in patients undergoing THA for rheumatoid arthritis, the rates of both postoperative periprosthetic joint infection and hip dislocation are higher, when compared with osteoarthritis.^18,19 However, it is unclear how much of this difference in outcomes is due to the underlying disease, adverse effects of steroids, or both. Given the high prevalence of chronic systemic steroid use, it is essential to elucidate more clearly the impact that these medications have on perioperative outcomes after THA.  

Therefore, the purpose of this study was to characterize short-term perioperative outcomes, including complication and readmission rates in patients undergoing THA while taking chronic preoperative corticosteroids. We also sought to identify the most common reasons for hospital readmission in patients who did and did not use long-term steroids.

MATERIALS AND METHODS

STUDY DESIGN AND SETTING

This investigation was a retrospective cohort study that utilized the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) registry.²⁰ The ACS-NSQIP is a prospectively collected, multi-institutional database that collects demographical information, operative variables, and both postoperative complications and hospital readmission data. Data is collected for up to 30 days after the index procedure, and patients are contacted by telephone if they are discharged before 30 days. Patient data is entered by specially trained surgical clinical reviewers and is routinely audited by the ACS-NSQIP, leading to more accurate data when compared with administrative research databases.^21,22 The ACS-NSQIP has been used in orthopedic surgery outcomes-based studies.^23-25

Continue to: All patients undergoing...

 

All patients undergoing THA between 2005 and 2015 were identified in the registry using primary Current Procedural Terminology code 27130. Patients were split into 2 groups based on whether or not they chronically used corticosteroids preoperatively for a medical condition. A patient was considered a chronic corticosteroid user if he/she used oral or parenteral corticosteroids within 30 days before the index procedure for >10 of the preceding 30 days. Those who received a 1-time steroid pulse or those who used topical or inhaled steroids were not considered as steroid users in this study.

BASELINE CHARACTERISTICS AND PERIOPERATIVE OUTCOMES

Baseline patient and operative characteristics, including patient age, gender, body mass index (BMI), functional status, American Society of Anesthesiologists (ASA) class, anesthesia type, operative duration, and medical comorbidities including hypertension, COPD, diabetes mellitus, and smoking history, were compared between both groups. Perioperative outcomes that were assessed in this study include death, renal, respiratory, and cardiac complications, deep vein thrombosis or pulmonary embolism, stroke, sepsis, return to the operating room, urinary tract infection (UTI), wound dehiscence, superficial and deep SSI, need for a blood transfusion within 72 hours of index surgical procedure, and hospital readmissions. Renal complications were defined as acute or progressive renal insufficiency; respiratory complications were defined as failure to wean from the ventilator, need for intubation after the index procedure, and the occurrence of pneumonia; and cardiac complications were defined as myocardial infarction or cardiac arrest requiring cardiopulmonary resuscitation. Patients were excluded if they had missing baseline or operative characteristic data, an unclean wound classification at the time of admission, or if their THA was considered emergent.

STATISTICAL ANALYSIS

A propensity score-matched comparison was performed to adjust for differences in baseline and operative characteristics between the 2 cohorts in this study. In the current study, the propensity score was defined as the conditional probability that a patient chronically used preoperative corticosteroids for a medical condition, as a function of age, BMI, gender, ASA class, functional status, medical comorbidities, anesthesia type, and operative duration. A 1:1 matching with tight calipers (0.0001), and nearest-neighbor matching was used to generate 2 equally-sized, propensity-matched cohorts based on steroid status.²⁶ Nearest-neighbor matching identifies patients in both cohorts with the closest propensity scores for inclusion in propensity-matched cohorts. This matching is continued until 1 group runs out of patients to match. Baseline patient and operative characteristics for the unadjusted and propensity-matched groups were compared using Pearson’s χ² analysis. Outcomes after THA by steroid status were also compared in both unadjusted and propensity-matched groups. Finally, all patients who were readmitted were identified, and the reason for readmission was determined using the International Classification of Disease Ninth (ICD-9) and Tenth (ICD-10) edition codes. Patients were classified as having an infectious readmission only if the ICD code clearly stated an infectious etiology. For instance, a patient with an intestinal infection due to Clostridium difficile (ICD-9 008.45) was counted as a gastrointestinal infection, whereas diarrhea without a distinctly specified etiology (ICD-9 787.91, ICD-10 R19.7) was counted as a gastrointestinal medical complication. Readmission data was only available in ACS-NSQIP from 2011 to 2015, constituting 92.5% of all patients included in this study. We used SPSS version 23 (IBM Corporation) for all statistical analyses, and defined a significant P value as <.05.

RESULTS

BASELINE PATIENTS AND OPERATIVE CHARACTERISTICS

In total, we identified 101,532 patients who underwent THA (Table 1). O these, 3714 (3.7%) chronically used corticosteroids preoperatively, whereas 97,818 (96.3%) did not. 

When the unadjusted cohorts were compared, patients using corticosteroids were more likely to be female, less likely to obese, more likely to have hypertension, diabetes mellitus, COPD, higher ASA class, undergone THA with general anesthesia, and have a dependent functional status (P < .001 for all comparisons). After propensity matching, 2 equally sized cohorts of 3618 patients each were generated based on steroid status and no differences in baseline and operative characteristics were identified between the 2 groups.

Continue to: CLINICAL OUTCOMES BY STEROID STATUS

 

CLINCIAL OUTCOMES BY STEROID STATUS

A comparison of unadjusted cohorts showed that patients who used preoperative steroids had an increased rate of any complication (7.89%) when compared with those who did not (4.87%) (Table 2). 

Similarly, those who used corticosteroids preoperatively had an increased rate of renal complications, respiratory complications, return to the operating room, sepsis, UTI, superficial and deep SSI, and perioperative blood transfusions. They also were more likely to have a 30-day hospital readmission (P < .05 for all comparisons).

When propensity-matched cohorts were compared, patients who used steroids preoperatively were found to have higher rates of any complication (odds Ratio [OR] 1.30, P = .003), sepsis (OR 2.07, P = .022), UTI (OR 1.61, P = .020), superficial SSI (OR 1.73, P = .038), and hospital readmission (OR 1.50, P < .001; Table 3).

REASONS FOR HOSPITAL READMISSION

In total, 3397 patients were readmitted to the hospital within thirty days. Of these, 226 used steroids preoperatively, and 3171 did not (Table 4). 

The most common reason for hospital readmission in patients who used preoperative corticosteroids was infectious complications (72 patients, 31.9% of all readmitted patients in this cohort), followed by medical complications (59 patients, 26.1%), and hip-related complications (48 patients, 21.2%). In those who did not use steroids preoperatively, the most common reason for hospital readmission was medical complications (932 patients, 29.4% of all readmitted patients in this cohort), followed by infectious complications (792 patients, 25.0%), and hip-related complications (763 patients, 24.1%).

Continue to: DISCUSSION

 

 

DISCUSSION

Nearly 3% of individuals >80 years in the US population chronically use corticosteroids for a medical condition,⁸ and this rate is likely higher in specific subsets of patients, such as those with rheumatoid arthritis.⁹ While some studies have assessed the impact of intra-articular corticosteroid hip injections on perioperative outcomes in THA,¹⁷ similar studies assessing systemic corticosteroid usage are lacking. The purpose of this study was to characterize short-term perioperative outcomes in patients undergoing THA who chronically use systemic steroids when compared with those who do not. We found that the prevalence of preoperative chronic steroid use in this cohort of THA patients was 3.7%. We also identified increased rates of infectious complications, including sepsis, UTI, and superficial SSI, in patients who used preoperative corticosteroids. Furthermore, we found an increased rate of hospital readmissions in corticosteroid users and identified the most common reason for hospital readmission as infectious complications in this cohort.

The primary finding of this study was an increase in postoperative infections in patients who use preoperative steroids chronically for medical conditions. Immunosuppression has previously been identified as a risk factor for developing periprosthetic joint infections. Tannenbaum and colleagues²⁷ performed a retrospective study of 19 patients who underwent either a kidney or liver transplant and were maintained on an induction regimen of either prednisone and azathioprine or cyclosporine. These 19 patients also underwent either a THA or total knee arthroplasty, and 5 of these patients (26.3%) developed a periprosthetic joint infection after an average of 3.4 years following the arthroplasty procedure. In another study of 37 renal transplant and dialysis patients who underwent a total of 45 THA procedures, there were 3 instances of superficial SSI and 2 instances of deep SSI.²⁸ However, reported infection rates in transplant patients undergoing THA vary significantly, and studies have been unable to assess the true impact that chronic immunosuppression has on perioperative infection rates.²⁹ In this study, patients who used preoperative corticosteroids chronically were at increased risk of perioperative infections, including sepsis, UTI, and superficial SSI.

Deep vein thrombosis is another postoperative complication that has been associated with chronic steroid use.³⁰ In a case-control study of 38,765 patients who developed a venous thromboembolism and 387,650 control patients who did not, Johannesdottir and colleagues³⁰ found an increased thromboembolic risk in current users of systemic glucocorticoids, but not former users, as well as an increased risk as the dose of glucocorticoids increased. We were not able to identify a similar increase in DVT/PE in chronic corticosteroid users, perhaps due to our sample size, or because we could not do subgroup analyses based on the type or dosage of steroid that a patient was taking. Future studies that identify the highest risk patients among those using systemic corticosteroids are important because parenteral corticosteroids are being increasingly used in THA to alleviate postoperative pain as an opioid-sparing measure.^31,32

Finally, we also found that patients who use chronic, systemic corticosteroids are at an increased risk for hospital readmission, when compared with those patients who are not using steroids and are most likely to be readmitted for an infectious complication. Schairer and colleagues³³ assessed readmission rates after THA and found 30- and 90-day readmission rate of 4% and 7%, respectively. These authors also found that medical complications accounted for approximately 25% of readmissions, and hip-related complications (eg, dislocation, SSI) accounted for >50%. In our study, we found a 30-day readmission rate in non-steroid users of 3.53% and a rate of 6.52% in chronic steroid users. More than 30% of patients using a steroid were readmitted for infectious complications. As THA is becoming increasingly reimbursed under a bundled payments model by Medicare and Medicaid,^34-36 reducing short-term readmissions is imperative. Therefore, discharge counseling that emphasizes how to recognize both the signs and symptoms of infection as well as how to prevent infections, such as reducing SSIs through appropriate wound care, may be warranted in higher risk chronic steroid users.

This study has a number of limitations that are inherent to ACS-NSQIP. First, we lacked specific information on a patient’s steroid history, including which corticosteroid they were using, dosage, frequency, and the indication for corticosteroid therapy. Therefore, we were unable to establish a dose-dependent relationship between steroid exposure and postoperative complications after THA. Second, we were able to assess only 30-day rates of complications and readmissions, and therefore, we were unable to identify intermediate- and long-term effects of systemic corticosteroid use on THA. Finally, we could not determine orthopedic- or hip-specific postoperative outcomes, such as functional scores and range of motion.

Continue to: CONCLUSION

 

 

CONCLUSION

In conclusion, this study quantified the increased risk for perioperative complications and hospital readmissions in patients who chronically use corticosteroids and are undergoing THA, when compared with those who do not use corticosteroids. These results suggest that patients who are on long-term steroids are at an increased risk for complications, primarily infectious complications. This finding has important implications for patient counseling, preoperative risk stratification, and suggests that higher risk patients, such as chronic steroid users, may benefit from improved discharge care to decrease complication rates.

There will be 53 million older adults in the US by 2020.¹ Increasing age often brings medical comorbidities and prescriptions for multiple medications. An increasing number of prescribed medications combined with age-related changes in the ability to metabolize drugs makes older adults highly vulnerable to adverse drug events (ADEs).² In addition, older adults often have difficulty self-managing their medications and adhering to prescribed regimens.³ As a result, ADEs can lead to poor health outcomes, including hospitalizations, in older adults.

Medication errors and ADEs are particularly common during transitions from hospital to home and can lead to unnecessary readmissions,a major cause of wasteful health care spending in the US.^4,5 More than $25 billion are estimated to be spent annually on hospital readmissions, with Medicare picking up the bill for $17 billion of the total.^6,7 Researchers have found that the majority of ADEs following hospital discharge are either entirely preventable or at least ameliorable (ie, the negative impact or harm resulting from the ADE could have been reduced).⁸

To address these issues, we undertook a clinical demonstration project that implemented a new transitional care program to improve the quality of care for older veterans transitioning from the Audie L. Murphy Veterans Memorial Hospital of the South Texas Veterans Health Care System (STVHCS) in San Antonio to home. The Geriatrics Medication Education at Discharge project (GMED) falls under the auspices of the San Antonio Geriatrics Research Education and Clinical Center (GRECC). Clinical demonstration projects are mandated for US Department of Veterans Affairs (VA) GRECCs to create and promote innovative models of care for older veterans. Dissemination of successful clinical demonstration projects to other VA sites is strongly encouraged. The GMED program was modeled after the Boston GRECC Pharmacological Intervention in Late Life (PILL) program.⁹ The PILL program, which focuses on serving older veterans with cognitive impairment, demonstrated that a postdischarge pharmacist telephone visit for medication reconciliation leads to a reduction in readmission within 60 days of discharge.⁹ The goals of the GMED program were to reduce polypharmacy, inappropriate prescribing and 30-day readmissions.

Methods

The project was conducted when a full-time clinical pharmacy specialist (CPS) was available (May-September 2013 and April 2014-March 2015). This project was approved as nonresearch/quality improvement by the University of Texas Health Science Center Institutional Review Board, which serves the STVHCS. Consent was not required.

Eligibility

Patients were identified via a daily hospital database query of all adults aged ≥ 65 years admitted to the hospital through Inpatient Medicine, Neurology, or Cardiology services within the prior 24 hours. Patients meeting any of the following criteria based on review of the Computerized Patient Record System (CPRS) by the team geriatrician and CPS were considered eligible: (1) aged ≥ 70 years prescribed ≥ 12 outpatient medications; (2) aged ≥ 65 years with a medical history of dementia; (3) aged ≥ 65 years prescribed outpatient medications meeting Beers criteria¹⁰; (4) age ≥ 65 years with ≥ 2 hospital admissions (including the current, index admission) within the past calendar year; or (5) aged ≥ 65 years with ≥ 3 emergency department visits within the past calendar year. For the first polypharmacy criterion, patients aged ≥ 70 years were selected instead of aged ≥ 65 years so as not to exceed the capacity of 1 CPS. Twelve or more medications were used as a cutoff for polypharmacy based on prior quality improvement information gathered from our VA geriatrics clinic examining the average number of medications taken by older veterans in the outpatient setting.

Related: Reducing COPD Readmission Rates: Using a COPD Care Service During Care Transitions

 

Patients were excluded if they were expected to be discharged to any facility where the patient and/or the caregiver were not primarily responsible for medication administration after discharge. Patients who met eligibility criteria but were not seen by the transitional program pharmacist (due to staff capacity) were included in this analysis as a convenience comparison group of patients who received usual care. Patients were not randomized. All communication occurred in English, but this project did not exclude patients with limited English proficiency.

A program support assistant conducted the daily query of the hospital database. The pharmacist conducted the chart review to determine eligibility and delivered the intervention. Eligible patients were selected at random for the intervention with the intention of providing the intervention to as many veterans as possible.

The GMED Intervention

The GMED program included 2 phases, which were both conducted by a CPS with oversight from a senior CPS with geriatric pharmacology expertise and an internist/geriatrician. 

The CPS carrying out the transitional care program was involved in the planning and design of the project and met weekly with the geriatrician. The Figure provides an overview of the intervention.

The first phase of the transitional care program included an individual, face-to-face meeting between the CPS and the patient during the hospitalization. If a veteran was not present in the room at the time of an attempted visit, the pharmacist made 2 additional attempts (3 total) to include the patient in the transitional care program during the hospitalization. 

The CPS performed medication reconciliation and provided medication education regarding administration and usage of the patient’s medications, using an open-ended format.¹¹ The caregiver, if any, was included in the discussion either at the bedside or by telephone following the face-to-face visit with the patient. The CPS communicated recommendations regarding appropriateness of therapy (including any potential barriers to medication adherence) to the medical team (including the attending, resident[s], and interns) in person or by telephone and through documentation in the CPRS. 

The recommendations were based on the clinical expertise of the CPS as well as on guidelines for prescribing in older adults.^10,12 The CPS used a checklist to ensure all components of the intervention were completed (Appendices 1 and 2).

The second component of the transitional care program included a telephone visit within 2 to 3 days of discharge, conducted by the same CPS who performed the face-to-face visit. The purpose of the telephone visit was to perform medication reconciliation, identify and rectify medication errors, provide further patient education, and assist in facilitating appropriate follow-up by the patient’s primary care provider (PCP), if required. At a minimum, veterans were asked a series of questions pertaining to their concerns about medication regimens, receipt of newly prescribed medications at discharge, additional education regarding medications after the CPS encounter during hospitalization, and whether the veteran required assistance with the medication regimen in the home setting. Follow-up questions were asked as needed to clarify and identify potential medication problems. All information from this telephone encounter was communicated to the PCP through CPRS documentation and by telephone as needed.

Related: Initiative to Minimize Pharmaceutical Risk in Older Veterans (IMPROVE) Polypharmacy Clinic

 

Data Collection

A standardized questionnaire was used prospectively for patients in the transitional care program group to assess patient education, primary residence, presence of a caregiver, fall history, medication adherence, and cognitive status (using Mini-Cog).¹³ Additional information (patient age, number of outpatient medications prior to and following the admission, presence of Beers criteria outpatient medications prior to and following the admission, new outpatient prescriptions, and changes to existing prescriptions as a result of the hospitalization) was gathered prospectively from patient interviews or from chart review.

For patients included in the comparison group, a retrospective administrative chart review was conducted to collect information such as age, sex, ethnic group, admission within 1 year prior to index admission, frailty, and Charlson Comorbidity Index (CCI) score, a method of categorizing comorbidities of patients based on the diagnosis codes found in administrative data.¹⁴ Each comorbidity category has an associated weight (from 1 to 6), based on the adjusted risk of mortality or resource use, and the sum of all the weights results in a single comorbidity score for a patient (0 indicates no comorbidities; higher scores predict greater risk of mortality or increased resource use).

We used the index developed from 17 disease categories. The range for CCI was 0 to 25. Frailty was defined as the presence of any of the following frailty-related diagnoses: anemia; fall, head injury, other injury; coagulopathy; electrolyte disturbance; or gait disorder. These diagnoses are either primary frailty characteristics within the frailty phenotype or have been shown in prior studies to be associated with the frailty phenotype.^15-18 While more widely accepted frailty definitions exist,these other definitions require direct examination of the patient and could not be used in this project because we did not directly interact with the comparison group.^16,19 The frailty definition used has been previously identified as a predictor of health care utilization and 30-day readmission in a veteran population.²⁰ Whether or not the CPS detected a postdischarge medication error was recorded. All CPS recommendations were documented.

An index admission was defined as a hospital admission that occurred during the project period. Thirty-day readmission was defined as a hospital admission that occurred within 30 days of the discharge date of an index admission. Each index admission was considered individually for readmission (yes vs no) even if it occurred in the same patient over the project period. A 30-day readmission was not considered an index admission. An admission that occurred after a 30-day readmission was considered a subsequent index admission. Patients who died in the hospital were not included in this analysis, as they would not have participated in the entire intervention.

Statistical Analysis

We compared characteristics between patients who received GMED and patients who never received GMED (comparison group). Generalized estimating equations (GEE) were used to determine whether the rate of 30-day readmission (yes vs no) in the transitional care program group differed from that of the comparison group. In our GEE analysis, we assumed a binomial distribution and the logit link to model the log-odds of readmission as a linear function of transitional care program status (yes vs no) and other covariates, including age, frailty, hospital admission within 1 year prior to the index admission, and CCI score as covariates. Thirty-day readmission status associated with each index admission was coded as 1 for a readmission within 30 days of the discharge date of the index admission, or 0 for no readmission within 30 days.

 

Transitional care program status was determined whether or not the individual received the transitional care program for each index admission. This analysis allowed us to model repeated measures of index admissions as a function of the project period and whether the patient was seen by the GMED CPS during the index admission. The patient identifier was used as a cluster variable in the GEE analysis. Inverse propensity scores of receiving GMED at the index admission were adjusted as weights in the GEE analysis to minimize confounding and, hence, to strengthen the causal interpretation of the effect of the transitional care program. If there was ≥ 1 index admission, the GMED status (yes vs no) at the initial index admission was used as the dependent variable to calculate propensity scores. The propensity scores of transitional care program status were derived from the logistic regression analysis that modeled the log-odds of receiving the transitional care program at the index admission as a linear function of age, CCI, frailty, and prior hospitalization during the 1-year period prior to the index admission.

Related: Development and Implementation of a Geriatric Walking Clinic

Results

The GMED CPS saw 435 patients during the project period; 47 (10.8%) died prior to 30 days and were excluded, leaving 388 patients who received the transitional care program included in this evaluation. 

Another 1,189 patients met the eligibility criteria but were not included and were included in the comparison group. Patients in the transitional care program group were similar to those receiving usual care in the comparison group with regard to sex, ethnic group, frailty status, and CCI score (Table 1).

Data from the CPS-patient interviews and chart reviews were available for 378 of the 388 patients (Table 2). Patients were primarily male, non-Hispanic white, with a high school education. More than half (65%) the patients were admitted for a new diagnosis or clinical condition. 

The majority of patients had diabetes mellitus, and about one-third had chronic obstructive pulmonary disease, congestive heart failure, or cognitive impairment. Although about 60% of patients were prescribed a new medication as a result of the hospital admission, the number of medications from admission to discharge did not differ significantly (15.4 ± 5.5 vs 15.7 ± 5.8; P = .08).

The 30-day readmission rate was 15.6% for the transitional care program group and 21.9% for the comparison group. Three hundred seventy-one patients received the transitional care program only once, 16 patients received the transitional care program twice (ie, they had 2 index admissions during the study period and received the intervention both times), and 1 patient received the transitional care program 3 times.

In an unadjusted GEE model, the odds ratio (OR) for readmission in the transitional care program group was 0.74 (95% CI, 0.54-1.0, P = .06) compared with the usual care group (Table 3). 

After covariate adjustment, the OR for readmission was 0.54 (95% CI, 0.32-0.90, P = .02).

Thirty-five percent of patients had ≥ 1 CPS-recommended change in their treatment at the time of the inpatient admission (Table 4). 

The most common recommendation was discontinuation of at least 1 medication (23.0%), followed by correcting the medication reconciliation list that was on record for the admission (17.8%). Thirty-nine percent of patients had ≥ 1 CPS-recommended change in their treatment at the time of the follow-up phone call. The most common recommendation was to clarify medication instructions for the patient and/or caregiver and provide medication education (33.7%). Other common recommendations were to correct a medication reconciliation (16.9%) and communicate pertinent information about the admission to the PCP (14.5%).

 

Discussion

We developed a transitional care program for hospitalized older veterans to improve the transition from hospital to home. After adjusting for clinical factors, GMED was associated with 26% lower odds of readmission within 30 days of discharge compared with that of the control group. The GMED CPS made changes to the medical regimen both during the inpatient admission as well as after discharge to correct medication errors and educate patients.

In addition, GMED led to a reduction in the number of prescribed medications, which impacts inappropriate polypharmacy—a significant problem in older adults, which contributes to ADEs.²¹ Our intervention was patient centered, as all decisions and education regarding medication management were tailored to each patient, taking into account medical and psychosocial factors.

Studies of similar programs have shown that a pharmacist-based program can improve outcomes in patients transitioning from hospital to home. A meta-analysis of 19 studies that evaluated the effectiveness of pharmacy-led medication reconciliation interventions at the time of a care transition showed that compared with usual care a pharmacist intervention led to reduced medication discrepancies.²² In this meta-analysis, medication discrepancies of higher clinical impact were more easily identified through pharmacy-led interventions than with usual care, suggesting improved safety. Although not all studies have shown a clear reduction in readmission rates or other health care utilization, the addition of clinical pharmacist services in the care of inpatients has generally resulted in improved care with no evidence of harm.²³

Based on these findings and collaboration with another GRECC, we designed our program to focus on older adults with polypharmacy, cognitive impairment, high-risk medication usage, and/or a history of high health care use.⁹ Our findings add to the growing body of evidence that a CPS-led transitional care program results in reduced polypharmacy and reduced unnecessary hospital readmissions. Further, our findings have demonstrated the effectiveness of this type of program in a practical, clinical setting with veteran patients.

At the time of project inception, we believed that the majority of our interventions would occur postdischarge. We were somewhat surprised that a major component of GMED was suggested interventions by our pharmacist at the time of admission. We believe that because the CPS made suggestions during admission, we prevented postdischarge ADEs. A frequent intervention corrected the medication reconciliation on file at admission. This finding also was seen in another study by Gleason and colleagues, which examined medication errors at admission for 651 adult medicine inpatients.²⁴ This study found that more than one-third of patients had medication reconciliation errors. Further, older age (≥ 65 years) was associated with increased odds of medication errors in this study.

Of note, a survey of hospital-based pharmacists indicated medication reconciliation is the most important role of the pharmacist in improving care transitions.²⁵ The pharmacists stated that detection of errors at the time of admission is very important. The pharmacists further reported that additional education and counseling for patients with poor understanding of their medications was also important. Our findings support these findings and the use of a pharmacist as part of the medical team to improve medication reconciliation and education.

 

Limitations

A limitation of GMED is that we monitored only admissions to our hospital; therefore, we did not account for any hospitalizations that may have occurred outside the STVHCS. Another limitation is that this was not a randomized controlled trial, and we used a convenience sample of patients who met our criteria for eligibility but were not seen due to time constraints. This introduces potential bias such that patients admitted and discharged on nights or weekends when the CPS was not available were not included in the transitional care program group, and these patients may fundamentally differ from those admitted and discharged Monday through Friday.

However, Khanna and colleagues found that night or weekend admission was not associated with 30-day readmission or other worse outcomes (such as length of stay, 30-day emergency department visit, or intensive care unit transfer) in 857 general medicine admissions at a tertiary care hospital.²⁶ Every effort was made to include as many eligible patients as possible in the transitional program group, and we were able to demonstrate that the patients in the 2 groups were similar. Frailty and prior hospital admission were more prevalent, although not significantly so, in the transitional program group, suggesting that any selection bias would have actually attenuated—not enhanced—the observed effect of the transitional program. Although the transitional program group patients were slightly younger by 0.3 years, they were similar in frailty status and CCI score.

Conclusion

The GMED program was associated with reduced 30-day hospital readmission, discontinuation of unnecessary medications, and corrected medication errors and discrepancies. We propose that a CPS-based transitional care program can improve the quality of care for older patients being discharged to home.

Acknowledgments

Supported by funding from the Veterans Health Administration T21 Non-Institutional Long-Term Care Initiative and VA Office of Rural Health and the San Antonio Geriatrics Research, Education, and Clinical Center. The sponsor did not have any role in the design, methods, data collection, or analysis, and preparation.

Author Contributions

R. Rottman-Sagebiel developed the transitional program concept and design and executed the program implementation, interpretation of data, and preparation of the manuscript. S. Pastewait, N. Cupples, A. Conde, M. Moris, and E. Gonzalez assisted with program design and implementation. S. Cope assisted with interpretation of data and preparation of the manuscript. H. Braden assisted with interpretation of data. D. MacCarthy assisted with data management and statistical analysis. C. Wang and S. Espinoza developed the program concept and design, performed statistical analysis and interpretation of data, and helped prepare the manuscript.

Advances in Geriatrics

Advances in Geriatrics features articles focused on quality improvement/quality assurance initiatives, pilot studies, best practices, research, patient education, and patient-centered care written by health care providers associated with Veteran Health Administration Geriatric Research Education and Clinical Centers. Interested authors can submit articles at editorialmanager.com/fedprac or send a brief 2 to 3 sentence abstract to [email protected] for feedback and publication recommendations.

There will be 53 million older adults in the US by 2020.¹ Increasing age often brings medical comorbidities and prescriptions for multiple medications. An increasing number of prescribed medications combined with age-related changes in the ability to metabolize drugs makes older adults highly vulnerable to adverse drug events (ADEs).² In addition, older adults often have difficulty self-managing their medications and adhering to prescribed regimens.³ As a result, ADEs can lead to poor health outcomes, including hospitalizations, in older adults.

Medication errors and ADEs are particularly common during transitions from hospital to home and can lead to unnecessary readmissions,a major cause of wasteful health care spending in the US.^4,5 More than $25 billion are estimated to be spent annually on hospital readmissions, with Medicare picking up the bill for $17 billion of the total.^6,7 Researchers have found that the majority of ADEs following hospital discharge are either entirely preventable or at least ameliorable (ie, the negative impact or harm resulting from the ADE could have been reduced).⁸

To address these issues, we undertook a clinical demonstration project that implemented a new transitional care program to improve the quality of care for older veterans transitioning from the Audie L. Murphy Veterans Memorial Hospital of the South Texas Veterans Health Care System (STVHCS) in San Antonio to home. The Geriatrics Medication Education at Discharge project (GMED) falls under the auspices of the San Antonio Geriatrics Research Education and Clinical Center (GRECC). Clinical demonstration projects are mandated for US Department of Veterans Affairs (VA) GRECCs to create and promote innovative models of care for older veterans. Dissemination of successful clinical demonstration projects to other VA sites is strongly encouraged. The GMED program was modeled after the Boston GRECC Pharmacological Intervention in Late Life (PILL) program.⁹ The PILL program, which focuses on serving older veterans with cognitive impairment, demonstrated that a postdischarge pharmacist telephone visit for medication reconciliation leads to a reduction in readmission within 60 days of discharge.⁹ The goals of the GMED program were to reduce polypharmacy, inappropriate prescribing and 30-day readmissions.

Methods

The project was conducted when a full-time clinical pharmacy specialist (CPS) was available (May-September 2013 and April 2014-March 2015). This project was approved as nonresearch/quality improvement by the University of Texas Health Science Center Institutional Review Board, which serves the STVHCS. Consent was not required.

Eligibility

Patients were identified via a daily hospital database query of all adults aged ≥ 65 years admitted to the hospital through Inpatient Medicine, Neurology, or Cardiology services within the prior 24 hours. Patients meeting any of the following criteria based on review of the Computerized Patient Record System (CPRS) by the team geriatrician and CPS were considered eligible: (1) aged ≥ 70 years prescribed ≥ 12 outpatient medications; (2) aged ≥ 65 years with a medical history of dementia; (3) aged ≥ 65 years prescribed outpatient medications meeting Beers criteria¹⁰; (4) age ≥ 65 years with ≥ 2 hospital admissions (including the current, index admission) within the past calendar year; or (5) aged ≥ 65 years with ≥ 3 emergency department visits within the past calendar year. For the first polypharmacy criterion, patients aged ≥ 70 years were selected instead of aged ≥ 65 years so as not to exceed the capacity of 1 CPS. Twelve or more medications were used as a cutoff for polypharmacy based on prior quality improvement information gathered from our VA geriatrics clinic examining the average number of medications taken by older veterans in the outpatient setting.

Related: Reducing COPD Readmission Rates: Using a COPD Care Service During Care Transitions

 

Patients were excluded if they were expected to be discharged to any facility where the patient and/or the caregiver were not primarily responsible for medication administration after discharge. Patients who met eligibility criteria but were not seen by the transitional program pharmacist (due to staff capacity) were included in this analysis as a convenience comparison group of patients who received usual care. Patients were not randomized. All communication occurred in English, but this project did not exclude patients with limited English proficiency.

A program support assistant conducted the daily query of the hospital database. The pharmacist conducted the chart review to determine eligibility and delivered the intervention. Eligible patients were selected at random for the intervention with the intention of providing the intervention to as many veterans as possible.

The GMED Intervention

The GMED program included 2 phases, which were both conducted by a CPS with oversight from a senior CPS with geriatric pharmacology expertise and an internist/geriatrician. 

The CPS carrying out the transitional care program was involved in the planning and design of the project and met weekly with the geriatrician. The Figure provides an overview of the intervention.

The first phase of the transitional care program included an individual, face-to-face meeting between the CPS and the patient during the hospitalization. If a veteran was not present in the room at the time of an attempted visit, the pharmacist made 2 additional attempts (3 total) to include the patient in the transitional care program during the hospitalization. 

The CPS performed medication reconciliation and provided medication education regarding administration and usage of the patient’s medications, using an open-ended format.¹¹ The caregiver, if any, was included in the discussion either at the bedside or by telephone following the face-to-face visit with the patient. The CPS communicated recommendations regarding appropriateness of therapy (including any potential barriers to medication adherence) to the medical team (including the attending, resident[s], and interns) in person or by telephone and through documentation in the CPRS. 

The recommendations were based on the clinical expertise of the CPS as well as on guidelines for prescribing in older adults.^10,12 The CPS used a checklist to ensure all components of the intervention were completed (Appendices 1 and 2).

The second component of the transitional care program included a telephone visit within 2 to 3 days of discharge, conducted by the same CPS who performed the face-to-face visit. The purpose of the telephone visit was to perform medication reconciliation, identify and rectify medication errors, provide further patient education, and assist in facilitating appropriate follow-up by the patient’s primary care provider (PCP), if required. At a minimum, veterans were asked a series of questions pertaining to their concerns about medication regimens, receipt of newly prescribed medications at discharge, additional education regarding medications after the CPS encounter during hospitalization, and whether the veteran required assistance with the medication regimen in the home setting. Follow-up questions were asked as needed to clarify and identify potential medication problems. All information from this telephone encounter was communicated to the PCP through CPRS documentation and by telephone as needed.

Related: Initiative to Minimize Pharmaceutical Risk in Older Veterans (IMPROVE) Polypharmacy Clinic

 

Data Collection

A standardized questionnaire was used prospectively for patients in the transitional care program group to assess patient education, primary residence, presence of a caregiver, fall history, medication adherence, and cognitive status (using Mini-Cog).¹³ Additional information (patient age, number of outpatient medications prior to and following the admission, presence of Beers criteria outpatient medications prior to and following the admission, new outpatient prescriptions, and changes to existing prescriptions as a result of the hospitalization) was gathered prospectively from patient interviews or from chart review.

For patients included in the comparison group, a retrospective administrative chart review was conducted to collect information such as age, sex, ethnic group, admission within 1 year prior to index admission, frailty, and Charlson Comorbidity Index (CCI) score, a method of categorizing comorbidities of patients based on the diagnosis codes found in administrative data.¹⁴ Each comorbidity category has an associated weight (from 1 to 6), based on the adjusted risk of mortality or resource use, and the sum of all the weights results in a single comorbidity score for a patient (0 indicates no comorbidities; higher scores predict greater risk of mortality or increased resource use).

We used the index developed from 17 disease categories. The range for CCI was 0 to 25. Frailty was defined as the presence of any of the following frailty-related diagnoses: anemia; fall, head injury, other injury; coagulopathy; electrolyte disturbance; or gait disorder. These diagnoses are either primary frailty characteristics within the frailty phenotype or have been shown in prior studies to be associated with the frailty phenotype.^15-18 While more widely accepted frailty definitions exist,these other definitions require direct examination of the patient and could not be used in this project because we did not directly interact with the comparison group.^16,19 The frailty definition used has been previously identified as a predictor of health care utilization and 30-day readmission in a veteran population.²⁰ Whether or not the CPS detected a postdischarge medication error was recorded. All CPS recommendations were documented.

An index admission was defined as a hospital admission that occurred during the project period. Thirty-day readmission was defined as a hospital admission that occurred within 30 days of the discharge date of an index admission. Each index admission was considered individually for readmission (yes vs no) even if it occurred in the same patient over the project period. A 30-day readmission was not considered an index admission. An admission that occurred after a 30-day readmission was considered a subsequent index admission. Patients who died in the hospital were not included in this analysis, as they would not have participated in the entire intervention.

Statistical Analysis

We compared characteristics between patients who received GMED and patients who never received GMED (comparison group). Generalized estimating equations (GEE) were used to determine whether the rate of 30-day readmission (yes vs no) in the transitional care program group differed from that of the comparison group. In our GEE analysis, we assumed a binomial distribution and the logit link to model the log-odds of readmission as a linear function of transitional care program status (yes vs no) and other covariates, including age, frailty, hospital admission within 1 year prior to the index admission, and CCI score as covariates. Thirty-day readmission status associated with each index admission was coded as 1 for a readmission within 30 days of the discharge date of the index admission, or 0 for no readmission within 30 days.

 

Transitional care program status was determined whether or not the individual received the transitional care program for each index admission. This analysis allowed us to model repeated measures of index admissions as a function of the project period and whether the patient was seen by the GMED CPS during the index admission. The patient identifier was used as a cluster variable in the GEE analysis. Inverse propensity scores of receiving GMED at the index admission were adjusted as weights in the GEE analysis to minimize confounding and, hence, to strengthen the causal interpretation of the effect of the transitional care program. If there was ≥ 1 index admission, the GMED status (yes vs no) at the initial index admission was used as the dependent variable to calculate propensity scores. The propensity scores of transitional care program status were derived from the logistic regression analysis that modeled the log-odds of receiving the transitional care program at the index admission as a linear function of age, CCI, frailty, and prior hospitalization during the 1-year period prior to the index admission.

Related: Development and Implementation of a Geriatric Walking Clinic

Results

The GMED CPS saw 435 patients during the project period; 47 (10.8%) died prior to 30 days and were excluded, leaving 388 patients who received the transitional care program included in this evaluation. 

Another 1,189 patients met the eligibility criteria but were not included and were included in the comparison group. Patients in the transitional care program group were similar to those receiving usual care in the comparison group with regard to sex, ethnic group, frailty status, and CCI score (Table 1).

Data from the CPS-patient interviews and chart reviews were available for 378 of the 388 patients (Table 2). Patients were primarily male, non-Hispanic white, with a high school education. More than half (65%) the patients were admitted for a new diagnosis or clinical condition. 

The majority of patients had diabetes mellitus, and about one-third had chronic obstructive pulmonary disease, congestive heart failure, or cognitive impairment. Although about 60% of patients were prescribed a new medication as a result of the hospital admission, the number of medications from admission to discharge did not differ significantly (15.4 ± 5.5 vs 15.7 ± 5.8; P = .08).

The 30-day readmission rate was 15.6% for the transitional care program group and 21.9% for the comparison group. Three hundred seventy-one patients received the transitional care program only once, 16 patients received the transitional care program twice (ie, they had 2 index admissions during the study period and received the intervention both times), and 1 patient received the transitional care program 3 times.

In an unadjusted GEE model, the odds ratio (OR) for readmission in the transitional care program group was 0.74 (95% CI, 0.54-1.0, P = .06) compared with the usual care group (Table 3). 

After covariate adjustment, the OR for readmission was 0.54 (95% CI, 0.32-0.90, P = .02).

Thirty-five percent of patients had ≥ 1 CPS-recommended change in their treatment at the time of the inpatient admission (Table 4). 

The most common recommendation was discontinuation of at least 1 medication (23.0%), followed by correcting the medication reconciliation list that was on record for the admission (17.8%). Thirty-nine percent of patients had ≥ 1 CPS-recommended change in their treatment at the time of the follow-up phone call. The most common recommendation was to clarify medication instructions for the patient and/or caregiver and provide medication education (33.7%). Other common recommendations were to correct a medication reconciliation (16.9%) and communicate pertinent information about the admission to the PCP (14.5%).

 

Discussion

We developed a transitional care program for hospitalized older veterans to improve the transition from hospital to home. After adjusting for clinical factors, GMED was associated with 26% lower odds of readmission within 30 days of discharge compared with that of the control group. The GMED CPS made changes to the medical regimen both during the inpatient admission as well as after discharge to correct medication errors and educate patients.

In addition, GMED led to a reduction in the number of prescribed medications, which impacts inappropriate polypharmacy—a significant problem in older adults, which contributes to ADEs.²¹ Our intervention was patient centered, as all decisions and education regarding medication management were tailored to each patient, taking into account medical and psychosocial factors.

Studies of similar programs have shown that a pharmacist-based program can improve outcomes in patients transitioning from hospital to home. A meta-analysis of 19 studies that evaluated the effectiveness of pharmacy-led medication reconciliation interventions at the time of a care transition showed that compared with usual care a pharmacist intervention led to reduced medication discrepancies.²² In this meta-analysis, medication discrepancies of higher clinical impact were more easily identified through pharmacy-led interventions than with usual care, suggesting improved safety. Although not all studies have shown a clear reduction in readmission rates or other health care utilization, the addition of clinical pharmacist services in the care of inpatients has generally resulted in improved care with no evidence of harm.²³

Based on these findings and collaboration with another GRECC, we designed our program to focus on older adults with polypharmacy, cognitive impairment, high-risk medication usage, and/or a history of high health care use.⁹ Our findings add to the growing body of evidence that a CPS-led transitional care program results in reduced polypharmacy and reduced unnecessary hospital readmissions. Further, our findings have demonstrated the effectiveness of this type of program in a practical, clinical setting with veteran patients.

At the time of project inception, we believed that the majority of our interventions would occur postdischarge. We were somewhat surprised that a major component of GMED was suggested interventions by our pharmacist at the time of admission. We believe that because the CPS made suggestions during admission, we prevented postdischarge ADEs. A frequent intervention corrected the medication reconciliation on file at admission. This finding also was seen in another study by Gleason and colleagues, which examined medication errors at admission for 651 adult medicine inpatients.²⁴ This study found that more than one-third of patients had medication reconciliation errors. Further, older age (≥ 65 years) was associated with increased odds of medication errors in this study.

Of note, a survey of hospital-based pharmacists indicated medication reconciliation is the most important role of the pharmacist in improving care transitions.²⁵ The pharmacists stated that detection of errors at the time of admission is very important. The pharmacists further reported that additional education and counseling for patients with poor understanding of their medications was also important. Our findings support these findings and the use of a pharmacist as part of the medical team to improve medication reconciliation and education.

 

Limitations

A limitation of GMED is that we monitored only admissions to our hospital; therefore, we did not account for any hospitalizations that may have occurred outside the STVHCS. Another limitation is that this was not a randomized controlled trial, and we used a convenience sample of patients who met our criteria for eligibility but were not seen due to time constraints. This introduces potential bias such that patients admitted and discharged on nights or weekends when the CPS was not available were not included in the transitional care program group, and these patients may fundamentally differ from those admitted and discharged Monday through Friday.

However, Khanna and colleagues found that night or weekend admission was not associated with 30-day readmission or other worse outcomes (such as length of stay, 30-day emergency department visit, or intensive care unit transfer) in 857 general medicine admissions at a tertiary care hospital.²⁶ Every effort was made to include as many eligible patients as possible in the transitional program group, and we were able to demonstrate that the patients in the 2 groups were similar. Frailty and prior hospital admission were more prevalent, although not significantly so, in the transitional program group, suggesting that any selection bias would have actually attenuated—not enhanced—the observed effect of the transitional program. Although the transitional program group patients were slightly younger by 0.3 years, they were similar in frailty status and CCI score.

Conclusion

The GMED program was associated with reduced 30-day hospital readmission, discontinuation of unnecessary medications, and corrected medication errors and discrepancies. We propose that a CPS-based transitional care program can improve the quality of care for older patients being discharged to home.

Acknowledgments

Supported by funding from the Veterans Health Administration T21 Non-Institutional Long-Term Care Initiative and VA Office of Rural Health and the San Antonio Geriatrics Research, Education, and Clinical Center. The sponsor did not have any role in the design, methods, data collection, or analysis, and preparation.

Author Contributions

R. Rottman-Sagebiel developed the transitional program concept and design and executed the program implementation, interpretation of data, and preparation of the manuscript. S. Pastewait, N. Cupples, A. Conde, M. Moris, and E. Gonzalez assisted with program design and implementation. S. Cope assisted with interpretation of data and preparation of the manuscript. H. Braden assisted with interpretation of data. D. MacCarthy assisted with data management and statistical analysis. C. Wang and S. Espinoza developed the program concept and design, performed statistical analysis and interpretation of data, and helped prepare the manuscript.

Advances in Geriatrics

Advances in Geriatrics features articles focused on quality improvement/quality assurance initiatives, pilot studies, best practices, research, patient education, and patient-centered care written by health care providers associated with Veteran Health Administration Geriatric Research Education and Clinical Centers. Interested authors can submit articles at editorialmanager.com/fedprac or send a brief 2 to 3 sentence abstract to [email protected] for feedback and publication recommendations.

There will be 53 million older adults in the US by 2020.¹ Increasing age often brings medical comorbidities and prescriptions for multiple medications. An increasing number of prescribed medications combined with age-related changes in the ability to metabolize drugs makes older adults highly vulnerable to adverse drug events (ADEs).² In addition, older adults often have difficulty self-managing their medications and adhering to prescribed regimens.³ As a result, ADEs can lead to poor health outcomes, including hospitalizations, in older adults.

Medication errors and ADEs are particularly common during transitions from hospital to home and can lead to unnecessary readmissions,a major cause of wasteful health care spending in the US.^4,5 More than $25 billion are estimated to be spent annually on hospital readmissions, with Medicare picking up the bill for $17 billion of the total.^6,7 Researchers have found that the majority of ADEs following hospital discharge are either entirely preventable or at least ameliorable (ie, the negative impact or harm resulting from the ADE could have been reduced).⁸

To address these issues, we undertook a clinical demonstration project that implemented a new transitional care program to improve the quality of care for older veterans transitioning from the Audie L. Murphy Veterans Memorial Hospital of the South Texas Veterans Health Care System (STVHCS) in San Antonio to home. The Geriatrics Medication Education at Discharge project (GMED) falls under the auspices of the San Antonio Geriatrics Research Education and Clinical Center (GRECC). Clinical demonstration projects are mandated for US Department of Veterans Affairs (VA) GRECCs to create and promote innovative models of care for older veterans. Dissemination of successful clinical demonstration projects to other VA sites is strongly encouraged. The GMED program was modeled after the Boston GRECC Pharmacological Intervention in Late Life (PILL) program.⁹ The PILL program, which focuses on serving older veterans with cognitive impairment, demonstrated that a postdischarge pharmacist telephone visit for medication reconciliation leads to a reduction in readmission within 60 days of discharge.⁹ The goals of the GMED program were to reduce polypharmacy, inappropriate prescribing and 30-day readmissions.

Methods

The project was conducted when a full-time clinical pharmacy specialist (CPS) was available (May-September 2013 and April 2014-March 2015). This project was approved as nonresearch/quality improvement by the University of Texas Health Science Center Institutional Review Board, which serves the STVHCS. Consent was not required.

Eligibility

Patients were identified via a daily hospital database query of all adults aged ≥ 65 years admitted to the hospital through Inpatient Medicine, Neurology, or Cardiology services within the prior 24 hours. Patients meeting any of the following criteria based on review of the Computerized Patient Record System (CPRS) by the team geriatrician and CPS were considered eligible: (1) aged ≥ 70 years prescribed ≥ 12 outpatient medications; (2) aged ≥ 65 years with a medical history of dementia; (3) aged ≥ 65 years prescribed outpatient medications meeting Beers criteria¹⁰; (4) age ≥ 65 years with ≥ 2 hospital admissions (including the current, index admission) within the past calendar year; or (5) aged ≥ 65 years with ≥ 3 emergency department visits within the past calendar year. For the first polypharmacy criterion, patients aged ≥ 70 years were selected instead of aged ≥ 65 years so as not to exceed the capacity of 1 CPS. Twelve or more medications were used as a cutoff for polypharmacy based on prior quality improvement information gathered from our VA geriatrics clinic examining the average number of medications taken by older veterans in the outpatient setting.

Related: Reducing COPD Readmission Rates: Using a COPD Care Service During Care Transitions

 

Patients were excluded if they were expected to be discharged to any facility where the patient and/or the caregiver were not primarily responsible for medication administration after discharge. Patients who met eligibility criteria but were not seen by the transitional program pharmacist (due to staff capacity) were included in this analysis as a convenience comparison group of patients who received usual care. Patients were not randomized. All communication occurred in English, but this project did not exclude patients with limited English proficiency.

A program support assistant conducted the daily query of the hospital database. The pharmacist conducted the chart review to determine eligibility and delivered the intervention. Eligible patients were selected at random for the intervention with the intention of providing the intervention to as many veterans as possible.

The GMED Intervention

The GMED program included 2 phases, which were both conducted by a CPS with oversight from a senior CPS with geriatric pharmacology expertise and an internist/geriatrician. 

The CPS carrying out the transitional care program was involved in the planning and design of the project and met weekly with the geriatrician. The Figure provides an overview of the intervention.

The first phase of the transitional care program included an individual, face-to-face meeting between the CPS and the patient during the hospitalization. If a veteran was not present in the room at the time of an attempted visit, the pharmacist made 2 additional attempts (3 total) to include the patient in the transitional care program during the hospitalization. 

The CPS performed medication reconciliation and provided medication education regarding administration and usage of the patient’s medications, using an open-ended format.¹¹ The caregiver, if any, was included in the discussion either at the bedside or by telephone following the face-to-face visit with the patient. The CPS communicated recommendations regarding appropriateness of therapy (including any potential barriers to medication adherence) to the medical team (including the attending, resident[s], and interns) in person or by telephone and through documentation in the CPRS. 

The recommendations were based on the clinical expertise of the CPS as well as on guidelines for prescribing in older adults.^10,12 The CPS used a checklist to ensure all components of the intervention were completed (Appendices 1 and 2).

The second component of the transitional care program included a telephone visit within 2 to 3 days of discharge, conducted by the same CPS who performed the face-to-face visit. The purpose of the telephone visit was to perform medication reconciliation, identify and rectify medication errors, provide further patient education, and assist in facilitating appropriate follow-up by the patient’s primary care provider (PCP), if required. At a minimum, veterans were asked a series of questions pertaining to their concerns about medication regimens, receipt of newly prescribed medications at discharge, additional education regarding medications after the CPS encounter during hospitalization, and whether the veteran required assistance with the medication regimen in the home setting. Follow-up questions were asked as needed to clarify and identify potential medication problems. All information from this telephone encounter was communicated to the PCP through CPRS documentation and by telephone as needed.

Related: Initiative to Minimize Pharmaceutical Risk in Older Veterans (IMPROVE) Polypharmacy Clinic

 

Data Collection

A standardized questionnaire was used prospectively for patients in the transitional care program group to assess patient education, primary residence, presence of a caregiver, fall history, medication adherence, and cognitive status (using Mini-Cog).¹³ Additional information (patient age, number of outpatient medications prior to and following the admission, presence of Beers criteria outpatient medications prior to and following the admission, new outpatient prescriptions, and changes to existing prescriptions as a result of the hospitalization) was gathered prospectively from patient interviews or from chart review.

For patients included in the comparison group, a retrospective administrative chart review was conducted to collect information such as age, sex, ethnic group, admission within 1 year prior to index admission, frailty, and Charlson Comorbidity Index (CCI) score, a method of categorizing comorbidities of patients based on the diagnosis codes found in administrative data.¹⁴ Each comorbidity category has an associated weight (from 1 to 6), based on the adjusted risk of mortality or resource use, and the sum of all the weights results in a single comorbidity score for a patient (0 indicates no comorbidities; higher scores predict greater risk of mortality or increased resource use).

We used the index developed from 17 disease categories. The range for CCI was 0 to 25. Frailty was defined as the presence of any of the following frailty-related diagnoses: anemia; fall, head injury, other injury; coagulopathy; electrolyte disturbance; or gait disorder. These diagnoses are either primary frailty characteristics within the frailty phenotype or have been shown in prior studies to be associated with the frailty phenotype.^15-18 While more widely accepted frailty definitions exist,these other definitions require direct examination of the patient and could not be used in this project because we did not directly interact with the comparison group.^16,19 The frailty definition used has been previously identified as a predictor of health care utilization and 30-day readmission in a veteran population.²⁰ Whether or not the CPS detected a postdischarge medication error was recorded. All CPS recommendations were documented.

An index admission was defined as a hospital admission that occurred during the project period. Thirty-day readmission was defined as a hospital admission that occurred within 30 days of the discharge date of an index admission. Each index admission was considered individually for readmission (yes vs no) even if it occurred in the same patient over the project period. A 30-day readmission was not considered an index admission. An admission that occurred after a 30-day readmission was considered a subsequent index admission. Patients who died in the hospital were not included in this analysis, as they would not have participated in the entire intervention.

Statistical Analysis

We compared characteristics between patients who received GMED and patients who never received GMED (comparison group). Generalized estimating equations (GEE) were used to determine whether the rate of 30-day readmission (yes vs no) in the transitional care program group differed from that of the comparison group. In our GEE analysis, we assumed a binomial distribution and the logit link to model the log-odds of readmission as a linear function of transitional care program status (yes vs no) and other covariates, including age, frailty, hospital admission within 1 year prior to the index admission, and CCI score as covariates. Thirty-day readmission status associated with each index admission was coded as 1 for a readmission within 30 days of the discharge date of the index admission, or 0 for no readmission within 30 days.

 

Transitional care program status was determined whether or not the individual received the transitional care program for each index admission. This analysis allowed us to model repeated measures of index admissions as a function of the project period and whether the patient was seen by the GMED CPS during the index admission. The patient identifier was used as a cluster variable in the GEE analysis. Inverse propensity scores of receiving GMED at the index admission were adjusted as weights in the GEE analysis to minimize confounding and, hence, to strengthen the causal interpretation of the effect of the transitional care program. If there was ≥ 1 index admission, the GMED status (yes vs no) at the initial index admission was used as the dependent variable to calculate propensity scores. The propensity scores of transitional care program status were derived from the logistic regression analysis that modeled the log-odds of receiving the transitional care program at the index admission as a linear function of age, CCI, frailty, and prior hospitalization during the 1-year period prior to the index admission.

Related: Development and Implementation of a Geriatric Walking Clinic

Results

The GMED CPS saw 435 patients during the project period; 47 (10.8%) died prior to 30 days and were excluded, leaving 388 patients who received the transitional care program included in this evaluation. 

Another 1,189 patients met the eligibility criteria but were not included and were included in the comparison group. Patients in the transitional care program group were similar to those receiving usual care in the comparison group with regard to sex, ethnic group, frailty status, and CCI score (Table 1).

Data from the CPS-patient interviews and chart reviews were available for 378 of the 388 patients (Table 2). Patients were primarily male, non-Hispanic white, with a high school education. More than half (65%) the patients were admitted for a new diagnosis or clinical condition. 

The majority of patients had diabetes mellitus, and about one-third had chronic obstructive pulmonary disease, congestive heart failure, or cognitive impairment. Although about 60% of patients were prescribed a new medication as a result of the hospital admission, the number of medications from admission to discharge did not differ significantly (15.4 ± 5.5 vs 15.7 ± 5.8; P = .08).

The 30-day readmission rate was 15.6% for the transitional care program group and 21.9% for the comparison group. Three hundred seventy-one patients received the transitional care program only once, 16 patients received the transitional care program twice (ie, they had 2 index admissions during the study period and received the intervention both times), and 1 patient received the transitional care program 3 times.

In an unadjusted GEE model, the odds ratio (OR) for readmission in the transitional care program group was 0.74 (95% CI, 0.54-1.0, P = .06) compared with the usual care group (Table 3). 

After covariate adjustment, the OR for readmission was 0.54 (95% CI, 0.32-0.90, P = .02).

Thirty-five percent of patients had ≥ 1 CPS-recommended change in their treatment at the time of the inpatient admission (Table 4). 

The most common recommendation was discontinuation of at least 1 medication (23.0%), followed by correcting the medication reconciliation list that was on record for the admission (17.8%). Thirty-nine percent of patients had ≥ 1 CPS-recommended change in their treatment at the time of the follow-up phone call. The most common recommendation was to clarify medication instructions for the patient and/or caregiver and provide medication education (33.7%). Other common recommendations were to correct a medication reconciliation (16.9%) and communicate pertinent information about the admission to the PCP (14.5%).

 

Discussion

We developed a transitional care program for hospitalized older veterans to improve the transition from hospital to home. After adjusting for clinical factors, GMED was associated with 26% lower odds of readmission within 30 days of discharge compared with that of the control group. The GMED CPS made changes to the medical regimen both during the inpatient admission as well as after discharge to correct medication errors and educate patients.

In addition, GMED led to a reduction in the number of prescribed medications, which impacts inappropriate polypharmacy—a significant problem in older adults, which contributes to ADEs.²¹ Our intervention was patient centered, as all decisions and education regarding medication management were tailored to each patient, taking into account medical and psychosocial factors.

Studies of similar programs have shown that a pharmacist-based program can improve outcomes in patients transitioning from hospital to home. A meta-analysis of 19 studies that evaluated the effectiveness of pharmacy-led medication reconciliation interventions at the time of a care transition showed that compared with usual care a pharmacist intervention led to reduced medication discrepancies.²² In this meta-analysis, medication discrepancies of higher clinical impact were more easily identified through pharmacy-led interventions than with usual care, suggesting improved safety. Although not all studies have shown a clear reduction in readmission rates or other health care utilization, the addition of clinical pharmacist services in the care of inpatients has generally resulted in improved care with no evidence of harm.²³

Based on these findings and collaboration with another GRECC, we designed our program to focus on older adults with polypharmacy, cognitive impairment, high-risk medication usage, and/or a history of high health care use.⁹ Our findings add to the growing body of evidence that a CPS-led transitional care program results in reduced polypharmacy and reduced unnecessary hospital readmissions. Further, our findings have demonstrated the effectiveness of this type of program in a practical, clinical setting with veteran patients.

At the time of project inception, we believed that the majority of our interventions would occur postdischarge. We were somewhat surprised that a major component of GMED was suggested interventions by our pharmacist at the time of admission. We believe that because the CPS made suggestions during admission, we prevented postdischarge ADEs. A frequent intervention corrected the medication reconciliation on file at admission. This finding also was seen in another study by Gleason and colleagues, which examined medication errors at admission for 651 adult medicine inpatients.²⁴ This study found that more than one-third of patients had medication reconciliation errors. Further, older age (≥ 65 years) was associated with increased odds of medication errors in this study.

Of note, a survey of hospital-based pharmacists indicated medication reconciliation is the most important role of the pharmacist in improving care transitions.²⁵ The pharmacists stated that detection of errors at the time of admission is very important. The pharmacists further reported that additional education and counseling for patients with poor understanding of their medications was also important. Our findings support these findings and the use of a pharmacist as part of the medical team to improve medication reconciliation and education.

 

Limitations

A limitation of GMED is that we monitored only admissions to our hospital; therefore, we did not account for any hospitalizations that may have occurred outside the STVHCS. Another limitation is that this was not a randomized controlled trial, and we used a convenience sample of patients who met our criteria for eligibility but were not seen due to time constraints. This introduces potential bias such that patients admitted and discharged on nights or weekends when the CPS was not available were not included in the transitional care program group, and these patients may fundamentally differ from those admitted and discharged Monday through Friday.

However, Khanna and colleagues found that night or weekend admission was not associated with 30-day readmission or other worse outcomes (such as length of stay, 30-day emergency department visit, or intensive care unit transfer) in 857 general medicine admissions at a tertiary care hospital.²⁶ Every effort was made to include as many eligible patients as possible in the transitional program group, and we were able to demonstrate that the patients in the 2 groups were similar. Frailty and prior hospital admission were more prevalent, although not significantly so, in the transitional program group, suggesting that any selection bias would have actually attenuated—not enhanced—the observed effect of the transitional program. Although the transitional program group patients were slightly younger by 0.3 years, they were similar in frailty status and CCI score.

Conclusion

The GMED program was associated with reduced 30-day hospital readmission, discontinuation of unnecessary medications, and corrected medication errors and discrepancies. We propose that a CPS-based transitional care program can improve the quality of care for older patients being discharged to home.

Acknowledgments

Supported by funding from the Veterans Health Administration T21 Non-Institutional Long-Term Care Initiative and VA Office of Rural Health and the San Antonio Geriatrics Research, Education, and Clinical Center. The sponsor did not have any role in the design, methods, data collection, or analysis, and preparation.

Author Contributions

R. Rottman-Sagebiel developed the transitional program concept and design and executed the program implementation, interpretation of data, and preparation of the manuscript. S. Pastewait, N. Cupples, A. Conde, M. Moris, and E. Gonzalez assisted with program design and implementation. S. Cope assisted with interpretation of data and preparation of the manuscript. H. Braden assisted with interpretation of data. D. MacCarthy assisted with data management and statistical analysis. C. Wang and S. Espinoza developed the program concept and design, performed statistical analysis and interpretation of data, and helped prepare the manuscript.

Advances in Geriatrics

Advances in Geriatrics features articles focused on quality improvement/quality assurance initiatives, pilot studies, best practices, research, patient education, and patient-centered care written by health care providers associated with Veteran Health Administration Geriatric Research Education and Clinical Centers. Interested authors can submit articles at editorialmanager.com/fedprac or send a brief 2 to 3 sentence abstract to [email protected] for feedback and publication recommendations.

This article is part of a series that illustrates strategies intended to redesign primary care education at the Veterans Health Administration (VHA), using interprofessional workplace learning. All have been implemented in the VA Centers of Excellence in Primary Care Education (CoEPCE). These models embody visionary transformation of clinical and educational environments that have potential for replication and dissemination throughout VA and other primary care clinical educational environments. For an introduction to the series see Klink K. Transforming primary care clinical learning environments to optimize education, outcomes, and satisfaction. Fed Pract. 2018;35(9):8-10.

Background

In 2011, 5 US Department of Veterans Affairs (VA) medical centers (VAMCs) were selected by the Office of Academic Affiliations (OAA) to establish CoEPCE. Part of the VA New Models of Care initiative, the 5 Centers of Excellence (CoE) in Boise, Idaho; Cleveland, Ohio; San Francisco, California; Seattle, Washington; and West Haven, Connecticut, are utilizing VA primary care settings to develop and test innovative approaches to prepare physician residents and students, advanced practice nurse residents and undergraduate nursing students, and other professions of health trainees (eg, pharmacy, social work, psychology, physician assistants [PAs]) for primary care practice in the 21st century.

The Boise CoE developed and implemented a practice-based learning model. Nurse practitioner (NP) students and residents, physician residents, pharmacy residents, psychology interns, and psychology postdoctoral fellows participate in a comprehensive curriculum and practice together for 1 to 3 years. The goal is to produce providers who are able to lead and practice health care in patient-centered primary care and rural care environments. All core curricula are interprofessionally coauthored and cotaught.¹

Methods

In 2015, OAA evaluators reviewed background documents and conducted open-ended interviews with 10 CoE staff, participating trainees, VA faculty, VA facility leadership, and affiliate faculty. In response to questions focused on their experiences, informants described lessons learned, challenges encountered, and benefits for participants, veterans, and the VA. Using a qualitative and quantitative approach, this case study draws on those interviews, surveys of PACT ICU (patient aligned care team interprofessional care update) participants, and analysis of presented patients to examine PACT ICU outcomes.

Related: Hypoglycemia Safety Initiative: Working With PACT Clinical Pharmacy Specialists to Individualize HbA1c Goals

Interprofessional Education and Care

A key CoEPCE aim is to create more clinical opportunities for CoE trainees from a variety of professions to work as a team in ways that anticipate and address the care needs of veterans. This emphasis on workplace learning is needed since most current health care professional education programs lack settings where trainees from different professions can learn and work together with their clinic partners to provide care for patients. With the emphasis on patient-centered medical homes (PCMH) and team-based care in the Affordable Care Act, there is an imperative to develop new training models that address this gap in the preparation of future health professionals. Along with this imperative, clinicians are increasingly required to optimize the health of complex patients who consequently require a multidisciplinary approach to care, particularly high-risk, high-needs patients inappropriately using services, such as frequent emergency department (ED) use.

 

Addressing Complex Needs

In 2010, the Boise VA Medical Center (VAMC) phased in patient aligned care teams (PACTs), the VA-mandated version of PCMH that consist of a physician or NP primary care provider (PCP), a registered nurse (RN) care manager, a licensed vocational nurse (LVN), and a medical support assistant (MSA). 

Research shows that when trainees develop a shared understanding of each other’s skill sets, procedures, and values, patient care is improved.² To facilitate a move toward a care model featuring this shared understanding, the Boise CoE developed an interprofessional, biweekly case conference for the highest risk patients (who are also high utilizers) in the trainee panels. The PACT ICU focuses appropriate resources on patients with the highest need in clinic (eg, high clinic/ED use, chronic pain, multiple comorbidities or psychosocial impediments to care).

The PACT ICU also serves as a venue in which trainees and supervisors from different professions use a patient-centered framework to collaborate on these specific patient cases. The PACT ICU is easily applied to a range of health conditions, such as diabetes mellitus (DM), mental and behavioral health, lack of social support, and delivery system issues, such as ED use. The goals of PACT ICU are to improve the quality and satisfaction of patient care for high-risk patients; encourage appropriate use of health care resources by prioritizing continuity with the PACT team; and enhance interprofessional PACT team function, decreasing PCP and staff stress.

Planning and Implementation

In January 2013, Boise VAMC and the Caldwell, Idaho community-based outpatient clinic (CBOC) implemented PACT ICU. Other nonteaching clinics followed later in the year. Planning and executing PACT ICU took about 10 hours of CoE staff time and required no change in Boise VAMC policy. Program leadership approval was necessary for participation of CoE residents and postdocs. Service-line leadership support was required to protect clinic staff time (nurse care manager, social workers, chaplaincy, and ethics service). At the Caldwell CBOC, the section chief approved the program, and it took about 1 month to initiate a similar version of PACT ICU.

Curriculum

PACT ICU is a workplace clinical activity with roots in the case conference model, specifically the EFECT model (Elicit the narrative of illness, Facilitate a group meeting, Evidence-based gap analysis, Care plan, and Track changes).³ PACT ICU emphasizes a patient-centered approach to developing care plans. Staff review the 5 highest risk patients who are identified by the VA Care Assessment Need (CAN) registry. The CAN is an analytic tool that is available throughout VA and estimates patients’ risk of mortality or hospitalization in the following 90 days. Physician and NP residents choose 1 of the 5 patients to discuss in PACT ICU, while the remaining 4 serve as case-control comparisons to examine long-term patient outcomes. All trainees, faculty, and staff are provided patient data that can be discussed on a secure website.

The PACT ICU combines didactic teaching with workplace learning. For example, the patient’s medical issues may lead to a formal presentation about a topic, such as secondary stroke medication prophylaxis. The workplace learning occurs as the trainees observe and participate in the decision making toward a treatment plan. Interprofessional interactions are role-modeled by clinical faculty and staff during these discussions, and the result impact the patients care. PACT ICU embodies the core domains that shape the CoEPCE curriculum: Interprofessional collaboration (IPC), performance improvement (PI), sustained relationships (SR), and shared decision making (SDM) (Table 1). 

First, trainees learn IPC concepts, such as role clarification and how to work with an interprofessional team. Second, CoE trainees work with data from the CAN registry to develop a care plan that includes a PI objective. Third, the huddle creates SR among team members while improving the quality of the clinic experience as well as SR with patients though increased continuity of care. Last, PACT ICU strengthens communications, understanding of team roles, and system resources to support SDM.

There have been some changes to the PACT ICU model over time. Initially, conferences took place on a weekly basis, to build momentum among the team and to normalize processes. After about 2 years, this decreased to every other week to reduce the time burden. Additionally, the CoE has strengthened the “tracking changes” component of the EFECT model—trainees now present a 5-minute update on the last patient they presented at the prior PACT ICU case conference. Most recently, psychology postdoctoral candidates have instituted preconference calls with patients to further improve the teams understanding of the patients’ perspective and narrative.

Related: Improving Team-Based Care Coordination Delivery and Documentation in the Health Record

 

Resources

The CoE faculty participate in an education program concerning facilitation of interprofessional meetings. All faculty are expected to role model collaborative behavior and mentor trainees on the cases they present.

The PACT ICU requires a room large enough to accommodate at least 12 people. One staff member is required to review patient cases prior to the case conferences (usually about 1 hour of preparation per case conference). Another staff person creates and shares a spreadsheet stored with VA-approved information security with data fields to include the site, PACT ICU date, patient identifier, the CAN score, and a checkbox for whether the patient was selected or part of a control group. Logistic support is required for reserving the room and sending information to presenters. A clinic-based RN with training in interprofessional care case management uses an online schedule to facilitate selection and review of patients. The RN care managers can use a secure management tool to track patient care and outreach.

The RN care manager also needs to be available to attend the PACT ICU case conferences. The Boise CoE built a website to share and standardize resources, such as a presenter schedule, PACT ICU worksheet, and provider questionnaire. (Contact Boise CoE staff for access.) For the initial evaluation of impact, PACT ICU utilized staff data support in the form of a data manager and biostatistician to identify, collect, and analyze data. While optional, this was helpful in refining the approach and demonstrating the impact of the project. Other resource-related requirements for exporting PACT ICU include:

• Staff members, usually RN care managers who coordinate meetings with participants and identify appropriate patients using a registry, such as CAN;
• Meeting facilitators who enforce use of the EFECT model and interprofessional participation to ensure that the interprofessional care plan is carried out by the presenting provider; and
• Interprofessional trainees and faculty who participate in PACT ICU and complete surveys after the first conference.

Monitoring and Assessment

The CoE staff have analyzed the evaluation of PACT ICU with participant self-evaluation, consultation referral patterns, and utilization data, combination of ED and episodic care visits along with hospitalizations).⁴ Pharmacy faculty are exploring the use polypharmacy registries, and psychology will use registries of poor psychosocial function.

Partnerships

Beyond support and engagement from VA CoEPCE and affiliate faculty, PACT ICU has greatly benefited from partnerships with VA facility department and CBOC leadership. The CoEPCE codirector and faculty are in facility committees, such as the PACT Strategic Planning Committee.

Academic affiliates are integral partners who assist with NP student and resident recruitment as well as participate in the planning and refinement of CoEPCE components. PACT ICU supports their mandate to encourage interprofessional teamwork. Faculty members from Gonzaga University (NP affiliate) were involved in the initial discussion on PACT ICU and consider it a “learning laboratory” to work through challenging problems. Gonzaga CoEPCE NP trainees are asked to talk about their PACT ICU experience—its strengths, weaknesses, and challenges—to other Gonzaga students who don’t have exposure to the team experience.

 

Challenges and Solutions

The demand for direct patient care puts pressure on indirect patient care approaches like PACT ICU, which is a time-intensive process with high impact on only a small number of patients. The argument for deploying strategies such as PACT ICU is that managing chronic conditions and encouraging appropriate use of services will improve outcomes for the highest risk patients and save important system resources in the long-run. However, in the short-term, a strong case must be made for the diversion of resources from usual clinic flow, particularly securing recurring blocks of provider time and clinic staff members. In addition, issues about team communication and understanding of appropriate team-based care can overflow to complex patients not presented in the PACT ICU conference.

Providing a facilitated interprofessional venue to discuss how to appropriately coordinate care improves the participation and perceived value of different team members. This approach has led to improved engagement of the team for patients discussed in the PACT ICU, as well as in general care within the participating clinic. With recent changes, the VA does see a workload benefit, and participants get encounter credit through “Non face-to-face prolonged service” codes (CPT 99358/99359), and other possibilities exist related to clinical team conference codes (CPT 99367-8) and complex chronic care management codes (CPT 99487-89). More information on documentation, scheduling and encountering/billing can be found at boisevacoe.org under Products. Other challenges include logistic challenges of finding appropriate patients and distributing sensitive patient information among the team. Additionally, PACT ICU has to wrestle with staffing shortages and episodic participation by some professions that are chronically understaffed. We have addressed many of these problems by receiving buy-in from both leadership and participants. Leadership have allowed time for participation in clinic staff schedules, and each participant has committed to recruiting a substitute in case of a schedule conflict.

Factors for Success

The commitment from the Boise VAMC facility, primary care clinic leadership and affiliated training programs to support staff and trainee participation also has been critical. Additionally, VA facility leadership commitment to ongoing improvements to PACT implementation was a key facilitating factor. Colocation of trainees and clinic staff on the academic PACT team facilitates communication between PACT ICU case conferences, while also supporting team dynamics and sustained relationships with patients. Many of these patients can and will typically seek care using the interdisciplinary trainees, and trainees were motivated to proactively coordinate warm handoffs and other models of transfer of care. PACT ICU has been successfully replicated and sustained at 4 of the 5 CoEPCE sites. The Caldwell CBOC PACT ICU has been up and running for 2 years, and 2 other nonacademic clinics have piloted PACT ICU managed care conferences thus far. Experience regarding the implementation at other academic sites has been published.⁵

Accomplishments and Benefits

There is evidence that PACT ICU is achieving its goals of improving trainee learning and patient outcomes. Trainees are using team skills to provide patient-centered care; trainees are strengthening their overall clinical skills by learning how to improve their responses to high-risk patients. There is also evidence of an increase in interprofessional warm handoffs within the clinic, in which “a clinician directly introduces a patient to another clinician at the time of the patient’s visit, and often a brief encounter between the patient and the health care professional occurs.”^4,6

 

Unlike a traditional didactic with classroom case conferences on interprofessional collaboration, PACT ICU is an opportunity for health care professionals to both learn and work together providing care in a clinic. Moreover, colocation of diverse trainee and faculty professions during the case conferences better prepares trainees to work with other professions and supports all participants to work and communicate as a team.

CoE staff have assessed educational outcomes before and after attendance in PACT ICU. On average, trainees (n = 30) said they found the PACT ICU case conferences to be “very helpful” in developing treatment plans. 

Second, trainees reported increased understanding of the elements that should be considered in developing a care plan and the variety of roles played by team members in providing care to difficult or complex patients (Table 2).

Interprofessional Collaboration

Team building and colocating trainees, faculty, and clinic staff from different professions are a primary focus of PACT ICU. The case conferences are designed to break down silos and foster a team approach to care. Trainees learn how the team works and the ways other professionals can help them take care of the patient. For example, trainees learn early about the contributions and expertise that the pharmacist and psychologist offer in terms of their scope of practice and referral opportunities. Additionally, the RN care manager increases the integration with the PACT clinical team by sharing pertinent information on individual patients. Based on recent trainee survey findings, the CoE has observed a positive change in the team dynamic and trainee ability to interface between professions. PACT ICU participants were more likely to make referrals to other members within the PACT team, such as a warm handoff during a clinic appointment, while they were less likely to seek a consult outside the team.⁷

Clinical Performance

The PACT ICU is an opportunity for a trainee to increase clinical expertise. It provides exposure to a variety of patientsand their care needs and serves as an opportunity to present a high-risk, challenging patient to colleagues of various professions. As of June 2018, 96 physician resident and NP residents have presented complex patient cases.

In addition, a structured forum for discussing patients and their care options strengthens team clinical performance, which supports people to work to the full scope of their practice. Trainees learn and apply team skills, such as communication and the warm handoff.

An interprofessional care plan that is delineated during the meeting supports the trainee and is carried out with help from consultants as needed. These consultants often facilitate plans for a covisit or warm handoff at the next clinic visit, a call from the RN care manager, a virtual clinic appointment, or other nontraditional visits. The clinic staff can get information from PCPs about patient’s plan of care, and PCPs get a more complete picture of a patient’s situation (eg, history, communications, and life-style factors). In addition, surveys of PACT ICU participants suggest the curriculum’s effectiveness at encouraging use of PACT principles within the clinic team and improving appropriate referrals to other members of the PACT team, such as pharmacy and behavioral health.

Patients presented at PACT ICU can be particularly challenging, so there may be a psychological benefit to working with a team to develop a new care plan. The PCPs who feel they are overwhelmed and have exhausted every option step back, get input, and look at the patient in a new light.

Related: Interprofessional Education in Patient Aligned Care Team Primary Care-Mental Health Integration

 

CoEPCE Function

The PACT ICU is flexible and has been adapted to different ambulatory care settings. Currently, PACT ICU case conferences take place at Boise VAMC, the Caldwell CBOCs, and more recently at a smaller CBOC in Burns, Oregon. The PACT ICU structure is slightly different in the clinic settings since the VA primary care clinic has different resources to draw upon, such as hospital and specialty services. The Caldwell CBOC was unable to protect time for PCPs, so it holds a monthly PACT ICU case conference. In addition to continuing expansion in other nonacademic PACT clinics and collaboration with other CoEPCE sites, work is underway to disseminate generalizable principles for interprofessional education, as well as exporting the model for implementation in non-VA settings.

Primary Care Services

The PACT ICU has the potential to create efficiencies in busy clinic settings. It strengthens communication between PCPs and is an opportunity to touch base on the patient, delegate care, and keep track of high-risk patients who might otherwise receive attention only when having an acute problem. Nurses gain a deeper understanding of the patients presented at PACT ICU.

PACT ICU leverages and builds on existing PACT resources in an achievable and sustainable manner benefiting primary care. CoE trainees, who are part of the Silver Team, tap in to the information that team nurses gain from checking in with these high-risk patients biweekly. Moreover, the integration with the Silver Team improves continuity, which helps enhance a patient’s level of trust. The relationship strengthened between primary care and behavioral health at the Caldwell CBOC, providing improved patient access and increased professional sharing.

Patient Outcomes

The PACT ICU provides a forum for input beyond that of the PCP. This feature results in a more robust treatment plan than might be developed by individual PCPs who might not have time to consider options that are outside their scope of practice. Formulating an enriched care plan, informed by multiple professions, has the potential to improve utilization and provide better care.

The Boise VAMC PACT ICU has presented 219 patients as of June 2018. While clinical outcomes data are difficult to collect, the CoE has data on utilization differences on all patients presented at the PACT ICU case conferences. This includes 4 control patients from the same PCP, with similarly high risk based on CAN scores at the time of selection. A single control patient is selected based on gender, closest age, and CAN score; this serves as a comparator for subsequent utilization analysis.

Data from the first 2 years of this study demonstrate that compared with the high-risk control group, there was an increase in contacts with PACT team members, including behavioral health, clinical pharmacists, and nurse care management, persisting up to 6 months following the PACT ICU presentation.⁴ However, PACT ICU participation did not increase the number of visits with the PCP, indicating better engagement with the entire team. Participation was associated with significantly decreased hospitalizations and a trend toward decreased ED visits. These findings persisted when compared with controls in the PCP’s panel with similar CAN scores, making “regression to the mean” often seen in these studies much less likely.

Analysis of patients early in the project suggests the possibility of improved glycemic control in patients with DM and improved blood pressure control in hypertensive patients presented at the PACT ICU compared with that of non-PACT ICU patients.⁸ Another potential benefit includes better team-based coordination. Because the patient now has a team focusing on care, this new dynamic results in improving outreach, identifying patients who could receive care by a telephone, and better preparing team members to establish rapport when the patient calls or comes in for a visit.

 

The Future

In stage 2 of the CoEPCE program, a multi-site trial of PACT ICU was completed to better understand which elements are critical to success, with the goal of facilitating broader exportability.⁵ The trial focused on 3 intertwined elements: structure, delivery, and evaluation. Using local implementation and the multisite trial, the most effective practices have been documented as part of an implementation kit, available at boisevacoe.org. The goal of the implementation kit is to facilitate step-by-step implementation of PACT ICU to other settings beyond the multisite study. Since the open-ended structure of PACT ICU enables accommodating different professions and specialties beyond the model’s Boise VAMC participants, it could be easily adapted to potentially support a variety of implementations elsewhere (Appendix).

Another opportunity for expansion is increased patient involvement. Currently, PACT ICU patients have the opportunity to review and ask questions about their multidisciplinary care plans before starting. 

Patients know they have a team working on their behalf, but there are opportunities for more follow-up, including presenting patients who are seen by other providers outside the CoE, such as the attending physician who may also have challenging patients. Long-term goals include developing sustainable formats for supporting PACT ICU in nonacademic settings as part of “routine care” and evaluating the implementation and impact on patient care, satisfaction, and utilization.

This article is part of a series that illustrates strategies intended to redesign primary care education at the Veterans Health Administration (VHA), using interprofessional workplace learning. All have been implemented in the VA Centers of Excellence in Primary Care Education (CoEPCE). These models embody visionary transformation of clinical and educational environments that have potential for replication and dissemination throughout VA and other primary care clinical educational environments. For an introduction to the series see Klink K. Transforming primary care clinical learning environments to optimize education, outcomes, and satisfaction. Fed Pract. 2018;35(9):8-10.

Background

In 2011, 5 US Department of Veterans Affairs (VA) medical centers (VAMCs) were selected by the Office of Academic Affiliations (OAA) to establish CoEPCE. Part of the VA New Models of Care initiative, the 5 Centers of Excellence (CoE) in Boise, Idaho; Cleveland, Ohio; San Francisco, California; Seattle, Washington; and West Haven, Connecticut, are utilizing VA primary care settings to develop and test innovative approaches to prepare physician residents and students, advanced practice nurse residents and undergraduate nursing students, and other professions of health trainees (eg, pharmacy, social work, psychology, physician assistants [PAs]) for primary care practice in the 21st century.

The Boise CoE developed and implemented a practice-based learning model. Nurse practitioner (NP) students and residents, physician residents, pharmacy residents, psychology interns, and psychology postdoctoral fellows participate in a comprehensive curriculum and practice together for 1 to 3 years. The goal is to produce providers who are able to lead and practice health care in patient-centered primary care and rural care environments. All core curricula are interprofessionally coauthored and cotaught.¹

Methods

In 2015, OAA evaluators reviewed background documents and conducted open-ended interviews with 10 CoE staff, participating trainees, VA faculty, VA facility leadership, and affiliate faculty. In response to questions focused on their experiences, informants described lessons learned, challenges encountered, and benefits for participants, veterans, and the VA. Using a qualitative and quantitative approach, this case study draws on those interviews, surveys of PACT ICU (patient aligned care team interprofessional care update) participants, and analysis of presented patients to examine PACT ICU outcomes.

Related: Hypoglycemia Safety Initiative: Working With PACT Clinical Pharmacy Specialists to Individualize HbA1c Goals

Interprofessional Education and Care

A key CoEPCE aim is to create more clinical opportunities for CoE trainees from a variety of professions to work as a team in ways that anticipate and address the care needs of veterans. This emphasis on workplace learning is needed since most current health care professional education programs lack settings where trainees from different professions can learn and work together with their clinic partners to provide care for patients. With the emphasis on patient-centered medical homes (PCMH) and team-based care in the Affordable Care Act, there is an imperative to develop new training models that address this gap in the preparation of future health professionals. Along with this imperative, clinicians are increasingly required to optimize the health of complex patients who consequently require a multidisciplinary approach to care, particularly high-risk, high-needs patients inappropriately using services, such as frequent emergency department (ED) use.

 

Addressing Complex Needs

In 2010, the Boise VA Medical Center (VAMC) phased in patient aligned care teams (PACTs), the VA-mandated version of PCMH that consist of a physician or NP primary care provider (PCP), a registered nurse (RN) care manager, a licensed vocational nurse (LVN), and a medical support assistant (MSA). 

Research shows that when trainees develop a shared understanding of each other’s skill sets, procedures, and values, patient care is improved.² To facilitate a move toward a care model featuring this shared understanding, the Boise CoE developed an interprofessional, biweekly case conference for the highest risk patients (who are also high utilizers) in the trainee panels. The PACT ICU focuses appropriate resources on patients with the highest need in clinic (eg, high clinic/ED use, chronic pain, multiple comorbidities or psychosocial impediments to care).

The PACT ICU also serves as a venue in which trainees and supervisors from different professions use a patient-centered framework to collaborate on these specific patient cases. The PACT ICU is easily applied to a range of health conditions, such as diabetes mellitus (DM), mental and behavioral health, lack of social support, and delivery system issues, such as ED use. The goals of PACT ICU are to improve the quality and satisfaction of patient care for high-risk patients; encourage appropriate use of health care resources by prioritizing continuity with the PACT team; and enhance interprofessional PACT team function, decreasing PCP and staff stress.

Planning and Implementation

In January 2013, Boise VAMC and the Caldwell, Idaho community-based outpatient clinic (CBOC) implemented PACT ICU. Other nonteaching clinics followed later in the year. Planning and executing PACT ICU took about 10 hours of CoE staff time and required no change in Boise VAMC policy. Program leadership approval was necessary for participation of CoE residents and postdocs. Service-line leadership support was required to protect clinic staff time (nurse care manager, social workers, chaplaincy, and ethics service). At the Caldwell CBOC, the section chief approved the program, and it took about 1 month to initiate a similar version of PACT ICU.

Curriculum

PACT ICU is a workplace clinical activity with roots in the case conference model, specifically the EFECT model (Elicit the narrative of illness, Facilitate a group meeting, Evidence-based gap analysis, Care plan, and Track changes).³ PACT ICU emphasizes a patient-centered approach to developing care plans. Staff review the 5 highest risk patients who are identified by the VA Care Assessment Need (CAN) registry. The CAN is an analytic tool that is available throughout VA and estimates patients’ risk of mortality or hospitalization in the following 90 days. Physician and NP residents choose 1 of the 5 patients to discuss in PACT ICU, while the remaining 4 serve as case-control comparisons to examine long-term patient outcomes. All trainees, faculty, and staff are provided patient data that can be discussed on a secure website.

The PACT ICU combines didactic teaching with workplace learning. For example, the patient’s medical issues may lead to a formal presentation about a topic, such as secondary stroke medication prophylaxis. The workplace learning occurs as the trainees observe and participate in the decision making toward a treatment plan. Interprofessional interactions are role-modeled by clinical faculty and staff during these discussions, and the result impact the patients care. PACT ICU embodies the core domains that shape the CoEPCE curriculum: Interprofessional collaboration (IPC), performance improvement (PI), sustained relationships (SR), and shared decision making (SDM) (Table 1). 

First, trainees learn IPC concepts, such as role clarification and how to work with an interprofessional team. Second, CoE trainees work with data from the CAN registry to develop a care plan that includes a PI objective. Third, the huddle creates SR among team members while improving the quality of the clinic experience as well as SR with patients though increased continuity of care. Last, PACT ICU strengthens communications, understanding of team roles, and system resources to support SDM.

There have been some changes to the PACT ICU model over time. Initially, conferences took place on a weekly basis, to build momentum among the team and to normalize processes. After about 2 years, this decreased to every other week to reduce the time burden. Additionally, the CoE has strengthened the “tracking changes” component of the EFECT model—trainees now present a 5-minute update on the last patient they presented at the prior PACT ICU case conference. Most recently, psychology postdoctoral candidates have instituted preconference calls with patients to further improve the teams understanding of the patients’ perspective and narrative.

Related: Improving Team-Based Care Coordination Delivery and Documentation in the Health Record

 

Resources

The CoE faculty participate in an education program concerning facilitation of interprofessional meetings. All faculty are expected to role model collaborative behavior and mentor trainees on the cases they present.

The PACT ICU requires a room large enough to accommodate at least 12 people. One staff member is required to review patient cases prior to the case conferences (usually about 1 hour of preparation per case conference). Another staff person creates and shares a spreadsheet stored with VA-approved information security with data fields to include the site, PACT ICU date, patient identifier, the CAN score, and a checkbox for whether the patient was selected or part of a control group. Logistic support is required for reserving the room and sending information to presenters. A clinic-based RN with training in interprofessional care case management uses an online schedule to facilitate selection and review of patients. The RN care managers can use a secure management tool to track patient care and outreach.

The RN care manager also needs to be available to attend the PACT ICU case conferences. The Boise CoE built a website to share and standardize resources, such as a presenter schedule, PACT ICU worksheet, and provider questionnaire. (Contact Boise CoE staff for access.) For the initial evaluation of impact, PACT ICU utilized staff data support in the form of a data manager and biostatistician to identify, collect, and analyze data. While optional, this was helpful in refining the approach and demonstrating the impact of the project. Other resource-related requirements for exporting PACT ICU include:

• Staff members, usually RN care managers who coordinate meetings with participants and identify appropriate patients using a registry, such as CAN;
• Meeting facilitators who enforce use of the EFECT model and interprofessional participation to ensure that the interprofessional care plan is carried out by the presenting provider; and
• Interprofessional trainees and faculty who participate in PACT ICU and complete surveys after the first conference.

Monitoring and Assessment

The CoE staff have analyzed the evaluation of PACT ICU with participant self-evaluation, consultation referral patterns, and utilization data, combination of ED and episodic care visits along with hospitalizations).⁴ Pharmacy faculty are exploring the use polypharmacy registries, and psychology will use registries of poor psychosocial function.

Partnerships

Beyond support and engagement from VA CoEPCE and affiliate faculty, PACT ICU has greatly benefited from partnerships with VA facility department and CBOC leadership. The CoEPCE codirector and faculty are in facility committees, such as the PACT Strategic Planning Committee.

Academic affiliates are integral partners who assist with NP student and resident recruitment as well as participate in the planning and refinement of CoEPCE components. PACT ICU supports their mandate to encourage interprofessional teamwork. Faculty members from Gonzaga University (NP affiliate) were involved in the initial discussion on PACT ICU and consider it a “learning laboratory” to work through challenging problems. Gonzaga CoEPCE NP trainees are asked to talk about their PACT ICU experience—its strengths, weaknesses, and challenges—to other Gonzaga students who don’t have exposure to the team experience.

 

Challenges and Solutions

The demand for direct patient care puts pressure on indirect patient care approaches like PACT ICU, which is a time-intensive process with high impact on only a small number of patients. The argument for deploying strategies such as PACT ICU is that managing chronic conditions and encouraging appropriate use of services will improve outcomes for the highest risk patients and save important system resources in the long-run. However, in the short-term, a strong case must be made for the diversion of resources from usual clinic flow, particularly securing recurring blocks of provider time and clinic staff members. In addition, issues about team communication and understanding of appropriate team-based care can overflow to complex patients not presented in the PACT ICU conference.

Providing a facilitated interprofessional venue to discuss how to appropriately coordinate care improves the participation and perceived value of different team members. This approach has led to improved engagement of the team for patients discussed in the PACT ICU, as well as in general care within the participating clinic. With recent changes, the VA does see a workload benefit, and participants get encounter credit through “Non face-to-face prolonged service” codes (CPT 99358/99359), and other possibilities exist related to clinical team conference codes (CPT 99367-8) and complex chronic care management codes (CPT 99487-89). More information on documentation, scheduling and encountering/billing can be found at boisevacoe.org under Products. Other challenges include logistic challenges of finding appropriate patients and distributing sensitive patient information among the team. Additionally, PACT ICU has to wrestle with staffing shortages and episodic participation by some professions that are chronically understaffed. We have addressed many of these problems by receiving buy-in from both leadership and participants. Leadership have allowed time for participation in clinic staff schedules, and each participant has committed to recruiting a substitute in case of a schedule conflict.

Factors for Success

The commitment from the Boise VAMC facility, primary care clinic leadership and affiliated training programs to support staff and trainee participation also has been critical. Additionally, VA facility leadership commitment to ongoing improvements to PACT implementation was a key facilitating factor. Colocation of trainees and clinic staff on the academic PACT team facilitates communication between PACT ICU case conferences, while also supporting team dynamics and sustained relationships with patients. Many of these patients can and will typically seek care using the interdisciplinary trainees, and trainees were motivated to proactively coordinate warm handoffs and other models of transfer of care. PACT ICU has been successfully replicated and sustained at 4 of the 5 CoEPCE sites. The Caldwell CBOC PACT ICU has been up and running for 2 years, and 2 other nonacademic clinics have piloted PACT ICU managed care conferences thus far. Experience regarding the implementation at other academic sites has been published.⁵

Accomplishments and Benefits

There is evidence that PACT ICU is achieving its goals of improving trainee learning and patient outcomes. Trainees are using team skills to provide patient-centered care; trainees are strengthening their overall clinical skills by learning how to improve their responses to high-risk patients. There is also evidence of an increase in interprofessional warm handoffs within the clinic, in which “a clinician directly introduces a patient to another clinician at the time of the patient’s visit, and often a brief encounter between the patient and the health care professional occurs.”^4,6

 

Unlike a traditional didactic with classroom case conferences on interprofessional collaboration, PACT ICU is an opportunity for health care professionals to both learn and work together providing care in a clinic. Moreover, colocation of diverse trainee and faculty professions during the case conferences better prepares trainees to work with other professions and supports all participants to work and communicate as a team.

CoE staff have assessed educational outcomes before and after attendance in PACT ICU. On average, trainees (n = 30) said they found the PACT ICU case conferences to be “very helpful” in developing treatment plans. 

Second, trainees reported increased understanding of the elements that should be considered in developing a care plan and the variety of roles played by team members in providing care to difficult or complex patients (Table 2).

Interprofessional Collaboration

Team building and colocating trainees, faculty, and clinic staff from different professions are a primary focus of PACT ICU. The case conferences are designed to break down silos and foster a team approach to care. Trainees learn how the team works and the ways other professionals can help them take care of the patient. For example, trainees learn early about the contributions and expertise that the pharmacist and psychologist offer in terms of their scope of practice and referral opportunities. Additionally, the RN care manager increases the integration with the PACT clinical team by sharing pertinent information on individual patients. Based on recent trainee survey findings, the CoE has observed a positive change in the team dynamic and trainee ability to interface between professions. PACT ICU participants were more likely to make referrals to other members within the PACT team, such as a warm handoff during a clinic appointment, while they were less likely to seek a consult outside the team.⁷

Clinical Performance

The PACT ICU is an opportunity for a trainee to increase clinical expertise. It provides exposure to a variety of patientsand their care needs and serves as an opportunity to present a high-risk, challenging patient to colleagues of various professions. As of June 2018, 96 physician resident and NP residents have presented complex patient cases.

In addition, a structured forum for discussing patients and their care options strengthens team clinical performance, which supports people to work to the full scope of their practice. Trainees learn and apply team skills, such as communication and the warm handoff.

An interprofessional care plan that is delineated during the meeting supports the trainee and is carried out with help from consultants as needed. These consultants often facilitate plans for a covisit or warm handoff at the next clinic visit, a call from the RN care manager, a virtual clinic appointment, or other nontraditional visits. The clinic staff can get information from PCPs about patient’s plan of care, and PCPs get a more complete picture of a patient’s situation (eg, history, communications, and life-style factors). In addition, surveys of PACT ICU participants suggest the curriculum’s effectiveness at encouraging use of PACT principles within the clinic team and improving appropriate referrals to other members of the PACT team, such as pharmacy and behavioral health.

Patients presented at PACT ICU can be particularly challenging, so there may be a psychological benefit to working with a team to develop a new care plan. The PCPs who feel they are overwhelmed and have exhausted every option step back, get input, and look at the patient in a new light.

Related: Interprofessional Education in Patient Aligned Care Team Primary Care-Mental Health Integration

 

CoEPCE Function

The PACT ICU is flexible and has been adapted to different ambulatory care settings. Currently, PACT ICU case conferences take place at Boise VAMC, the Caldwell CBOCs, and more recently at a smaller CBOC in Burns, Oregon. The PACT ICU structure is slightly different in the clinic settings since the VA primary care clinic has different resources to draw upon, such as hospital and specialty services. The Caldwell CBOC was unable to protect time for PCPs, so it holds a monthly PACT ICU case conference. In addition to continuing expansion in other nonacademic PACT clinics and collaboration with other CoEPCE sites, work is underway to disseminate generalizable principles for interprofessional education, as well as exporting the model for implementation in non-VA settings.

Primary Care Services

The PACT ICU has the potential to create efficiencies in busy clinic settings. It strengthens communication between PCPs and is an opportunity to touch base on the patient, delegate care, and keep track of high-risk patients who might otherwise receive attention only when having an acute problem. Nurses gain a deeper understanding of the patients presented at PACT ICU.

PACT ICU leverages and builds on existing PACT resources in an achievable and sustainable manner benefiting primary care. CoE trainees, who are part of the Silver Team, tap in to the information that team nurses gain from checking in with these high-risk patients biweekly. Moreover, the integration with the Silver Team improves continuity, which helps enhance a patient’s level of trust. The relationship strengthened between primary care and behavioral health at the Caldwell CBOC, providing improved patient access and increased professional sharing.

Patient Outcomes

The PACT ICU provides a forum for input beyond that of the PCP. This feature results in a more robust treatment plan than might be developed by individual PCPs who might not have time to consider options that are outside their scope of practice. Formulating an enriched care plan, informed by multiple professions, has the potential to improve utilization and provide better care.

The Boise VAMC PACT ICU has presented 219 patients as of June 2018. While clinical outcomes data are difficult to collect, the CoE has data on utilization differences on all patients presented at the PACT ICU case conferences. This includes 4 control patients from the same PCP, with similarly high risk based on CAN scores at the time of selection. A single control patient is selected based on gender, closest age, and CAN score; this serves as a comparator for subsequent utilization analysis.

Data from the first 2 years of this study demonstrate that compared with the high-risk control group, there was an increase in contacts with PACT team members, including behavioral health, clinical pharmacists, and nurse care management, persisting up to 6 months following the PACT ICU presentation.⁴ However, PACT ICU participation did not increase the number of visits with the PCP, indicating better engagement with the entire team. Participation was associated with significantly decreased hospitalizations and a trend toward decreased ED visits. These findings persisted when compared with controls in the PCP’s panel with similar CAN scores, making “regression to the mean” often seen in these studies much less likely.

Analysis of patients early in the project suggests the possibility of improved glycemic control in patients with DM and improved blood pressure control in hypertensive patients presented at the PACT ICU compared with that of non-PACT ICU patients.⁸ Another potential benefit includes better team-based coordination. Because the patient now has a team focusing on care, this new dynamic results in improving outreach, identifying patients who could receive care by a telephone, and better preparing team members to establish rapport when the patient calls or comes in for a visit.

 

The Future

In stage 2 of the CoEPCE program, a multi-site trial of PACT ICU was completed to better understand which elements are critical to success, with the goal of facilitating broader exportability.⁵ The trial focused on 3 intertwined elements: structure, delivery, and evaluation. Using local implementation and the multisite trial, the most effective practices have been documented as part of an implementation kit, available at boisevacoe.org. The goal of the implementation kit is to facilitate step-by-step implementation of PACT ICU to other settings beyond the multisite study. Since the open-ended structure of PACT ICU enables accommodating different professions and specialties beyond the model’s Boise VAMC participants, it could be easily adapted to potentially support a variety of implementations elsewhere (Appendix).

Another opportunity for expansion is increased patient involvement. Currently, PACT ICU patients have the opportunity to review and ask questions about their multidisciplinary care plans before starting. 

Patients know they have a team working on their behalf, but there are opportunities for more follow-up, including presenting patients who are seen by other providers outside the CoE, such as the attending physician who may also have challenging patients. Long-term goals include developing sustainable formats for supporting PACT ICU in nonacademic settings as part of “routine care” and evaluating the implementation and impact on patient care, satisfaction, and utilization.

This article is part of a series that illustrates strategies intended to redesign primary care education at the Veterans Health Administration (VHA), using interprofessional workplace learning. All have been implemented in the VA Centers of Excellence in Primary Care Education (CoEPCE). These models embody visionary transformation of clinical and educational environments that have potential for replication and dissemination throughout VA and other primary care clinical educational environments. For an introduction to the series see Klink K. Transforming primary care clinical learning environments to optimize education, outcomes, and satisfaction. Fed Pract. 2018;35(9):8-10.

Background

In 2011, 5 US Department of Veterans Affairs (VA) medical centers (VAMCs) were selected by the Office of Academic Affiliations (OAA) to establish CoEPCE. Part of the VA New Models of Care initiative, the 5 Centers of Excellence (CoE) in Boise, Idaho; Cleveland, Ohio; San Francisco, California; Seattle, Washington; and West Haven, Connecticut, are utilizing VA primary care settings to develop and test innovative approaches to prepare physician residents and students, advanced practice nurse residents and undergraduate nursing students, and other professions of health trainees (eg, pharmacy, social work, psychology, physician assistants [PAs]) for primary care practice in the 21st century.

The Boise CoE developed and implemented a practice-based learning model. Nurse practitioner (NP) students and residents, physician residents, pharmacy residents, psychology interns, and psychology postdoctoral fellows participate in a comprehensive curriculum and practice together for 1 to 3 years. The goal is to produce providers who are able to lead and practice health care in patient-centered primary care and rural care environments. All core curricula are interprofessionally coauthored and cotaught.¹

Methods

In 2015, OAA evaluators reviewed background documents and conducted open-ended interviews with 10 CoE staff, participating trainees, VA faculty, VA facility leadership, and affiliate faculty. In response to questions focused on their experiences, informants described lessons learned, challenges encountered, and benefits for participants, veterans, and the VA. Using a qualitative and quantitative approach, this case study draws on those interviews, surveys of PACT ICU (patient aligned care team interprofessional care update) participants, and analysis of presented patients to examine PACT ICU outcomes.

Related: Hypoglycemia Safety Initiative: Working With PACT Clinical Pharmacy Specialists to Individualize HbA1c Goals

Interprofessional Education and Care

A key CoEPCE aim is to create more clinical opportunities for CoE trainees from a variety of professions to work as a team in ways that anticipate and address the care needs of veterans. This emphasis on workplace learning is needed since most current health care professional education programs lack settings where trainees from different professions can learn and work together with their clinic partners to provide care for patients. With the emphasis on patient-centered medical homes (PCMH) and team-based care in the Affordable Care Act, there is an imperative to develop new training models that address this gap in the preparation of future health professionals. Along with this imperative, clinicians are increasingly required to optimize the health of complex patients who consequently require a multidisciplinary approach to care, particularly high-risk, high-needs patients inappropriately using services, such as frequent emergency department (ED) use.

 

Addressing Complex Needs

In 2010, the Boise VA Medical Center (VAMC) phased in patient aligned care teams (PACTs), the VA-mandated version of PCMH that consist of a physician or NP primary care provider (PCP), a registered nurse (RN) care manager, a licensed vocational nurse (LVN), and a medical support assistant (MSA). 

Research shows that when trainees develop a shared understanding of each other’s skill sets, procedures, and values, patient care is improved.² To facilitate a move toward a care model featuring this shared understanding, the Boise CoE developed an interprofessional, biweekly case conference for the highest risk patients (who are also high utilizers) in the trainee panels. The PACT ICU focuses appropriate resources on patients with the highest need in clinic (eg, high clinic/ED use, chronic pain, multiple comorbidities or psychosocial impediments to care).

The PACT ICU also serves as a venue in which trainees and supervisors from different professions use a patient-centered framework to collaborate on these specific patient cases. The PACT ICU is easily applied to a range of health conditions, such as diabetes mellitus (DM), mental and behavioral health, lack of social support, and delivery system issues, such as ED use. The goals of PACT ICU are to improve the quality and satisfaction of patient care for high-risk patients; encourage appropriate use of health care resources by prioritizing continuity with the PACT team; and enhance interprofessional PACT team function, decreasing PCP and staff stress.

Planning and Implementation

In January 2013, Boise VAMC and the Caldwell, Idaho community-based outpatient clinic (CBOC) implemented PACT ICU. Other nonteaching clinics followed later in the year. Planning and executing PACT ICU took about 10 hours of CoE staff time and required no change in Boise VAMC policy. Program leadership approval was necessary for participation of CoE residents and postdocs. Service-line leadership support was required to protect clinic staff time (nurse care manager, social workers, chaplaincy, and ethics service). At the Caldwell CBOC, the section chief approved the program, and it took about 1 month to initiate a similar version of PACT ICU.

Curriculum

PACT ICU is a workplace clinical activity with roots in the case conference model, specifically the EFECT model (Elicit the narrative of illness, Facilitate a group meeting, Evidence-based gap analysis, Care plan, and Track changes).³ PACT ICU emphasizes a patient-centered approach to developing care plans. Staff review the 5 highest risk patients who are identified by the VA Care Assessment Need (CAN) registry. The CAN is an analytic tool that is available throughout VA and estimates patients’ risk of mortality or hospitalization in the following 90 days. Physician and NP residents choose 1 of the 5 patients to discuss in PACT ICU, while the remaining 4 serve as case-control comparisons to examine long-term patient outcomes. All trainees, faculty, and staff are provided patient data that can be discussed on a secure website.

The PACT ICU combines didactic teaching with workplace learning. For example, the patient’s medical issues may lead to a formal presentation about a topic, such as secondary stroke medication prophylaxis. The workplace learning occurs as the trainees observe and participate in the decision making toward a treatment plan. Interprofessional interactions are role-modeled by clinical faculty and staff during these discussions, and the result impact the patients care. PACT ICU embodies the core domains that shape the CoEPCE curriculum: Interprofessional collaboration (IPC), performance improvement (PI), sustained relationships (SR), and shared decision making (SDM) (Table 1). 

First, trainees learn IPC concepts, such as role clarification and how to work with an interprofessional team. Second, CoE trainees work with data from the CAN registry to develop a care plan that includes a PI objective. Third, the huddle creates SR among team members while improving the quality of the clinic experience as well as SR with patients though increased continuity of care. Last, PACT ICU strengthens communications, understanding of team roles, and system resources to support SDM.

There have been some changes to the PACT ICU model over time. Initially, conferences took place on a weekly basis, to build momentum among the team and to normalize processes. After about 2 years, this decreased to every other week to reduce the time burden. Additionally, the CoE has strengthened the “tracking changes” component of the EFECT model—trainees now present a 5-minute update on the last patient they presented at the prior PACT ICU case conference. Most recently, psychology postdoctoral candidates have instituted preconference calls with patients to further improve the teams understanding of the patients’ perspective and narrative.

Related: Improving Team-Based Care Coordination Delivery and Documentation in the Health Record

 

Resources

The CoE faculty participate in an education program concerning facilitation of interprofessional meetings. All faculty are expected to role model collaborative behavior and mentor trainees on the cases they present.

The PACT ICU requires a room large enough to accommodate at least 12 people. One staff member is required to review patient cases prior to the case conferences (usually about 1 hour of preparation per case conference). Another staff person creates and shares a spreadsheet stored with VA-approved information security with data fields to include the site, PACT ICU date, patient identifier, the CAN score, and a checkbox for whether the patient was selected or part of a control group. Logistic support is required for reserving the room and sending information to presenters. A clinic-based RN with training in interprofessional care case management uses an online schedule to facilitate selection and review of patients. The RN care managers can use a secure management tool to track patient care and outreach.

The RN care manager also needs to be available to attend the PACT ICU case conferences. The Boise CoE built a website to share and standardize resources, such as a presenter schedule, PACT ICU worksheet, and provider questionnaire. (Contact Boise CoE staff for access.) For the initial evaluation of impact, PACT ICU utilized staff data support in the form of a data manager and biostatistician to identify, collect, and analyze data. While optional, this was helpful in refining the approach and demonstrating the impact of the project. Other resource-related requirements for exporting PACT ICU include:

• Staff members, usually RN care managers who coordinate meetings with participants and identify appropriate patients using a registry, such as CAN;
• Meeting facilitators who enforce use of the EFECT model and interprofessional participation to ensure that the interprofessional care plan is carried out by the presenting provider; and
• Interprofessional trainees and faculty who participate in PACT ICU and complete surveys after the first conference.

Monitoring and Assessment

The CoE staff have analyzed the evaluation of PACT ICU with participant self-evaluation, consultation referral patterns, and utilization data, combination of ED and episodic care visits along with hospitalizations).⁴ Pharmacy faculty are exploring the use polypharmacy registries, and psychology will use registries of poor psychosocial function.

Partnerships

Beyond support and engagement from VA CoEPCE and affiliate faculty, PACT ICU has greatly benefited from partnerships with VA facility department and CBOC leadership. The CoEPCE codirector and faculty are in facility committees, such as the PACT Strategic Planning Committee.

Academic affiliates are integral partners who assist with NP student and resident recruitment as well as participate in the planning and refinement of CoEPCE components. PACT ICU supports their mandate to encourage interprofessional teamwork. Faculty members from Gonzaga University (NP affiliate) were involved in the initial discussion on PACT ICU and consider it a “learning laboratory” to work through challenging problems. Gonzaga CoEPCE NP trainees are asked to talk about their PACT ICU experience—its strengths, weaknesses, and challenges—to other Gonzaga students who don’t have exposure to the team experience.

 

Challenges and Solutions

The demand for direct patient care puts pressure on indirect patient care approaches like PACT ICU, which is a time-intensive process with high impact on only a small number of patients. The argument for deploying strategies such as PACT ICU is that managing chronic conditions and encouraging appropriate use of services will improve outcomes for the highest risk patients and save important system resources in the long-run. However, in the short-term, a strong case must be made for the diversion of resources from usual clinic flow, particularly securing recurring blocks of provider time and clinic staff members. In addition, issues about team communication and understanding of appropriate team-based care can overflow to complex patients not presented in the PACT ICU conference.

Providing a facilitated interprofessional venue to discuss how to appropriately coordinate care improves the participation and perceived value of different team members. This approach has led to improved engagement of the team for patients discussed in the PACT ICU, as well as in general care within the participating clinic. With recent changes, the VA does see a workload benefit, and participants get encounter credit through “Non face-to-face prolonged service” codes (CPT 99358/99359), and other possibilities exist related to clinical team conference codes (CPT 99367-8) and complex chronic care management codes (CPT 99487-89). More information on documentation, scheduling and encountering/billing can be found at boisevacoe.org under Products. Other challenges include logistic challenges of finding appropriate patients and distributing sensitive patient information among the team. Additionally, PACT ICU has to wrestle with staffing shortages and episodic participation by some professions that are chronically understaffed. We have addressed many of these problems by receiving buy-in from both leadership and participants. Leadership have allowed time for participation in clinic staff schedules, and each participant has committed to recruiting a substitute in case of a schedule conflict.

Factors for Success

The commitment from the Boise VAMC facility, primary care clinic leadership and affiliated training programs to support staff and trainee participation also has been critical. Additionally, VA facility leadership commitment to ongoing improvements to PACT implementation was a key facilitating factor. Colocation of trainees and clinic staff on the academic PACT team facilitates communication between PACT ICU case conferences, while also supporting team dynamics and sustained relationships with patients. Many of these patients can and will typically seek care using the interdisciplinary trainees, and trainees were motivated to proactively coordinate warm handoffs and other models of transfer of care. PACT ICU has been successfully replicated and sustained at 4 of the 5 CoEPCE sites. The Caldwell CBOC PACT ICU has been up and running for 2 years, and 2 other nonacademic clinics have piloted PACT ICU managed care conferences thus far. Experience regarding the implementation at other academic sites has been published.⁵

Accomplishments and Benefits

There is evidence that PACT ICU is achieving its goals of improving trainee learning and patient outcomes. Trainees are using team skills to provide patient-centered care; trainees are strengthening their overall clinical skills by learning how to improve their responses to high-risk patients. There is also evidence of an increase in interprofessional warm handoffs within the clinic, in which “a clinician directly introduces a patient to another clinician at the time of the patient’s visit, and often a brief encounter between the patient and the health care professional occurs.”^4,6

 

Unlike a traditional didactic with classroom case conferences on interprofessional collaboration, PACT ICU is an opportunity for health care professionals to both learn and work together providing care in a clinic. Moreover, colocation of diverse trainee and faculty professions during the case conferences better prepares trainees to work with other professions and supports all participants to work and communicate as a team.

CoE staff have assessed educational outcomes before and after attendance in PACT ICU. On average, trainees (n = 30) said they found the PACT ICU case conferences to be “very helpful” in developing treatment plans. 

Second, trainees reported increased understanding of the elements that should be considered in developing a care plan and the variety of roles played by team members in providing care to difficult or complex patients (Table 2).

Interprofessional Collaboration

Team building and colocating trainees, faculty, and clinic staff from different professions are a primary focus of PACT ICU. The case conferences are designed to break down silos and foster a team approach to care. Trainees learn how the team works and the ways other professionals can help them take care of the patient. For example, trainees learn early about the contributions and expertise that the pharmacist and psychologist offer in terms of their scope of practice and referral opportunities. Additionally, the RN care manager increases the integration with the PACT clinical team by sharing pertinent information on individual patients. Based on recent trainee survey findings, the CoE has observed a positive change in the team dynamic and trainee ability to interface between professions. PACT ICU participants were more likely to make referrals to other members within the PACT team, such as a warm handoff during a clinic appointment, while they were less likely to seek a consult outside the team.⁷

Clinical Performance

The PACT ICU is an opportunity for a trainee to increase clinical expertise. It provides exposure to a variety of patientsand their care needs and serves as an opportunity to present a high-risk, challenging patient to colleagues of various professions. As of June 2018, 96 physician resident and NP residents have presented complex patient cases.

In addition, a structured forum for discussing patients and their care options strengthens team clinical performance, which supports people to work to the full scope of their practice. Trainees learn and apply team skills, such as communication and the warm handoff.

An interprofessional care plan that is delineated during the meeting supports the trainee and is carried out with help from consultants as needed. These consultants often facilitate plans for a covisit or warm handoff at the next clinic visit, a call from the RN care manager, a virtual clinic appointment, or other nontraditional visits. The clinic staff can get information from PCPs about patient’s plan of care, and PCPs get a more complete picture of a patient’s situation (eg, history, communications, and life-style factors). In addition, surveys of PACT ICU participants suggest the curriculum’s effectiveness at encouraging use of PACT principles within the clinic team and improving appropriate referrals to other members of the PACT team, such as pharmacy and behavioral health.

Patients presented at PACT ICU can be particularly challenging, so there may be a psychological benefit to working with a team to develop a new care plan. The PCPs who feel they are overwhelmed and have exhausted every option step back, get input, and look at the patient in a new light.

Related: Interprofessional Education in Patient Aligned Care Team Primary Care-Mental Health Integration

 

CoEPCE Function

The PACT ICU is flexible and has been adapted to different ambulatory care settings. Currently, PACT ICU case conferences take place at Boise VAMC, the Caldwell CBOCs, and more recently at a smaller CBOC in Burns, Oregon. The PACT ICU structure is slightly different in the clinic settings since the VA primary care clinic has different resources to draw upon, such as hospital and specialty services. The Caldwell CBOC was unable to protect time for PCPs, so it holds a monthly PACT ICU case conference. In addition to continuing expansion in other nonacademic PACT clinics and collaboration with other CoEPCE sites, work is underway to disseminate generalizable principles for interprofessional education, as well as exporting the model for implementation in non-VA settings.

Primary Care Services

The PACT ICU has the potential to create efficiencies in busy clinic settings. It strengthens communication between PCPs and is an opportunity to touch base on the patient, delegate care, and keep track of high-risk patients who might otherwise receive attention only when having an acute problem. Nurses gain a deeper understanding of the patients presented at PACT ICU.

PACT ICU leverages and builds on existing PACT resources in an achievable and sustainable manner benefiting primary care. CoE trainees, who are part of the Silver Team, tap in to the information that team nurses gain from checking in with these high-risk patients biweekly. Moreover, the integration with the Silver Team improves continuity, which helps enhance a patient’s level of trust. The relationship strengthened between primary care and behavioral health at the Caldwell CBOC, providing improved patient access and increased professional sharing.

Patient Outcomes

The PACT ICU provides a forum for input beyond that of the PCP. This feature results in a more robust treatment plan than might be developed by individual PCPs who might not have time to consider options that are outside their scope of practice. Formulating an enriched care plan, informed by multiple professions, has the potential to improve utilization and provide better care.

The Boise VAMC PACT ICU has presented 219 patients as of June 2018. While clinical outcomes data are difficult to collect, the CoE has data on utilization differences on all patients presented at the PACT ICU case conferences. This includes 4 control patients from the same PCP, with similarly high risk based on CAN scores at the time of selection. A single control patient is selected based on gender, closest age, and CAN score; this serves as a comparator for subsequent utilization analysis.

Data from the first 2 years of this study demonstrate that compared with the high-risk control group, there was an increase in contacts with PACT team members, including behavioral health, clinical pharmacists, and nurse care management, persisting up to 6 months following the PACT ICU presentation.⁴ However, PACT ICU participation did not increase the number of visits with the PCP, indicating better engagement with the entire team. Participation was associated with significantly decreased hospitalizations and a trend toward decreased ED visits. These findings persisted when compared with controls in the PCP’s panel with similar CAN scores, making “regression to the mean” often seen in these studies much less likely.

Analysis of patients early in the project suggests the possibility of improved glycemic control in patients with DM and improved blood pressure control in hypertensive patients presented at the PACT ICU compared with that of non-PACT ICU patients.⁸ Another potential benefit includes better team-based coordination. Because the patient now has a team focusing on care, this new dynamic results in improving outreach, identifying patients who could receive care by a telephone, and better preparing team members to establish rapport when the patient calls or comes in for a visit.

 

The Future

In stage 2 of the CoEPCE program, a multi-site trial of PACT ICU was completed to better understand which elements are critical to success, with the goal of facilitating broader exportability.⁵ The trial focused on 3 intertwined elements: structure, delivery, and evaluation. Using local implementation and the multisite trial, the most effective practices have been documented as part of an implementation kit, available at boisevacoe.org. The goal of the implementation kit is to facilitate step-by-step implementation of PACT ICU to other settings beyond the multisite study. Since the open-ended structure of PACT ICU enables accommodating different professions and specialties beyond the model’s Boise VAMC participants, it could be easily adapted to potentially support a variety of implementations elsewhere (Appendix).

Another opportunity for expansion is increased patient involvement. Currently, PACT ICU patients have the opportunity to review and ask questions about their multidisciplinary care plans before starting. 

Patients know they have a team working on their behalf, but there are opportunities for more follow-up, including presenting patients who are seen by other providers outside the CoE, such as the attending physician who may also have challenging patients. Long-term goals include developing sustainable formats for supporting PACT ICU in nonacademic settings as part of “routine care” and evaluating the implementation and impact on patient care, satisfaction, and utilization.

The evaluation of acute kidney injury (AKI) often starts with the classic prerenal, renal, and postrenal causalities, delineating a practical workable approach in its differential diagnosis. Accordingly, the history, physical examination, urinalysis, and kidney-bladder sonography are standard resources in the initial approach to renal disease assessment. Ultrasonography has a well-established role as an important adjuvant for postrenal diagnosis of renal failure. Nevertheless, most of the causes of AKI are prerenal and renal.

Some etiologies of kidney injury are sequelae of systemic diseases in which sonography can be diagnostically analogous to the history and physical examination. Furthermore, ultrasonography may be informative in various clinical scenarios, for example, patients with chronic kidney disease (CKD) and end-stage renal disease (ESRD). In this narrative review, the contribution of point-of-care (POC) sonography to the evaluation and management of AKI, CKD, and associated diseases are explored beyond the traditional sonogram uses for kidney biopsy, central catheter placement, and/or screening of hydronephrosis.

Two important elements made possible the incorporation of POC sonography into nephrology practice.^1,2 First, the development of handheld reliable and portable ultrasound devices and, second, the derived capacity of POC sonography to obtain objective signs of physiologic and/or pathophysiologic phenomena. The latter clinical application is realized through the incorporation of POC protocols into the modified focused assessment with sonography for trauma (FAST) examination in conjunction with limited echocardiography and lung sonography (Figure 1). 

The original FAST protocol was developed by the American Institute of Ultrasound in Medicine and the American College of Emergency Physicians.³

These protocols have allowed the evaluation of extracellular volume, which is important to measure for the diagnosis and management of renal diseases. For example, the evaluation of lung water by POC ultrasonography for patients with ESRD is emerging as a promising tool. In a study of patients with ESRD undergoing hemodialysis, POC ultrasonography detected moderate-to-severe lung congestion in 45% of patients, most of whom (71%) were asymptomatic. Two years of follow-up of patients was associated with 3 to 4 times greater risk of heart attack and death, respectively, compared with individuals without congestion on sonography.^4-6 Thus, ultrasound assessment of lung water in patients with ESRD may prove to be an essential tool to assure an adequate ultrafiltration and improve patient outcomes.

Related: Nephrogenic Systemic Fibrosis in a Patient With Multiple Inflammatory Disorders

Acute Kidney Injury

Prerenal

The physical examination provides evaluation of effective arterial circulatory flow (EACF) and is clinically useful in the evaluation of prerenal azotemia. The utility is more obvious in the extremes of EACF. However, in the case of blood volume losses of > 10% or the physiologic equivalent, heart rate, blood pressure, skin turgor, urinary output, and capillary refill may be within normal limits. Obvious changes in these parameters during the physical examination are considered relatively late manifestations.^7-10 Therefore, prerenal failure is frequently diagnosed retrospectively after correction of the EACF through use of crystalloids, blood products, vasopressors, inotropic agents, discontinuation of antihypertensive agents, or treatment of its prerenal causes. Certain sonographic maneuvers, performed at the bedside during acute renal injury, may be useful in many patients to evaluate a multitude of prerenal causes of AKI.

 

Sonographic inferior vena cava (IVC) luminal diameter and inspiratory collapsibility together serve as a surrogate marker of preload venous return and right side heart function. Such imaging results have been shown to be more accurate than jugular venous distension on physical examination but only modestly helpful as a surrogate for central venous pressure (CVP), with more accuracy in the lower values of the CVP.¹¹ However, this procedure can be repeated often after volume resuscitation to achieve a 1.5- to 2.5-cm diameter dimension of the IVC and < 25% inspiratory collapsibility as a goal.

An IVC with a diameter > 2.5 cm in the context of a suspected prerenal AKI is more likely the consequence of heart failure (HF) rather than hypovolemia. The caveat to this finding is that pulmonary hypertension may induce false-positive results.^12,13 Hepatic vein dilation is another sign of HF and/or pulmonary hypertension. Furthermore, sonographic images of the left ventricle either from the parasternal long axis or subxiphoid approach can identify supranormal left ventricular ejection fraction (LVEF) or hyperdynamic heart as an important clue of the absolute or relative decrease of EACF.¹⁴ Conversely, a decrease in EACF in patients with low LVEF can be assessed qualitatively at the bedside in patients with systolic HF. Supporting evidence of prerenal azotemia as the result of HF can be suggested by the presence of pleural effusions and bilateral comet/rockets tails or B lines in lung sonography.¹⁵

The easily recognizable hypoechoic ascitic fluid in the presence of small, hyperechoic gross changes in the echocardiographic texture of liver may indicate a hepatorenal component as the cause of prerenal failure. A small increase of > 20% in the diameter of the portal vein with deep inspiration indicates portal hypertension, with a sensitivity of 80% and a specificity of 100%.^15,16 Other clinical scenarios leading to AKI in association with systemic hypotension may be identified quickly with the aid of POC sonography. These scenarios include cardiac tamponade, tension pneumothorax, right ventricular dysfunction (as a surrogate of pulmonary embolism), or an acute coronary event.^16,17 Alternatively, identifying the presence of severe left ventricular hypertrophy through POC ultrasonography in a patient with AKI and normal or low normal blood pressures may alert clinicians to the diagnosis of normotensive renal failure in individuals with previously unrecognized severe hypertension. In this clinical context, keeping mean arterial pressures higher than usual with vasopressors may improve renal function while decreasing dialysis utilization.^18-21

Likewise, in clinical scenarios of shock with AKI, POC ultrasonography has proven to be an indispensable tool. For example, rapid exploration of the biliary tree demonstrating anterior gallbladder wall thickening, a stone or sludge, common bile duct dilation, or perigallbladder inflammation suggests acute cholecystitis and/or cholangitis as the cause. The presence of dyspnea in association with hypotension and unilateral signs of a higher proportion of comet tails and/or a lung consolidation suggests pneumonia. Rapid differentiation between acute respiratory distress syndrome (ARDS) and pulmonary edema from HF is possible with ultrasonography. When pleural line abnormalities are seen, ARDS is a common cause.

POC ultrasonography will be key in management of ARDS, as ultrasound results will help avoid the use of excessive diuretics, which can result in renal hypoperfusion and AKI.²² In trauma patients, the ultrasound examination will identify free fluid (bleeding) as the source of the prerenal failure, along with its cause (aortic dissection, hepatic hemorrhage, splenic hemorrhage, ectopic pregnancy, etc).²³ Sonographic free air observed in the abdomen can provide the clue of a perforated viscus.²⁴ The sonographic image of an inflamed pancreas can suggest pancreatitis as the cause of the systemic hypotension. Ultimately, intravascular losses in the hypoechoic edematous bowel wall in obstruction, ileus, pseudomembranous, or infectious or autoimmune enterocolitis can lead to significant decreases in the EACF and cause prerenal injury.

Related: Prevalence of Suspicious Ultrasound Features in Hot Thyroid Nodules

 

Intrinsic Renal Disease

In intrinsic AKI, acute tubular necrosis (ATN), glomerulonephritis, and interstitial nephritis are the typical causes. Although no signs are specific to each of the potential causes, a poor corticomedullary differentiation, kidney size < 9 cm, and cortex size < 1 cm help to distinguish CKD from AKI, especially if no previous serum creatinine values are available. The early diagnosis of ATN continues to be clinically relevant in the management of acute renal failure. Despite not being a practical tool for POC sonography currently, the use of bedside Doppler repetitive renal vasculature measures of resistive index predict occurrence and severity of ATN in the critical care setting and are an independent risk factor for poor survival in arterial hypertension and HF.^25-30

Other POC sonographic evaluations of intrinsic AKI have been helpful in the following clinical scenarios. The presence of an ultrasonographic sign of sinusitis in the context of nephritic sediment and a rapid decline of renal function suggest antineutrophil cytoplasmic antibody (ANCA)-related vasculitis. Likewise, in younger adults, nephritic sediment and bilateral sonographic lung interstitial fluid in the absence of infection and a normal POC echocardiogram without significant edema elsewhere suggest glomerulonephritis in the category of pulmonary lung syndrome caused by antiglomerular basement membrane antibodies.

In the elderly, a similar systemic presentation suggests an ANCA vasculitis. Pleural effusion, synovitis, proteinuria, and/or hematuria will suggest lupus nephritis. Another important cause of acute renal failure in the critical care setting is intra-abdominal compartment syndrome. Here, bladder pressure measurement protocols are the standard of care. A human model evaluated the predictive value of intra-abdominal compartment syndrome pressures using the IVC square surface. In this study, a normal surface area of the IVC of > 1 cm²/m² excluded the presence of intra-abdominal hypertension 87.5% of the time. However, the sensitivity of detection of the intra-abdominal hypertension was only 67.5% when the surface area of the IVC was < 1 cm²/m².³¹

CKD and Associated Diseases

The diagnostic validity of ultrasonography is well established in adult-onset polycystic kidney disease. Bedside visualization of a parathyroid adenoma may be an important clue for a patient with CKD, echogenic kidneys, or nephrolithiasis with or without hypercalcemia to diagnose primary hyperparathyroidism. The sonographic diagnosis of abnormal parathyroid gland compared with parathyroid surgical exploration had a sensitivity, specificity, and positive predictive value of 74%, 96%, and 90%, respectively.³² In the clinical presentation of severe hypertension with headaches, ultrasonography at bedside can provide valuable diagnostic and risk assessment information of endocranial hypertension from measuring the optic nerve sheath. Sensitivity and specificity of papilledema was 90% and 79%, respectively, when 3.3 mm was the cutoff of the nerve sheath with a 30-degrees sign.³³ The carotid artery intima media thickness measured on sonography correlates with the future development of atherogenesis, left ventricular hypertrophy, cognition deficits, CKD, and cardiovascular disease in asymptomatic patients. An intima media thickness of > 1.1 mm has been associated with a higher cardiovascular mortality.

Early initiation of antihypertensive medications and/or statins has been suggested to lower risk in these asymptomatic patients.³⁴ The size and contour (smooth or irregular) of kidneys may provide clues to reflux nephropathy, dysplastic kidneys, radiation nephritis, or chronic pyelonephritis. The presence of nephrotic syndrome and abnormal free light chains ratio with a bedside echocardiogram showing the typical refractile myocardial walls with a peculiar speckled pattern is strongly suggestive of amyloidosis.³⁵ Conditions associated with chronic hypercalcemia, medullary sponge kidney, milk alkali syndrome, sarcoidosis, and distal renal tubular acidosis are causes of nephrocalcinosis. Some degree of CKD is a constant feature in nephrocalcinosis. The initial imaging of choice in nephrocalcinosis and specially the medullary type is ultrasonography preferable to X-ray and perhaps to computed tomography.³⁶

 

End-Stage Renal Disease

In a patient undergoing peritoneal dialysis with exit-site infection, the presence of > 1 mm radiolucent rim around the subcutaneous catheter after antibiotics has a bad prognosis and prompts catheter removal. This sonographic sign has a positive and negative predictive value for a tunneled infection of 84.6% and 94.1%, respectively.^37,38 A risk factor for peritonitis in peritoneal dialysis is air in the peritoneum, which can be seen in one-third of patients. These individuals have 2.4 times more risk of peritonitis compared with patients without pneumoperitoneum. The sensitivity and specificity of sonographic detection of pneumoperitoneum is 94% and 100%, respectively, using the scissor technique.³⁹ Proper training in performing home peritoneal dialysis decreases the incidence of pneumoperitoneum. Although not formally assessed, patient education and change in procedure techniques may decrease the incidence of pneumoperitoneum and peritonitis. The use of prelaparoscopic ultrasonography before insertion of the peritoneal dialysis catheter has detected intra-abdominal adhesions (visceral slide sign) with a sensitivity of 90% to 92%.⁴⁰

History and physical examination are frequently helpful in the diagnosis of malfunctioning arteriovenous fistulas (AVF) for inflow or outflow disturbances, with sensitivity ranging from 70% to 100% and specificity ranging from 71% to 93% compared with angiography. Frequently, POC limited ultrasound can be helpful for a problematic AVF, either for cannulation or diagnosis. The congruence of duplex sonography with arteriogram is 85% to 96%. Various etiologies of a dysfunctional AVF (pseudo- or true aneurysm, poor development, stenosis, thrombi, or accessory veins) can be observed in the dialysis unit through limited sonography.^41-44

After placement of a hemodialysis catheter using real-time ultrasonography, pneumohemothorax can be diagnosed reliably and rapidly. Catheter misplacement outside of the right atrium was detected by thoracic echocardiogram with a sensitivity of 96%, a specificity of 83%, and a positive predictive value of 98%.^45,46 Ultimately, ultrasonography may replace chest X-ray in most cases after central vein dialysis catheter placement in the acute care setting.

Postrenal Failure

The sensitivity of ultrasonography to detect dilation to hydronephrosis of the pelvicaliceal system is well established. Sonography is the diagnostic examination of choice in pregnancy and the initial screening test for the nonpregnant patient. Computed tomography is the preferred imaging study in nephroureterolithiasis; however, due to ionizing radiation and cost, ultrasonography is gaining popularity for initial and/or follow-up evaluations. The ureteral jet is a relatively unexplored color and Doppler sonographic methodology that can provide insight into pelvicalyceal peristalsis, potentially yielding evidence of functional obstruction.^47-51 Postvoid bladder residual volumes and bladder wall hypertrophy may provide important clues as to the cause(s) of the obstructive uropathy.

Telenephrology

In our institution, sonography is used in the evaluation of IVC, lungs, and kidneys via telemedicine. The probe is handled by trained nurses at the distant site. 

The nurses perform and obtain sonographic images under direct supervision provided by a trained attending physician via real-time transmission of the tele-encounter. 

Figures 2 to 4 are real-time photos taken to evaluate the IVC (Figure 2), the kidneys (Figure 3), and lungs (Figure 4), respectively, during a clinic video teleconference. The use of “tele-POC sonography” may eliminate unnecessary traveling by patients and lower health care utilization costs while providing real-time assessment of a multitude of clinical issues.

 

Cardiac Arrest in ESRD

Patients with ESRD may have sudden cardiac arrest as a result of several etiologies. During the advance cardiac life support algorithm, there is a brief period of evaluation of the electrical rhythm in which echocardiography can be helpful with the diagnosis immediately after the 2 initial minutes of cardiopulmonary resuscitation. An enlarged right ventricular cavity (> 2/3 of the left ventricle) is a sonographic sign of a pulmonary embolism.

Bedside sonography has the potential to alter the current guidelines of advance cardiac life support management. For example, if the bedside echo shows a significant pericardial effusion, a pericardiocentesis could be performed faster as it would be diagnosed faster. In addition, at times the heart may appear to be beating rapidly but there is a small amount of fluid (blood) within the cardiac chambers. This may be from an extreme case of dehydration for which rapid administration of IV fluids may help manage. Therefore, a quick bedside point of care echocardiography may reveal a cardiac anomaly that may be able to be restored in a efficient manner. 

Pulseless electrical activity is the most common rhythm found in ESRD. The presence of hypercontractile myocardium in the absence of a pulse would suggest the need for fluids or blood instead of the usual epinephrine and cardiopulmonary resuscitation (Figure 5).

Related: General Applications of Ultrasound in Rheumatology Practice

Conclusion

Ultrasonography at the POC provides an important and continuously expanding tool to improve nephrological diagnostic accuracy in concert with history and physical examination. Extracellular fluid evaluation is paramount in all kidney disease conditions. Recent clinical studies in lung ultrasonography suggest that the learning curve for the medical provider is quicker than with other organs. Because POC sonography in association with limited bedside echocardiography may reveal discriminatory signs of pneumonia and differentiate between cardiogenic vs noncardiogenic pulmonary edema, such imaging may be important cost-effective strategies in the management of dyspnea and in the categorization/etiology of AKI. Therefore, incorporation of POC sonography into clinical practice will require that medical schools, residency programs, and nephrology fellowship programs design teaching strategies within their respective curricula. Research studies with outcomes regarding diagnosis, morbidity, and mortality are necessary in these areas.

The evaluation of acute kidney injury (AKI) often starts with the classic prerenal, renal, and postrenal causalities, delineating a practical workable approach in its differential diagnosis. Accordingly, the history, physical examination, urinalysis, and kidney-bladder sonography are standard resources in the initial approach to renal disease assessment. Ultrasonography has a well-established role as an important adjuvant for postrenal diagnosis of renal failure. Nevertheless, most of the causes of AKI are prerenal and renal.

Some etiologies of kidney injury are sequelae of systemic diseases in which sonography can be diagnostically analogous to the history and physical examination. Furthermore, ultrasonography may be informative in various clinical scenarios, for example, patients with chronic kidney disease (CKD) and end-stage renal disease (ESRD). In this narrative review, the contribution of point-of-care (POC) sonography to the evaluation and management of AKI, CKD, and associated diseases are explored beyond the traditional sonogram uses for kidney biopsy, central catheter placement, and/or screening of hydronephrosis.

Two important elements made possible the incorporation of POC sonography into nephrology practice.^1,2 First, the development of handheld reliable and portable ultrasound devices and, second, the derived capacity of POC sonography to obtain objective signs of physiologic and/or pathophysiologic phenomena. The latter clinical application is realized through the incorporation of POC protocols into the modified focused assessment with sonography for trauma (FAST) examination in conjunction with limited echocardiography and lung sonography (Figure 1). 

The original FAST protocol was developed by the American Institute of Ultrasound in Medicine and the American College of Emergency Physicians.³

These protocols have allowed the evaluation of extracellular volume, which is important to measure for the diagnosis and management of renal diseases. For example, the evaluation of lung water by POC ultrasonography for patients with ESRD is emerging as a promising tool. In a study of patients with ESRD undergoing hemodialysis, POC ultrasonography detected moderate-to-severe lung congestion in 45% of patients, most of whom (71%) were asymptomatic. Two years of follow-up of patients was associated with 3 to 4 times greater risk of heart attack and death, respectively, compared with individuals without congestion on sonography.^4-6 Thus, ultrasound assessment of lung water in patients with ESRD may prove to be an essential tool to assure an adequate ultrafiltration and improve patient outcomes.

Related: Nephrogenic Systemic Fibrosis in a Patient With Multiple Inflammatory Disorders

Acute Kidney Injury

Prerenal

The physical examination provides evaluation of effective arterial circulatory flow (EACF) and is clinically useful in the evaluation of prerenal azotemia. The utility is more obvious in the extremes of EACF. However, in the case of blood volume losses of > 10% or the physiologic equivalent, heart rate, blood pressure, skin turgor, urinary output, and capillary refill may be within normal limits. Obvious changes in these parameters during the physical examination are considered relatively late manifestations.^7-10 Therefore, prerenal failure is frequently diagnosed retrospectively after correction of the EACF through use of crystalloids, blood products, vasopressors, inotropic agents, discontinuation of antihypertensive agents, or treatment of its prerenal causes. Certain sonographic maneuvers, performed at the bedside during acute renal injury, may be useful in many patients to evaluate a multitude of prerenal causes of AKI.

 

Sonographic inferior vena cava (IVC) luminal diameter and inspiratory collapsibility together serve as a surrogate marker of preload venous return and right side heart function. Such imaging results have been shown to be more accurate than jugular venous distension on physical examination but only modestly helpful as a surrogate for central venous pressure (CVP), with more accuracy in the lower values of the CVP.¹¹ However, this procedure can be repeated often after volume resuscitation to achieve a 1.5- to 2.5-cm diameter dimension of the IVC and < 25% inspiratory collapsibility as a goal.

An IVC with a diameter > 2.5 cm in the context of a suspected prerenal AKI is more likely the consequence of heart failure (HF) rather than hypovolemia. The caveat to this finding is that pulmonary hypertension may induce false-positive results.^12,13 Hepatic vein dilation is another sign of HF and/or pulmonary hypertension. Furthermore, sonographic images of the left ventricle either from the parasternal long axis or subxiphoid approach can identify supranormal left ventricular ejection fraction (LVEF) or hyperdynamic heart as an important clue of the absolute or relative decrease of EACF.¹⁴ Conversely, a decrease in EACF in patients with low LVEF can be assessed qualitatively at the bedside in patients with systolic HF. Supporting evidence of prerenal azotemia as the result of HF can be suggested by the presence of pleural effusions and bilateral comet/rockets tails or B lines in lung sonography.¹⁵

The easily recognizable hypoechoic ascitic fluid in the presence of small, hyperechoic gross changes in the echocardiographic texture of liver may indicate a hepatorenal component as the cause of prerenal failure. A small increase of > 20% in the diameter of the portal vein with deep inspiration indicates portal hypertension, with a sensitivity of 80% and a specificity of 100%.^15,16 Other clinical scenarios leading to AKI in association with systemic hypotension may be identified quickly with the aid of POC sonography. These scenarios include cardiac tamponade, tension pneumothorax, right ventricular dysfunction (as a surrogate of pulmonary embolism), or an acute coronary event.^16,17 Alternatively, identifying the presence of severe left ventricular hypertrophy through POC ultrasonography in a patient with AKI and normal or low normal blood pressures may alert clinicians to the diagnosis of normotensive renal failure in individuals with previously unrecognized severe hypertension. In this clinical context, keeping mean arterial pressures higher than usual with vasopressors may improve renal function while decreasing dialysis utilization.^18-21

Likewise, in clinical scenarios of shock with AKI, POC ultrasonography has proven to be an indispensable tool. For example, rapid exploration of the biliary tree demonstrating anterior gallbladder wall thickening, a stone or sludge, common bile duct dilation, or perigallbladder inflammation suggests acute cholecystitis and/or cholangitis as the cause. The presence of dyspnea in association with hypotension and unilateral signs of a higher proportion of comet tails and/or a lung consolidation suggests pneumonia. Rapid differentiation between acute respiratory distress syndrome (ARDS) and pulmonary edema from HF is possible with ultrasonography. When pleural line abnormalities are seen, ARDS is a common cause.

POC ultrasonography will be key in management of ARDS, as ultrasound results will help avoid the use of excessive diuretics, which can result in renal hypoperfusion and AKI.²² In trauma patients, the ultrasound examination will identify free fluid (bleeding) as the source of the prerenal failure, along with its cause (aortic dissection, hepatic hemorrhage, splenic hemorrhage, ectopic pregnancy, etc).²³ Sonographic free air observed in the abdomen can provide the clue of a perforated viscus.²⁴ The sonographic image of an inflamed pancreas can suggest pancreatitis as the cause of the systemic hypotension. Ultimately, intravascular losses in the hypoechoic edematous bowel wall in obstruction, ileus, pseudomembranous, or infectious or autoimmune enterocolitis can lead to significant decreases in the EACF and cause prerenal injury.

Related: Prevalence of Suspicious Ultrasound Features in Hot Thyroid Nodules

 

Intrinsic Renal Disease

In intrinsic AKI, acute tubular necrosis (ATN), glomerulonephritis, and interstitial nephritis are the typical causes. Although no signs are specific to each of the potential causes, a poor corticomedullary differentiation, kidney size < 9 cm, and cortex size < 1 cm help to distinguish CKD from AKI, especially if no previous serum creatinine values are available. The early diagnosis of ATN continues to be clinically relevant in the management of acute renal failure. Despite not being a practical tool for POC sonography currently, the use of bedside Doppler repetitive renal vasculature measures of resistive index predict occurrence and severity of ATN in the critical care setting and are an independent risk factor for poor survival in arterial hypertension and HF.^25-30

Other POC sonographic evaluations of intrinsic AKI have been helpful in the following clinical scenarios. The presence of an ultrasonographic sign of sinusitis in the context of nephritic sediment and a rapid decline of renal function suggest antineutrophil cytoplasmic antibody (ANCA)-related vasculitis. Likewise, in younger adults, nephritic sediment and bilateral sonographic lung interstitial fluid in the absence of infection and a normal POC echocardiogram without significant edema elsewhere suggest glomerulonephritis in the category of pulmonary lung syndrome caused by antiglomerular basement membrane antibodies.

In the elderly, a similar systemic presentation suggests an ANCA vasculitis. Pleural effusion, synovitis, proteinuria, and/or hematuria will suggest lupus nephritis. Another important cause of acute renal failure in the critical care setting is intra-abdominal compartment syndrome. Here, bladder pressure measurement protocols are the standard of care. A human model evaluated the predictive value of intra-abdominal compartment syndrome pressures using the IVC square surface. In this study, a normal surface area of the IVC of > 1 cm²/m² excluded the presence of intra-abdominal hypertension 87.5% of the time. However, the sensitivity of detection of the intra-abdominal hypertension was only 67.5% when the surface area of the IVC was < 1 cm²/m².³¹

CKD and Associated Diseases

The diagnostic validity of ultrasonography is well established in adult-onset polycystic kidney disease. Bedside visualization of a parathyroid adenoma may be an important clue for a patient with CKD, echogenic kidneys, or nephrolithiasis with or without hypercalcemia to diagnose primary hyperparathyroidism. The sonographic diagnosis of abnormal parathyroid gland compared with parathyroid surgical exploration had a sensitivity, specificity, and positive predictive value of 74%, 96%, and 90%, respectively.³² In the clinical presentation of severe hypertension with headaches, ultrasonography at bedside can provide valuable diagnostic and risk assessment information of endocranial hypertension from measuring the optic nerve sheath. Sensitivity and specificity of papilledema was 90% and 79%, respectively, when 3.3 mm was the cutoff of the nerve sheath with a 30-degrees sign.³³ The carotid artery intima media thickness measured on sonography correlates with the future development of atherogenesis, left ventricular hypertrophy, cognition deficits, CKD, and cardiovascular disease in asymptomatic patients. An intima media thickness of > 1.1 mm has been associated with a higher cardiovascular mortality.

Early initiation of antihypertensive medications and/or statins has been suggested to lower risk in these asymptomatic patients.³⁴ The size and contour (smooth or irregular) of kidneys may provide clues to reflux nephropathy, dysplastic kidneys, radiation nephritis, or chronic pyelonephritis. The presence of nephrotic syndrome and abnormal free light chains ratio with a bedside echocardiogram showing the typical refractile myocardial walls with a peculiar speckled pattern is strongly suggestive of amyloidosis.³⁵ Conditions associated with chronic hypercalcemia, medullary sponge kidney, milk alkali syndrome, sarcoidosis, and distal renal tubular acidosis are causes of nephrocalcinosis. Some degree of CKD is a constant feature in nephrocalcinosis. The initial imaging of choice in nephrocalcinosis and specially the medullary type is ultrasonography preferable to X-ray and perhaps to computed tomography.³⁶

 

End-Stage Renal Disease

In a patient undergoing peritoneal dialysis with exit-site infection, the presence of > 1 mm radiolucent rim around the subcutaneous catheter after antibiotics has a bad prognosis and prompts catheter removal. This sonographic sign has a positive and negative predictive value for a tunneled infection of 84.6% and 94.1%, respectively.^37,38 A risk factor for peritonitis in peritoneal dialysis is air in the peritoneum, which can be seen in one-third of patients. These individuals have 2.4 times more risk of peritonitis compared with patients without pneumoperitoneum. The sensitivity and specificity of sonographic detection of pneumoperitoneum is 94% and 100%, respectively, using the scissor technique.³⁹ Proper training in performing home peritoneal dialysis decreases the incidence of pneumoperitoneum. Although not formally assessed, patient education and change in procedure techniques may decrease the incidence of pneumoperitoneum and peritonitis. The use of prelaparoscopic ultrasonography before insertion of the peritoneal dialysis catheter has detected intra-abdominal adhesions (visceral slide sign) with a sensitivity of 90% to 92%.⁴⁰

History and physical examination are frequently helpful in the diagnosis of malfunctioning arteriovenous fistulas (AVF) for inflow or outflow disturbances, with sensitivity ranging from 70% to 100% and specificity ranging from 71% to 93% compared with angiography. Frequently, POC limited ultrasound can be helpful for a problematic AVF, either for cannulation or diagnosis. The congruence of duplex sonography with arteriogram is 85% to 96%. Various etiologies of a dysfunctional AVF (pseudo- or true aneurysm, poor development, stenosis, thrombi, or accessory veins) can be observed in the dialysis unit through limited sonography.^41-44

After placement of a hemodialysis catheter using real-time ultrasonography, pneumohemothorax can be diagnosed reliably and rapidly. Catheter misplacement outside of the right atrium was detected by thoracic echocardiogram with a sensitivity of 96%, a specificity of 83%, and a positive predictive value of 98%.^45,46 Ultimately, ultrasonography may replace chest X-ray in most cases after central vein dialysis catheter placement in the acute care setting.

Postrenal Failure

The sensitivity of ultrasonography to detect dilation to hydronephrosis of the pelvicaliceal system is well established. Sonography is the diagnostic examination of choice in pregnancy and the initial screening test for the nonpregnant patient. Computed tomography is the preferred imaging study in nephroureterolithiasis; however, due to ionizing radiation and cost, ultrasonography is gaining popularity for initial and/or follow-up evaluations. The ureteral jet is a relatively unexplored color and Doppler sonographic methodology that can provide insight into pelvicalyceal peristalsis, potentially yielding evidence of functional obstruction.^47-51 Postvoid bladder residual volumes and bladder wall hypertrophy may provide important clues as to the cause(s) of the obstructive uropathy.

Telenephrology

In our institution, sonography is used in the evaluation of IVC, lungs, and kidneys via telemedicine. The probe is handled by trained nurses at the distant site. 

The nurses perform and obtain sonographic images under direct supervision provided by a trained attending physician via real-time transmission of the tele-encounter. 

Figures 2 to 4 are real-time photos taken to evaluate the IVC (Figure 2), the kidneys (Figure 3), and lungs (Figure 4), respectively, during a clinic video teleconference. The use of “tele-POC sonography” may eliminate unnecessary traveling by patients and lower health care utilization costs while providing real-time assessment of a multitude of clinical issues.

 

Cardiac Arrest in ESRD

Patients with ESRD may have sudden cardiac arrest as a result of several etiologies. During the advance cardiac life support algorithm, there is a brief period of evaluation of the electrical rhythm in which echocardiography can be helpful with the diagnosis immediately after the 2 initial minutes of cardiopulmonary resuscitation. An enlarged right ventricular cavity (> 2/3 of the left ventricle) is a sonographic sign of a pulmonary embolism.

Bedside sonography has the potential to alter the current guidelines of advance cardiac life support management. For example, if the bedside echo shows a significant pericardial effusion, a pericardiocentesis could be performed faster as it would be diagnosed faster. In addition, at times the heart may appear to be beating rapidly but there is a small amount of fluid (blood) within the cardiac chambers. This may be from an extreme case of dehydration for which rapid administration of IV fluids may help manage. Therefore, a quick bedside point of care echocardiography may reveal a cardiac anomaly that may be able to be restored in a efficient manner. 

Pulseless electrical activity is the most common rhythm found in ESRD. The presence of hypercontractile myocardium in the absence of a pulse would suggest the need for fluids or blood instead of the usual epinephrine and cardiopulmonary resuscitation (Figure 5).

Related: General Applications of Ultrasound in Rheumatology Practice

Conclusion

Ultrasonography at the POC provides an important and continuously expanding tool to improve nephrological diagnostic accuracy in concert with history and physical examination. Extracellular fluid evaluation is paramount in all kidney disease conditions. Recent clinical studies in lung ultrasonography suggest that the learning curve for the medical provider is quicker than with other organs. Because POC sonography in association with limited bedside echocardiography may reveal discriminatory signs of pneumonia and differentiate between cardiogenic vs noncardiogenic pulmonary edema, such imaging may be important cost-effective strategies in the management of dyspnea and in the categorization/etiology of AKI. Therefore, incorporation of POC sonography into clinical practice will require that medical schools, residency programs, and nephrology fellowship programs design teaching strategies within their respective curricula. Research studies with outcomes regarding diagnosis, morbidity, and mortality are necessary in these areas.

The evaluation of acute kidney injury (AKI) often starts with the classic prerenal, renal, and postrenal causalities, delineating a practical workable approach in its differential diagnosis. Accordingly, the history, physical examination, urinalysis, and kidney-bladder sonography are standard resources in the initial approach to renal disease assessment. Ultrasonography has a well-established role as an important adjuvant for postrenal diagnosis of renal failure. Nevertheless, most of the causes of AKI are prerenal and renal.

Some etiologies of kidney injury are sequelae of systemic diseases in which sonography can be diagnostically analogous to the history and physical examination. Furthermore, ultrasonography may be informative in various clinical scenarios, for example, patients with chronic kidney disease (CKD) and end-stage renal disease (ESRD). In this narrative review, the contribution of point-of-care (POC) sonography to the evaluation and management of AKI, CKD, and associated diseases are explored beyond the traditional sonogram uses for kidney biopsy, central catheter placement, and/or screening of hydronephrosis.

Two important elements made possible the incorporation of POC sonography into nephrology practice.^1,2 First, the development of handheld reliable and portable ultrasound devices and, second, the derived capacity of POC sonography to obtain objective signs of physiologic and/or pathophysiologic phenomena. The latter clinical application is realized through the incorporation of POC protocols into the modified focused assessment with sonography for trauma (FAST) examination in conjunction with limited echocardiography and lung sonography (Figure 1). 

The original FAST protocol was developed by the American Institute of Ultrasound in Medicine and the American College of Emergency Physicians.³

These protocols have allowed the evaluation of extracellular volume, which is important to measure for the diagnosis and management of renal diseases. For example, the evaluation of lung water by POC ultrasonography for patients with ESRD is emerging as a promising tool. In a study of patients with ESRD undergoing hemodialysis, POC ultrasonography detected moderate-to-severe lung congestion in 45% of patients, most of whom (71%) were asymptomatic. Two years of follow-up of patients was associated with 3 to 4 times greater risk of heart attack and death, respectively, compared with individuals without congestion on sonography.^4-6 Thus, ultrasound assessment of lung water in patients with ESRD may prove to be an essential tool to assure an adequate ultrafiltration and improve patient outcomes.

Related: Nephrogenic Systemic Fibrosis in a Patient With Multiple Inflammatory Disorders

Acute Kidney Injury

Prerenal

The physical examination provides evaluation of effective arterial circulatory flow (EACF) and is clinically useful in the evaluation of prerenal azotemia. The utility is more obvious in the extremes of EACF. However, in the case of blood volume losses of > 10% or the physiologic equivalent, heart rate, blood pressure, skin turgor, urinary output, and capillary refill may be within normal limits. Obvious changes in these parameters during the physical examination are considered relatively late manifestations.^7-10 Therefore, prerenal failure is frequently diagnosed retrospectively after correction of the EACF through use of crystalloids, blood products, vasopressors, inotropic agents, discontinuation of antihypertensive agents, or treatment of its prerenal causes. Certain sonographic maneuvers, performed at the bedside during acute renal injury, may be useful in many patients to evaluate a multitude of prerenal causes of AKI.

 

Sonographic inferior vena cava (IVC) luminal diameter and inspiratory collapsibility together serve as a surrogate marker of preload venous return and right side heart function. Such imaging results have been shown to be more accurate than jugular venous distension on physical examination but only modestly helpful as a surrogate for central venous pressure (CVP), with more accuracy in the lower values of the CVP.¹¹ However, this procedure can be repeated often after volume resuscitation to achieve a 1.5- to 2.5-cm diameter dimension of the IVC and < 25% inspiratory collapsibility as a goal.

An IVC with a diameter > 2.5 cm in the context of a suspected prerenal AKI is more likely the consequence of heart failure (HF) rather than hypovolemia. The caveat to this finding is that pulmonary hypertension may induce false-positive results.^12,13 Hepatic vein dilation is another sign of HF and/or pulmonary hypertension. Furthermore, sonographic images of the left ventricle either from the parasternal long axis or subxiphoid approach can identify supranormal left ventricular ejection fraction (LVEF) or hyperdynamic heart as an important clue of the absolute or relative decrease of EACF.¹⁴ Conversely, a decrease in EACF in patients with low LVEF can be assessed qualitatively at the bedside in patients with systolic HF. Supporting evidence of prerenal azotemia as the result of HF can be suggested by the presence of pleural effusions and bilateral comet/rockets tails or B lines in lung sonography.¹⁵

The easily recognizable hypoechoic ascitic fluid in the presence of small, hyperechoic gross changes in the echocardiographic texture of liver may indicate a hepatorenal component as the cause of prerenal failure. A small increase of > 20% in the diameter of the portal vein with deep inspiration indicates portal hypertension, with a sensitivity of 80% and a specificity of 100%.^15,16 Other clinical scenarios leading to AKI in association with systemic hypotension may be identified quickly with the aid of POC sonography. These scenarios include cardiac tamponade, tension pneumothorax, right ventricular dysfunction (as a surrogate of pulmonary embolism), or an acute coronary event.^16,17 Alternatively, identifying the presence of severe left ventricular hypertrophy through POC ultrasonography in a patient with AKI and normal or low normal blood pressures may alert clinicians to the diagnosis of normotensive renal failure in individuals with previously unrecognized severe hypertension. In this clinical context, keeping mean arterial pressures higher than usual with vasopressors may improve renal function while decreasing dialysis utilization.^18-21

Likewise, in clinical scenarios of shock with AKI, POC ultrasonography has proven to be an indispensable tool. For example, rapid exploration of the biliary tree demonstrating anterior gallbladder wall thickening, a stone or sludge, common bile duct dilation, or perigallbladder inflammation suggests acute cholecystitis and/or cholangitis as the cause. The presence of dyspnea in association with hypotension and unilateral signs of a higher proportion of comet tails and/or a lung consolidation suggests pneumonia. Rapid differentiation between acute respiratory distress syndrome (ARDS) and pulmonary edema from HF is possible with ultrasonography. When pleural line abnormalities are seen, ARDS is a common cause.

POC ultrasonography will be key in management of ARDS, as ultrasound results will help avoid the use of excessive diuretics, which can result in renal hypoperfusion and AKI.²² In trauma patients, the ultrasound examination will identify free fluid (bleeding) as the source of the prerenal failure, along with its cause (aortic dissection, hepatic hemorrhage, splenic hemorrhage, ectopic pregnancy, etc).²³ Sonographic free air observed in the abdomen can provide the clue of a perforated viscus.²⁴ The sonographic image of an inflamed pancreas can suggest pancreatitis as the cause of the systemic hypotension. Ultimately, intravascular losses in the hypoechoic edematous bowel wall in obstruction, ileus, pseudomembranous, or infectious or autoimmune enterocolitis can lead to significant decreases in the EACF and cause prerenal injury.

Related: Prevalence of Suspicious Ultrasound Features in Hot Thyroid Nodules

 

Intrinsic Renal Disease

In intrinsic AKI, acute tubular necrosis (ATN), glomerulonephritis, and interstitial nephritis are the typical causes. Although no signs are specific to each of the potential causes, a poor corticomedullary differentiation, kidney size < 9 cm, and cortex size < 1 cm help to distinguish CKD from AKI, especially if no previous serum creatinine values are available. The early diagnosis of ATN continues to be clinically relevant in the management of acute renal failure. Despite not being a practical tool for POC sonography currently, the use of bedside Doppler repetitive renal vasculature measures of resistive index predict occurrence and severity of ATN in the critical care setting and are an independent risk factor for poor survival in arterial hypertension and HF.^25-30

Other POC sonographic evaluations of intrinsic AKI have been helpful in the following clinical scenarios. The presence of an ultrasonographic sign of sinusitis in the context of nephritic sediment and a rapid decline of renal function suggest antineutrophil cytoplasmic antibody (ANCA)-related vasculitis. Likewise, in younger adults, nephritic sediment and bilateral sonographic lung interstitial fluid in the absence of infection and a normal POC echocardiogram without significant edema elsewhere suggest glomerulonephritis in the category of pulmonary lung syndrome caused by antiglomerular basement membrane antibodies.

In the elderly, a similar systemic presentation suggests an ANCA vasculitis. Pleural effusion, synovitis, proteinuria, and/or hematuria will suggest lupus nephritis. Another important cause of acute renal failure in the critical care setting is intra-abdominal compartment syndrome. Here, bladder pressure measurement protocols are the standard of care. A human model evaluated the predictive value of intra-abdominal compartment syndrome pressures using the IVC square surface. In this study, a normal surface area of the IVC of > 1 cm²/m² excluded the presence of intra-abdominal hypertension 87.5% of the time. However, the sensitivity of detection of the intra-abdominal hypertension was only 67.5% when the surface area of the IVC was < 1 cm²/m².³¹

CKD and Associated Diseases

The diagnostic validity of ultrasonography is well established in adult-onset polycystic kidney disease. Bedside visualization of a parathyroid adenoma may be an important clue for a patient with CKD, echogenic kidneys, or nephrolithiasis with or without hypercalcemia to diagnose primary hyperparathyroidism. The sonographic diagnosis of abnormal parathyroid gland compared with parathyroid surgical exploration had a sensitivity, specificity, and positive predictive value of 74%, 96%, and 90%, respectively.³² In the clinical presentation of severe hypertension with headaches, ultrasonography at bedside can provide valuable diagnostic and risk assessment information of endocranial hypertension from measuring the optic nerve sheath. Sensitivity and specificity of papilledema was 90% and 79%, respectively, when 3.3 mm was the cutoff of the nerve sheath with a 30-degrees sign.³³ The carotid artery intima media thickness measured on sonography correlates with the future development of atherogenesis, left ventricular hypertrophy, cognition deficits, CKD, and cardiovascular disease in asymptomatic patients. An intima media thickness of > 1.1 mm has been associated with a higher cardiovascular mortality.

Early initiation of antihypertensive medications and/or statins has been suggested to lower risk in these asymptomatic patients.³⁴ The size and contour (smooth or irregular) of kidneys may provide clues to reflux nephropathy, dysplastic kidneys, radiation nephritis, or chronic pyelonephritis. The presence of nephrotic syndrome and abnormal free light chains ratio with a bedside echocardiogram showing the typical refractile myocardial walls with a peculiar speckled pattern is strongly suggestive of amyloidosis.³⁵ Conditions associated with chronic hypercalcemia, medullary sponge kidney, milk alkali syndrome, sarcoidosis, and distal renal tubular acidosis are causes of nephrocalcinosis. Some degree of CKD is a constant feature in nephrocalcinosis. The initial imaging of choice in nephrocalcinosis and specially the medullary type is ultrasonography preferable to X-ray and perhaps to computed tomography.³⁶

 

End-Stage Renal Disease

In a patient undergoing peritoneal dialysis with exit-site infection, the presence of > 1 mm radiolucent rim around the subcutaneous catheter after antibiotics has a bad prognosis and prompts catheter removal. This sonographic sign has a positive and negative predictive value for a tunneled infection of 84.6% and 94.1%, respectively.^37,38 A risk factor for peritonitis in peritoneal dialysis is air in the peritoneum, which can be seen in one-third of patients. These individuals have 2.4 times more risk of peritonitis compared with patients without pneumoperitoneum. The sensitivity and specificity of sonographic detection of pneumoperitoneum is 94% and 100%, respectively, using the scissor technique.³⁹ Proper training in performing home peritoneal dialysis decreases the incidence of pneumoperitoneum. Although not formally assessed, patient education and change in procedure techniques may decrease the incidence of pneumoperitoneum and peritonitis. The use of prelaparoscopic ultrasonography before insertion of the peritoneal dialysis catheter has detected intra-abdominal adhesions (visceral slide sign) with a sensitivity of 90% to 92%.⁴⁰

History and physical examination are frequently helpful in the diagnosis of malfunctioning arteriovenous fistulas (AVF) for inflow or outflow disturbances, with sensitivity ranging from 70% to 100% and specificity ranging from 71% to 93% compared with angiography. Frequently, POC limited ultrasound can be helpful for a problematic AVF, either for cannulation or diagnosis. The congruence of duplex sonography with arteriogram is 85% to 96%. Various etiologies of a dysfunctional AVF (pseudo- or true aneurysm, poor development, stenosis, thrombi, or accessory veins) can be observed in the dialysis unit through limited sonography.^41-44

After placement of a hemodialysis catheter using real-time ultrasonography, pneumohemothorax can be diagnosed reliably and rapidly. Catheter misplacement outside of the right atrium was detected by thoracic echocardiogram with a sensitivity of 96%, a specificity of 83%, and a positive predictive value of 98%.^45,46 Ultimately, ultrasonography may replace chest X-ray in most cases after central vein dialysis catheter placement in the acute care setting.

Postrenal Failure

The sensitivity of ultrasonography to detect dilation to hydronephrosis of the pelvicaliceal system is well established. Sonography is the diagnostic examination of choice in pregnancy and the initial screening test for the nonpregnant patient. Computed tomography is the preferred imaging study in nephroureterolithiasis; however, due to ionizing radiation and cost, ultrasonography is gaining popularity for initial and/or follow-up evaluations. The ureteral jet is a relatively unexplored color and Doppler sonographic methodology that can provide insight into pelvicalyceal peristalsis, potentially yielding evidence of functional obstruction.^47-51 Postvoid bladder residual volumes and bladder wall hypertrophy may provide important clues as to the cause(s) of the obstructive uropathy.

Telenephrology

In our institution, sonography is used in the evaluation of IVC, lungs, and kidneys via telemedicine. The probe is handled by trained nurses at the distant site. 

The nurses perform and obtain sonographic images under direct supervision provided by a trained attending physician via real-time transmission of the tele-encounter. 

Figures 2 to 4 are real-time photos taken to evaluate the IVC (Figure 2), the kidneys (Figure 3), and lungs (Figure 4), respectively, during a clinic video teleconference. The use of “tele-POC sonography” may eliminate unnecessary traveling by patients and lower health care utilization costs while providing real-time assessment of a multitude of clinical issues.

 

Cardiac Arrest in ESRD

Patients with ESRD may have sudden cardiac arrest as a result of several etiologies. During the advance cardiac life support algorithm, there is a brief period of evaluation of the electrical rhythm in which echocardiography can be helpful with the diagnosis immediately after the 2 initial minutes of cardiopulmonary resuscitation. An enlarged right ventricular cavity (> 2/3 of the left ventricle) is a sonographic sign of a pulmonary embolism.

Bedside sonography has the potential to alter the current guidelines of advance cardiac life support management. For example, if the bedside echo shows a significant pericardial effusion, a pericardiocentesis could be performed faster as it would be diagnosed faster. In addition, at times the heart may appear to be beating rapidly but there is a small amount of fluid (blood) within the cardiac chambers. This may be from an extreme case of dehydration for which rapid administration of IV fluids may help manage. Therefore, a quick bedside point of care echocardiography may reveal a cardiac anomaly that may be able to be restored in a efficient manner. 

Pulseless electrical activity is the most common rhythm found in ESRD. The presence of hypercontractile myocardium in the absence of a pulse would suggest the need for fluids or blood instead of the usual epinephrine and cardiopulmonary resuscitation (Figure 5).

Related: General Applications of Ultrasound in Rheumatology Practice

Conclusion

Ultrasonography at the POC provides an important and continuously expanding tool to improve nephrological diagnostic accuracy in concert with history and physical examination. Extracellular fluid evaluation is paramount in all kidney disease conditions. Recent clinical studies in lung ultrasonography suggest that the learning curve for the medical provider is quicker than with other organs. Because POC sonography in association with limited bedside echocardiography may reveal discriminatory signs of pneumonia and differentiate between cardiogenic vs noncardiogenic pulmonary edema, such imaging may be important cost-effective strategies in the management of dyspnea and in the categorization/etiology of AKI. Therefore, incorporation of POC sonography into clinical practice will require that medical schools, residency programs, and nephrology fellowship programs design teaching strategies within their respective curricula. Research studies with outcomes regarding diagnosis, morbidity, and mortality are necessary in these areas.

Anesthesia care is provided by physician anesthesiologists, certified registered nurse anesthetists (CRNAs), anesthesiology residents, and anesthesiologist assistants. These providers may practice alone (anesthesiologists or CRNAs) or in various combinations of supervised roles and teams. Previous studies reveal mixed findings regarding whether patient outcomes differ by anesthesia practice models.^1-7However, little is known about the prevalence of various anesthesia models in the US.

Background

In recent years, anesthesiology has undergone substantial expansion in its scope of services provided, the settings in which it is provided, and the diversity of its workforce.⁸As the field continues to evolve, especially within the context of value-based health care reform, it is imperative to evaluate how anesthesia care models are used in health systems and how these models may optimize care delivery.

The Veterans Health Administration (VHA) is the largest integrated health care system in the US, providing surgical care in 110 inpatient medical centers and 27 ambulatory surgery centers. Despite national integration, anesthesia practices vary widely among facilities. The question of which model of anesthesia care is associated with the best outcomes and offers the most value is widely debated.^1,5,7,9 As an important first step in understanding anesthesia care delivery, a baseline assessment of the practice patterns of anesthesia providers is necessary and may benefit future studies of the impact of these care models on outcomes. Thus, the aim of this work was to understand and describe the previously unassessed landscape of anesthesia care delivery within the VHA.

Methods

As part of a larger evaluation of anesthesia care delivery in the VHA, an observational assessment of anesthesia provider practice patterns was conducted using retrospective surgical data. This project complies with VHA policy pertaining to nonresearch operational activities and did not require institutional review board approval and adheres to the EQUATOR Network guidelines described in Strengthening the Reporting of Observational Studies in Epidemiology (STROBE).¹⁰

Data were obtained from the VHA Managerial Cost Accounting National Data Extract for Surgery package for all surgical procedures (n = 726,706) between October 1, 2013 and March 31, 2015. There were 420 facilities represented in these surgical data. The VHA facility records were used to specifically identify inpatient and ambulatory surgery facilities for inclusion. Additionally, to ensure facilities were valid surgical sites with sufficient surgical volume, those with 100 or fewer cases during the period were excluded. In total, 288 facilities with 9,434 surgical cases (representing 1% of cases) were excluded. These excluded facilities included nursing homes (38%), domiciliaries (26%), outpatient clinics (11%), rehabilitation programs (9%), other nonsurgical facilities (8%), and medical centers (8%). The majority (80%) of excluded medical centers had 30 or fewer surgical cases.

In 6 instances, data from subfacilities were combined with their organizationally affiliated main facilities. The final sample included 125 facilities. The VHA assigns a complexity level designation to facilities, defined as follows: 1a (most complex), 1b, 1c, 2, and 3 (least complex).¹¹ Facilities with 1a designation perform the most complex surgical cases, such as cardiovascular surgery or neurosurgery and have more staff and resource support, whereas levels 2 and 3 facilities perform fewer and less complex cases.

Surgical records were excluded when the primary Current Procedural Terminology (CPT) code was missing (n = 85,748, or 12% of cases). This resulted in 631,524 remaining cases. The surgical CPT codes were mapped to anesthesia CPT codes to obtain the associated base unit (BU) values via a published crosswalk by the American Society of Anesthesiologists (ASA).¹² A higher number of associated BUs indicates a more complex procedure. For example, procedures such as biopsies, arthroscopies, and laparoscopies receive 3 to 4 BUs, whereas a venous thrombectomy of the leg and a transurethral resection of the prostate are both 5 BUs, a total knee arthroplasty is 7 BUs, a craniotomy is 10 BUs, and a coronary artery bypass receives 18 BUs. Surgical case complexity was defined as low (3 or 4 BUs), medium (5 BUs), and high (≥ 6 BUs). Although the VHA has an existing case complexity assignment process based on CPT codes, it defines complexity differently for inpatient facilities and ambulatory surgery centers. Thus, the BU-defined complexity permitted a standardized complexity categorization across all facilities. Categorization of BUs similar to this has previously been used in the literature as a proxy for case complexity.^13,14

Patient-level information included the ASA physical status classification, a measure of overall health status determined by an anesthesia provider preoperatively.¹⁵ These classifications included ASA I (healthy), ASA II (mild systemic disease), ASA III (severe systemic disease), ASA IV (severe systemic disease that is a constant threat to life), and ASA V (moribund patient who is not expected to survive without surgery). The last classification, ASA VI: brain-dead with planned organ donation, was excluded. The “E” subcategory denoting “emergency” was subsumed within the corresponding ASA category (eg, ASA V-E was combined with ASA V).

Provider data identified the principal and supervising (if present) anesthetists involved in the case. The provision of anesthesia care was categorized into 3 models: Model 1—a physician anesthesiologist supervising a CRNA; Model 2—a physician anesthesiologist practicing independently or supervising an anesthesiology resident; and Model 3—a CRNA without supervision. Surgical cases were excluded when there was no anesthesia provider (n = 95,795, or 15% of remaining cases), or a nonanesthesia provider (n = 51,647, or 8% of remaining cases) on record. The final sample was 484,082 surgical cases conducted at 125 facilities.

Related: Improving Care and Reducing Length of Stay in Patients Undergoing Total Knee Replacement

 

Statistical Analysis

The percentage of surgical cases in each anesthesia care model was calculated overall and by the following characteristics: surgical case complexity, ASA classification, and facility complexity. The anesthesia model was determined for each case and summed at the facility level, yielding a total number of cases attributed to each model for each facility, thus identifying the predominant anesthesia model for each facility. The facilities were geographically displayed by their predominant anesthesia model and total number of surgical cases during the period. Because the aim was to present a descriptive representation of anesthesia care models, rather than infer significance, statistical testing was not included.

Results

A total of 484,082 surgical cases met inclusion criteria (Table). These cases were from 109 inpatient facilities and 16 ambulatory surgery facilities. 

More than half (56.8%) of all surgical cases indicated a model of physician anesthesiologist supervising a CRNA (Model 1), whereas 31.6% of cases were categorized as having a physician-driven model (Model 2): physician anesthesiologist practicing independently or supervising a resident), and 11.7% of cases indicated a CRNA without supervision practice model (Model 3).

The percentage of cases in Model 1 was similar across the levels of surgical case complexity. However, a higher proportion of highly complex cases had a physician anesthesiologist (Model 2, 38.8%) than a CRNA (Model 3, 6.4%) as the primary anesthesia provider. Patients in each ASA classification were most likely to receive anesthesia care via Model 1. As ASA level increased, fewer patients had their anesthesia managed by a CRNA without supervision (Model 3: 18.4% of ASA 1 patients vs 8.3% of ASA 4 patients).

Facility complexity demonstrated notable differences in the proportions of surgical cases within each model. More than half of surgical cases in the largest, most complex facilities used Model 1 (64.9%, 58.2%, and 57.7% of cases in 1a, 1b, and 1c facilities, respectively). In comparison, Model 3 was found almost exclusively among surgical cases in smaller facilities with lower complexity (52% and 74% of cases in level 2 and 3 facilities, respectively).

The Figure displays the 125 facilities by their predominant model of anesthesia care. The diameter of the dots is relative to the facility’s total number of surgical cases. For each facility, the predominant model accounted for about half or more of cases but was not necessarily the only model of care used at a particular facility. 

Most facilities (n = 68, 54%) predominantly used Model 1, while 23% (n = 29) predominantly used Model 2, and 22% (n = 28) predominantly used Model 3. Facilities predominately using Model 3 tended to have a smaller case volume. In fact, 85% of level 3 complexity facilities, which have lower surgical volume, used Model 3 as a predominant model of anesthesia care compared with only 6% of level 1a, 1b, and 1c facilities combined.

Related: Initiative to Minimize Pharmaceutical Risk in Older Veterans (IMPROVE) Polypharmacy Clinic

Discussion

Anesthesia care in more than half of surgical cases in VHA facilities was delivered by physician anesthesiologists supervising CRNAs. This model of anesthesia care was the dominant model in 54% of the facilities included in the sample. Consistent with a study of non-VHA facilities, this assessment found that the type of facility may influence the model of anesthesia care, with smaller, less complex facilities more often using a CRNA without supervision model.⁴ In these data, it was noted that among the 28 facilities that predominantly used Model 3, half had 12% or fewer cases that indicated a physician anesthesiologist model of care, and 6 had no cases with physician anesthesiologist involvement. These findings may reflect the limited scope of surgical services offered at lower complexity facilities and/or the reduced availability and/or utilization of physician anesthesiologists in these facilities.

 

Limitations

We recognize limitations in our assessment of anesthesia care. The documented presence or absence of a supervising anesthesia provider on the surgical record may not adequately characterize the model of anesthesia care in use at a facility, thus limiting an understanding of care delivery relationships among anesthesia providers. In addition, the patterns of anesthesia care delivery are likely influenced by factors not accounted for in this assessment, including the labor market share and economic forces.^16,17 The veteran population tends to be older, male, and with substantial chronic disease burden, thus may have differing surgical needs and experiences than that of the general public.^18,19 The surgical services offered in VHA facilities as well as the policies and practice environment surrounding anesthesia care also may vary from those found in nongovernmental facilities. However, as the largest health care system in the US, the VHA provides a diverse and robust surgical program. Many VHA facilities are large teaching hospitals with academic affiliations that would parallel some in the public sector. For example, studies have demonstrated similar surgical outcomes for patients in VHA vs non-VHA facilities.²⁰ Therefore, the findings regarding anesthesia care models in VHA are likely relevant to non-VHA surgical sites.

Related: Improving Team-Based Care Coordination Delivery and Documentation in the Health Record

Conclusion

This preliminary assessment of the different models of anesthesia care demonstrates that although primarily relying on teams of anesthesiologists and CRNAs, the VA also uses unsupervised CRNAs to meet veterans’ surgical care needs. Although CRNA practice without supervision represented only 12% of surgical cases in our data, we identified 28 facilities (22%) that predominantly used CRNAs without supervision. Thus, CRNAs with and without supervision deliver a substantial portion of anesthesia care in the VA. The prevalence of CRNAs in documented VA surgical records and among surgical facilities nationwide highlights the importance of further examining their supervised and unsupervised roles in anesthesia care delivery.²¹ As the practice of anesthesiology continues to evolve, it is imperative that research efforts further investigate ways anesthesia care models may optimize care delivery, benefit anesthesia providers, and improve health outcomes for patients.

Anesthesia care is provided by physician anesthesiologists, certified registered nurse anesthetists (CRNAs), anesthesiology residents, and anesthesiologist assistants. These providers may practice alone (anesthesiologists or CRNAs) or in various combinations of supervised roles and teams. Previous studies reveal mixed findings regarding whether patient outcomes differ by anesthesia practice models.^1-7However, little is known about the prevalence of various anesthesia models in the US.

Background

In recent years, anesthesiology has undergone substantial expansion in its scope of services provided, the settings in which it is provided, and the diversity of its workforce.⁸As the field continues to evolve, especially within the context of value-based health care reform, it is imperative to evaluate how anesthesia care models are used in health systems and how these models may optimize care delivery.

The Veterans Health Administration (VHA) is the largest integrated health care system in the US, providing surgical care in 110 inpatient medical centers and 27 ambulatory surgery centers. Despite national integration, anesthesia practices vary widely among facilities. The question of which model of anesthesia care is associated with the best outcomes and offers the most value is widely debated.^1,5,7,9 As an important first step in understanding anesthesia care delivery, a baseline assessment of the practice patterns of anesthesia providers is necessary and may benefit future studies of the impact of these care models on outcomes. Thus, the aim of this work was to understand and describe the previously unassessed landscape of anesthesia care delivery within the VHA.

Methods

As part of a larger evaluation of anesthesia care delivery in the VHA, an observational assessment of anesthesia provider practice patterns was conducted using retrospective surgical data. This project complies with VHA policy pertaining to nonresearch operational activities and did not require institutional review board approval and adheres to the EQUATOR Network guidelines described in Strengthening the Reporting of Observational Studies in Epidemiology (STROBE).¹⁰

Data were obtained from the VHA Managerial Cost Accounting National Data Extract for Surgery package for all surgical procedures (n = 726,706) between October 1, 2013 and March 31, 2015. There were 420 facilities represented in these surgical data. The VHA facility records were used to specifically identify inpatient and ambulatory surgery facilities for inclusion. Additionally, to ensure facilities were valid surgical sites with sufficient surgical volume, those with 100 or fewer cases during the period were excluded. In total, 288 facilities with 9,434 surgical cases (representing 1% of cases) were excluded. These excluded facilities included nursing homes (38%), domiciliaries (26%), outpatient clinics (11%), rehabilitation programs (9%), other nonsurgical facilities (8%), and medical centers (8%). The majority (80%) of excluded medical centers had 30 or fewer surgical cases.

In 6 instances, data from subfacilities were combined with their organizationally affiliated main facilities. The final sample included 125 facilities. The VHA assigns a complexity level designation to facilities, defined as follows: 1a (most complex), 1b, 1c, 2, and 3 (least complex).¹¹ Facilities with 1a designation perform the most complex surgical cases, such as cardiovascular surgery or neurosurgery and have more staff and resource support, whereas levels 2 and 3 facilities perform fewer and less complex cases.

Surgical records were excluded when the primary Current Procedural Terminology (CPT) code was missing (n = 85,748, or 12% of cases). This resulted in 631,524 remaining cases. The surgical CPT codes were mapped to anesthesia CPT codes to obtain the associated base unit (BU) values via a published crosswalk by the American Society of Anesthesiologists (ASA).¹² A higher number of associated BUs indicates a more complex procedure. For example, procedures such as biopsies, arthroscopies, and laparoscopies receive 3 to 4 BUs, whereas a venous thrombectomy of the leg and a transurethral resection of the prostate are both 5 BUs, a total knee arthroplasty is 7 BUs, a craniotomy is 10 BUs, and a coronary artery bypass receives 18 BUs. Surgical case complexity was defined as low (3 or 4 BUs), medium (5 BUs), and high (≥ 6 BUs). Although the VHA has an existing case complexity assignment process based on CPT codes, it defines complexity differently for inpatient facilities and ambulatory surgery centers. Thus, the BU-defined complexity permitted a standardized complexity categorization across all facilities. Categorization of BUs similar to this has previously been used in the literature as a proxy for case complexity.^13,14

Patient-level information included the ASA physical status classification, a measure of overall health status determined by an anesthesia provider preoperatively.¹⁵ These classifications included ASA I (healthy), ASA II (mild systemic disease), ASA III (severe systemic disease), ASA IV (severe systemic disease that is a constant threat to life), and ASA V (moribund patient who is not expected to survive without surgery). The last classification, ASA VI: brain-dead with planned organ donation, was excluded. The “E” subcategory denoting “emergency” was subsumed within the corresponding ASA category (eg, ASA V-E was combined with ASA V).

Provider data identified the principal and supervising (if present) anesthetists involved in the case. The provision of anesthesia care was categorized into 3 models: Model 1—a physician anesthesiologist supervising a CRNA; Model 2—a physician anesthesiologist practicing independently or supervising an anesthesiology resident; and Model 3—a CRNA without supervision. Surgical cases were excluded when there was no anesthesia provider (n = 95,795, or 15% of remaining cases), or a nonanesthesia provider (n = 51,647, or 8% of remaining cases) on record. The final sample was 484,082 surgical cases conducted at 125 facilities.

Related: Improving Care and Reducing Length of Stay in Patients Undergoing Total Knee Replacement

 

Statistical Analysis

The percentage of surgical cases in each anesthesia care model was calculated overall and by the following characteristics: surgical case complexity, ASA classification, and facility complexity. The anesthesia model was determined for each case and summed at the facility level, yielding a total number of cases attributed to each model for each facility, thus identifying the predominant anesthesia model for each facility. The facilities were geographically displayed by their predominant anesthesia model and total number of surgical cases during the period. Because the aim was to present a descriptive representation of anesthesia care models, rather than infer significance, statistical testing was not included.

Results

A total of 484,082 surgical cases met inclusion criteria (Table). These cases were from 109 inpatient facilities and 16 ambulatory surgery facilities. 

More than half (56.8%) of all surgical cases indicated a model of physician anesthesiologist supervising a CRNA (Model 1), whereas 31.6% of cases were categorized as having a physician-driven model (Model 2): physician anesthesiologist practicing independently or supervising a resident), and 11.7% of cases indicated a CRNA without supervision practice model (Model 3).

The percentage of cases in Model 1 was similar across the levels of surgical case complexity. However, a higher proportion of highly complex cases had a physician anesthesiologist (Model 2, 38.8%) than a CRNA (Model 3, 6.4%) as the primary anesthesia provider. Patients in each ASA classification were most likely to receive anesthesia care via Model 1. As ASA level increased, fewer patients had their anesthesia managed by a CRNA without supervision (Model 3: 18.4% of ASA 1 patients vs 8.3% of ASA 4 patients).

Facility complexity demonstrated notable differences in the proportions of surgical cases within each model. More than half of surgical cases in the largest, most complex facilities used Model 1 (64.9%, 58.2%, and 57.7% of cases in 1a, 1b, and 1c facilities, respectively). In comparison, Model 3 was found almost exclusively among surgical cases in smaller facilities with lower complexity (52% and 74% of cases in level 2 and 3 facilities, respectively).

The Figure displays the 125 facilities by their predominant model of anesthesia care. The diameter of the dots is relative to the facility’s total number of surgical cases. For each facility, the predominant model accounted for about half or more of cases but was not necessarily the only model of care used at a particular facility. 

Most facilities (n = 68, 54%) predominantly used Model 1, while 23% (n = 29) predominantly used Model 2, and 22% (n = 28) predominantly used Model 3. Facilities predominately using Model 3 tended to have a smaller case volume. In fact, 85% of level 3 complexity facilities, which have lower surgical volume, used Model 3 as a predominant model of anesthesia care compared with only 6% of level 1a, 1b, and 1c facilities combined.

Related: Initiative to Minimize Pharmaceutical Risk in Older Veterans (IMPROVE) Polypharmacy Clinic

Discussion

Anesthesia care in more than half of surgical cases in VHA facilities was delivered by physician anesthesiologists supervising CRNAs. This model of anesthesia care was the dominant model in 54% of the facilities included in the sample. Consistent with a study of non-VHA facilities, this assessment found that the type of facility may influence the model of anesthesia care, with smaller, less complex facilities more often using a CRNA without supervision model.⁴ In these data, it was noted that among the 28 facilities that predominantly used Model 3, half had 12% or fewer cases that indicated a physician anesthesiologist model of care, and 6 had no cases with physician anesthesiologist involvement. These findings may reflect the limited scope of surgical services offered at lower complexity facilities and/or the reduced availability and/or utilization of physician anesthesiologists in these facilities.

 

Limitations

We recognize limitations in our assessment of anesthesia care. The documented presence or absence of a supervising anesthesia provider on the surgical record may not adequately characterize the model of anesthesia care in use at a facility, thus limiting an understanding of care delivery relationships among anesthesia providers. In addition, the patterns of anesthesia care delivery are likely influenced by factors not accounted for in this assessment, including the labor market share and economic forces.^16,17 The veteran population tends to be older, male, and with substantial chronic disease burden, thus may have differing surgical needs and experiences than that of the general public.^18,19 The surgical services offered in VHA facilities as well as the policies and practice environment surrounding anesthesia care also may vary from those found in nongovernmental facilities. However, as the largest health care system in the US, the VHA provides a diverse and robust surgical program. Many VHA facilities are large teaching hospitals with academic affiliations that would parallel some in the public sector. For example, studies have demonstrated similar surgical outcomes for patients in VHA vs non-VHA facilities.²⁰ Therefore, the findings regarding anesthesia care models in VHA are likely relevant to non-VHA surgical sites.

Related: Improving Team-Based Care Coordination Delivery and Documentation in the Health Record

Conclusion

This preliminary assessment of the different models of anesthesia care demonstrates that although primarily relying on teams of anesthesiologists and CRNAs, the VA also uses unsupervised CRNAs to meet veterans’ surgical care needs. Although CRNA practice without supervision represented only 12% of surgical cases in our data, we identified 28 facilities (22%) that predominantly used CRNAs without supervision. Thus, CRNAs with and without supervision deliver a substantial portion of anesthesia care in the VA. The prevalence of CRNAs in documented VA surgical records and among surgical facilities nationwide highlights the importance of further examining their supervised and unsupervised roles in anesthesia care delivery.²¹ As the practice of anesthesiology continues to evolve, it is imperative that research efforts further investigate ways anesthesia care models may optimize care delivery, benefit anesthesia providers, and improve health outcomes for patients.

Anesthesia care is provided by physician anesthesiologists, certified registered nurse anesthetists (CRNAs), anesthesiology residents, and anesthesiologist assistants. These providers may practice alone (anesthesiologists or CRNAs) or in various combinations of supervised roles and teams. Previous studies reveal mixed findings regarding whether patient outcomes differ by anesthesia practice models.^1-7However, little is known about the prevalence of various anesthesia models in the US.

Background

In recent years, anesthesiology has undergone substantial expansion in its scope of services provided, the settings in which it is provided, and the diversity of its workforce.⁸As the field continues to evolve, especially within the context of value-based health care reform, it is imperative to evaluate how anesthesia care models are used in health systems and how these models may optimize care delivery.

The Veterans Health Administration (VHA) is the largest integrated health care system in the US, providing surgical care in 110 inpatient medical centers and 27 ambulatory surgery centers. Despite national integration, anesthesia practices vary widely among facilities. The question of which model of anesthesia care is associated with the best outcomes and offers the most value is widely debated.^1,5,7,9 As an important first step in understanding anesthesia care delivery, a baseline assessment of the practice patterns of anesthesia providers is necessary and may benefit future studies of the impact of these care models on outcomes. Thus, the aim of this work was to understand and describe the previously unassessed landscape of anesthesia care delivery within the VHA.

Methods

As part of a larger evaluation of anesthesia care delivery in the VHA, an observational assessment of anesthesia provider practice patterns was conducted using retrospective surgical data. This project complies with VHA policy pertaining to nonresearch operational activities and did not require institutional review board approval and adheres to the EQUATOR Network guidelines described in Strengthening the Reporting of Observational Studies in Epidemiology (STROBE).¹⁰

Data were obtained from the VHA Managerial Cost Accounting National Data Extract for Surgery package for all surgical procedures (n = 726,706) between October 1, 2013 and March 31, 2015. There were 420 facilities represented in these surgical data. The VHA facility records were used to specifically identify inpatient and ambulatory surgery facilities for inclusion. Additionally, to ensure facilities were valid surgical sites with sufficient surgical volume, those with 100 or fewer cases during the period were excluded. In total, 288 facilities with 9,434 surgical cases (representing 1% of cases) were excluded. These excluded facilities included nursing homes (38%), domiciliaries (26%), outpatient clinics (11%), rehabilitation programs (9%), other nonsurgical facilities (8%), and medical centers (8%). The majority (80%) of excluded medical centers had 30 or fewer surgical cases.

In 6 instances, data from subfacilities were combined with their organizationally affiliated main facilities. The final sample included 125 facilities. The VHA assigns a complexity level designation to facilities, defined as follows: 1a (most complex), 1b, 1c, 2, and 3 (least complex).¹¹ Facilities with 1a designation perform the most complex surgical cases, such as cardiovascular surgery or neurosurgery and have more staff and resource support, whereas levels 2 and 3 facilities perform fewer and less complex cases.

Surgical records were excluded when the primary Current Procedural Terminology (CPT) code was missing (n = 85,748, or 12% of cases). This resulted in 631,524 remaining cases. The surgical CPT codes were mapped to anesthesia CPT codes to obtain the associated base unit (BU) values via a published crosswalk by the American Society of Anesthesiologists (ASA).¹² A higher number of associated BUs indicates a more complex procedure. For example, procedures such as biopsies, arthroscopies, and laparoscopies receive 3 to 4 BUs, whereas a venous thrombectomy of the leg and a transurethral resection of the prostate are both 5 BUs, a total knee arthroplasty is 7 BUs, a craniotomy is 10 BUs, and a coronary artery bypass receives 18 BUs. Surgical case complexity was defined as low (3 or 4 BUs), medium (5 BUs), and high (≥ 6 BUs). Although the VHA has an existing case complexity assignment process based on CPT codes, it defines complexity differently for inpatient facilities and ambulatory surgery centers. Thus, the BU-defined complexity permitted a standardized complexity categorization across all facilities. Categorization of BUs similar to this has previously been used in the literature as a proxy for case complexity.^13,14

Patient-level information included the ASA physical status classification, a measure of overall health status determined by an anesthesia provider preoperatively.¹⁵ These classifications included ASA I (healthy), ASA II (mild systemic disease), ASA III (severe systemic disease), ASA IV (severe systemic disease that is a constant threat to life), and ASA V (moribund patient who is not expected to survive without surgery). The last classification, ASA VI: brain-dead with planned organ donation, was excluded. The “E” subcategory denoting “emergency” was subsumed within the corresponding ASA category (eg, ASA V-E was combined with ASA V).

Provider data identified the principal and supervising (if present) anesthetists involved in the case. The provision of anesthesia care was categorized into 3 models: Model 1—a physician anesthesiologist supervising a CRNA; Model 2—a physician anesthesiologist practicing independently or supervising an anesthesiology resident; and Model 3—a CRNA without supervision. Surgical cases were excluded when there was no anesthesia provider (n = 95,795, or 15% of remaining cases), or a nonanesthesia provider (n = 51,647, or 8% of remaining cases) on record. The final sample was 484,082 surgical cases conducted at 125 facilities.

Related: Improving Care and Reducing Length of Stay in Patients Undergoing Total Knee Replacement

 

Statistical Analysis

The percentage of surgical cases in each anesthesia care model was calculated overall and by the following characteristics: surgical case complexity, ASA classification, and facility complexity. The anesthesia model was determined for each case and summed at the facility level, yielding a total number of cases attributed to each model for each facility, thus identifying the predominant anesthesia model for each facility. The facilities were geographically displayed by their predominant anesthesia model and total number of surgical cases during the period. Because the aim was to present a descriptive representation of anesthesia care models, rather than infer significance, statistical testing was not included.

Results

A total of 484,082 surgical cases met inclusion criteria (Table). These cases were from 109 inpatient facilities and 16 ambulatory surgery facilities. 

More than half (56.8%) of all surgical cases indicated a model of physician anesthesiologist supervising a CRNA (Model 1), whereas 31.6% of cases were categorized as having a physician-driven model (Model 2): physician anesthesiologist practicing independently or supervising a resident), and 11.7% of cases indicated a CRNA without supervision practice model (Model 3).

The percentage of cases in Model 1 was similar across the levels of surgical case complexity. However, a higher proportion of highly complex cases had a physician anesthesiologist (Model 2, 38.8%) than a CRNA (Model 3, 6.4%) as the primary anesthesia provider. Patients in each ASA classification were most likely to receive anesthesia care via Model 1. As ASA level increased, fewer patients had their anesthesia managed by a CRNA without supervision (Model 3: 18.4% of ASA 1 patients vs 8.3% of ASA 4 patients).

Facility complexity demonstrated notable differences in the proportions of surgical cases within each model. More than half of surgical cases in the largest, most complex facilities used Model 1 (64.9%, 58.2%, and 57.7% of cases in 1a, 1b, and 1c facilities, respectively). In comparison, Model 3 was found almost exclusively among surgical cases in smaller facilities with lower complexity (52% and 74% of cases in level 2 and 3 facilities, respectively).

The Figure displays the 125 facilities by their predominant model of anesthesia care. The diameter of the dots is relative to the facility’s total number of surgical cases. For each facility, the predominant model accounted for about half or more of cases but was not necessarily the only model of care used at a particular facility. 

Most facilities (n = 68, 54%) predominantly used Model 1, while 23% (n = 29) predominantly used Model 2, and 22% (n = 28) predominantly used Model 3. Facilities predominately using Model 3 tended to have a smaller case volume. In fact, 85% of level 3 complexity facilities, which have lower surgical volume, used Model 3 as a predominant model of anesthesia care compared with only 6% of level 1a, 1b, and 1c facilities combined.

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Discussion

Anesthesia care in more than half of surgical cases in VHA facilities was delivered by physician anesthesiologists supervising CRNAs. This model of anesthesia care was the dominant model in 54% of the facilities included in the sample. Consistent with a study of non-VHA facilities, this assessment found that the type of facility may influence the model of anesthesia care, with smaller, less complex facilities more often using a CRNA without supervision model.⁴ In these data, it was noted that among the 28 facilities that predominantly used Model 3, half had 12% or fewer cases that indicated a physician anesthesiologist model of care, and 6 had no cases with physician anesthesiologist involvement. These findings may reflect the limited scope of surgical services offered at lower complexity facilities and/or the reduced availability and/or utilization of physician anesthesiologists in these facilities.

 

Limitations

We recognize limitations in our assessment of anesthesia care. The documented presence or absence of a supervising anesthesia provider on the surgical record may not adequately characterize the model of anesthesia care in use at a facility, thus limiting an understanding of care delivery relationships among anesthesia providers. In addition, the patterns of anesthesia care delivery are likely influenced by factors not accounted for in this assessment, including the labor market share and economic forces.^16,17 The veteran population tends to be older, male, and with substantial chronic disease burden, thus may have differing surgical needs and experiences than that of the general public.^18,19 The surgical services offered in VHA facilities as well as the policies and practice environment surrounding anesthesia care also may vary from those found in nongovernmental facilities. However, as the largest health care system in the US, the VHA provides a diverse and robust surgical program. Many VHA facilities are large teaching hospitals with academic affiliations that would parallel some in the public sector. For example, studies have demonstrated similar surgical outcomes for patients in VHA vs non-VHA facilities.²⁰ Therefore, the findings regarding anesthesia care models in VHA are likely relevant to non-VHA surgical sites.

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Conclusion

This preliminary assessment of the different models of anesthesia care demonstrates that although primarily relying on teams of anesthesiologists and CRNAs, the VA also uses unsupervised CRNAs to meet veterans’ surgical care needs. Although CRNA practice without supervision represented only 12% of surgical cases in our data, we identified 28 facilities (22%) that predominantly used CRNAs without supervision. Thus, CRNAs with and without supervision deliver a substantial portion of anesthesia care in the VA. The prevalence of CRNAs in documented VA surgical records and among surgical facilities nationwide highlights the importance of further examining their supervised and unsupervised roles in anesthesia care delivery.²¹ As the practice of anesthesiology continues to evolve, it is imperative that research efforts further investigate ways anesthesia care models may optimize care delivery, benefit anesthesia providers, and improve health outcomes for patients.