Hyponatremia is an electrolyte disorder most commonly defined as a serum sodium concentration <135 mEq/L.1 Its exact definition can vary across studies, but typically ranges between <130 and <138 mEq/L.2, 3 Signs and symptoms of hyponatremia can include malaise, headache, disorientation, confusion, muscle weakness, and cramps. If severe, seizures, respiratory arrest, brainstem herniation, coma, and death may result.

The incidence of hyponatremia in the general hospitalized population has been reported to range between 1% and 6% when defined as <130135 mEq/L,4, 5 and its occurrence increases with a more prolonged hospital stay to 13%.6 A recent study reported that when hyponatremia was defined with a less stringent threshold of <138 mEq/L, the incidence at admission rose to 38%.3 Hyponatremia is a comorbid condition of multiple diseases, occurring in approximately 20% of patients with heart failure,7, 8 and 40% to 57% of patients with advanced cirrhosis.9, 10 The syndrome of the inappropriate release of antidiuretic hormone (SIADH) is additionally a predominant cause of hyponatremia, with a prevalence reported as high as 35% in hospitalized patients.11

Hyponatremia is not only widespread, but also an independent predictor of mortality. In a retrospective cohort analysis, Waikar et al reported that in comparison to patients who were normonatremic, patients with serum sodium concentrations <135 mEq/L had a risk of in‐hospital mortality as high as 47%, and that this risk doubled for patients with serum sodium concentrations between 125 and 129 mEq/L.6 In the study by Wald et al, which defined hyponatremia as <138 mEq/L, the risk of in‐hospital mortality was similar.3 In both of these studies, even mild hyponatremia (130137 mEq/L) was associated with increased risk of in‐hospital mortality.3, 6

The overall cost of hyponatremia is estimated to range between $1.6 and $3.6 billion for 2011.12, 13 Hospital readmissions are a significant contributor to total healthcare costs, with some being entirely avoidable with increased standards of care. The Centers for Medicare and Medicaid Services has begun to not only publicly report hospital readmission rates, but also penalize hospitals for early readmissions.14 Strategies to reduce hospital readmissions are currently being integrated into healthcare reform policy.14 In the present study, the incremental burden of hospitalized hyponatremic (HN) versus non‐HN patients in terms of hospital resource utilization, costs, and early hospital readmission in the real‐world was evaluated.

METHODS

RESULTS

DISCUSSION

This large‐scale, real‐world hospital database study provides healthcare utilization and cost data on the largest population of HN patients that has currently been studied. The results of this study are consistent with others in showing that HN patients use healthcare services more extensively, and represent a patient population which is more expensive to treat in the inpatient setting.5, 22 Additionally, this study yields new findings in that patients in the real‐world with hyponatremia resulting from various etiologies are more likely to be readmitted to the hospital than patients with similar demographics and characteristics who do not have hyponatremia. The results of the subgroup analysis were generally consistent with the results for the overall matched population, as the incremental burden estimates were directionally consistent. However, among the specific subgroups of patients, although it appears that hyponatremia is predictive of hospital readmission in patients with CHF, it did not necessarily correspond with hospital readmission rates among patients with CAP, UTI, or COPD. Therefore, hyponatremia, at least for patients with these latter conditions, may not be predictive of readmission, but remains associated with increased healthcare utilization during the initial hospitalization. Further evaluation of the incremental burden of hyponatremia among patients with specific disease conditions is needed to validate the findings.

The difference in hospital LOS at first admission between HN and non‐HN patients in this study was 1.1 days, and comparable to that reported by Shorr et al.23 In the study by Shorr et al, patients hospitalized for congestive heart failure (CHF), who were HN, had a 0.5 day increased LOS, and those who were severely HN had a 1.3 day increased LOS.23 Two other retrospective cohort analyses reported a 1.4 day5 and 2.0 day22 increased LOS for HN patients in comparison to non‐HN patients. Zilberberg et al and Callahan et al additionally reported that HN patients had a significantly greater need for ICU (4%10%).5, 22 In the present study, LOS in the ICU and associated costs were also compared among HN and non‐HN cohorts and, after adjustment for key patient characteristics, hyponatremia was associated with an incremental increase of 10.2% for ICU LOS and an 8.9% increase in ICU cost.

In this study, patients with hyponatremia of any severity were found to have a mean increase in hospital cost per admission of $1842, with multivariate analysis demonstrating an associated incremental increase of 8.2%. These results are also comparable to that reported in other retrospective cohort analyses. Shorr et al reported that in‐hospital costs attributed to hyponatremia were $509, and for severe hyponatremia were $1132.23 Callahan et al reported a $1200 increase in costs per admission for patients with mild‐to‐moderate hyponatremia, and a $3540 increase for patients with moderate‐to‐severe hyponatremia, in comparison to non‐HN patients.22

The Institute for Healthcare Improvement reports that hospitalizations account for nearly one‐third of the $882 billion spent on healthcare in the US in 2011, and that a substantial fraction of all hospitalizations are readmissions.13, 24 A recent meta‐analysis of 30‐day hospital readmission rates found 23.1% were classified as avoidable.25 Readmission rates of HN patients in comparison to non‐HN patients have been reported in 3 other studies, all of which were conducted on clinical trial patients with heart failure.7, 8, 26 Two of these studies, which evaluated only patients with acute class IV heart failure,8, 26 reported that hyponatremia was associated with a significant increase in readmission rates (up to 20% higher), and the other, which evaluated any patients hospitalized for heart failure, did not.7 The differences there may be partially attributed to the differences in heart failure severity among patient populations, as hyponatremia is an independent predictor of worsening heart failure. The only published study to date on the influence of hyponatremia on readmission rates in the real‐world was conducted by Scherz et al, who reported that the co‐occurrence of hyponatremia in patients with acute pulmonary embolism, discharged from 185 hospitals in Pennsylvania, was independently associated with an increased readmission rate of 19.3%.27 In the present study, hyponatremia was associated with an incremental increase ranging between 14% and 17% for hospital readmission for any cause. It was conducted on a patient population in which hyponatremia was resultant from many causes, and not all patients had a serious comorbid condition. Also, the HN and non‐HN cohorts were matched for comorbidity prevalence and disease severity, and in other studies they were not. Therefore, the results of this study importantly imply that hyponatremia, whether it is resultant from a serious disease or any other cause substantially increases the healthcare burden. The implementation of strategies to prevent hospital readmissions may play an important role in reducing the healthcare burden of hyponatremia, and future studies are warranted to evaluate this hypothesis alongside evaluation of the outpatient hyponatremia burden.

The limitations of this study include, firstly, that it is only representative of inpatient hospital costs, and excludes outpatient healthcare utilization and costs. Secondly, this study utilized the Premier Hospital Database for patient selection, and laboratory testing data for serum sodium level are not available in this database; therefore, the severity of hyponatremia could not be accurately established in the HN patient population. Thirdly, the occurrence of hyponatremia in patients with some diseases is a marker of disease severity, as is the case with congestive heart failure and cirrhosis.23, 28 Our study did not adjust for the specific disease (eg, CHF) severity, which may influence the results. Future research is needed to evaluate the impact of hyponatremia on underlying disease severity of other diseases, and how its co‐occurrence may influence healthcare resource utilization and cost in each case. Although the Premier Hospital Database contains information from a large number of hospitals across the US, it is possible that it may not be representative of the entire US population of HN patients. Additionally, billing and coding errors and missing data could potentially have occurred, although the large patient population size likely precludes a large impact on the results of this study. Finally, the frequency of use of fluid restriction in these hospital settings could not be chronicled, thus limiting the ability to assess therapies and treatment modalities in use.

Hyponatremia is an electrolyte disorder most commonly defined as a serum sodium concentration <135 mEq/L.1 Its exact definition can vary across studies, but typically ranges between <130 and <138 mEq/L.2, 3 Signs and symptoms of hyponatremia can include malaise, headache, disorientation, confusion, muscle weakness, and cramps. If severe, seizures, respiratory arrest, brainstem herniation, coma, and death may result.

The incidence of hyponatremia in the general hospitalized population has been reported to range between 1% and 6% when defined as <130135 mEq/L,4, 5 and its occurrence increases with a more prolonged hospital stay to 13%.6 A recent study reported that when hyponatremia was defined with a less stringent threshold of <138 mEq/L, the incidence at admission rose to 38%.3 Hyponatremia is a comorbid condition of multiple diseases, occurring in approximately 20% of patients with heart failure,7, 8 and 40% to 57% of patients with advanced cirrhosis.9, 10 The syndrome of the inappropriate release of antidiuretic hormone (SIADH) is additionally a predominant cause of hyponatremia, with a prevalence reported as high as 35% in hospitalized patients.11

Hyponatremia is not only widespread, but also an independent predictor of mortality. In a retrospective cohort analysis, Waikar et al reported that in comparison to patients who were normonatremic, patients with serum sodium concentrations <135 mEq/L had a risk of in‐hospital mortality as high as 47%, and that this risk doubled for patients with serum sodium concentrations between 125 and 129 mEq/L.6 In the study by Wald et al, which defined hyponatremia as <138 mEq/L, the risk of in‐hospital mortality was similar.3 In both of these studies, even mild hyponatremia (130137 mEq/L) was associated with increased risk of in‐hospital mortality.3, 6

The overall cost of hyponatremia is estimated to range between $1.6 and $3.6 billion for 2011.12, 13 Hospital readmissions are a significant contributor to total healthcare costs, with some being entirely avoidable with increased standards of care. The Centers for Medicare and Medicaid Services has begun to not only publicly report hospital readmission rates, but also penalize hospitals for early readmissions.14 Strategies to reduce hospital readmissions are currently being integrated into healthcare reform policy.14 In the present study, the incremental burden of hospitalized hyponatremic (HN) versus non‐HN patients in terms of hospital resource utilization, costs, and early hospital readmission in the real‐world was evaluated.

METHODS

RESULTS

DISCUSSION

This large‐scale, real‐world hospital database study provides healthcare utilization and cost data on the largest population of HN patients that has currently been studied. The results of this study are consistent with others in showing that HN patients use healthcare services more extensively, and represent a patient population which is more expensive to treat in the inpatient setting.5, 22 Additionally, this study yields new findings in that patients in the real‐world with hyponatremia resulting from various etiologies are more likely to be readmitted to the hospital than patients with similar demographics and characteristics who do not have hyponatremia. The results of the subgroup analysis were generally consistent with the results for the overall matched population, as the incremental burden estimates were directionally consistent. However, among the specific subgroups of patients, although it appears that hyponatremia is predictive of hospital readmission in patients with CHF, it did not necessarily correspond with hospital readmission rates among patients with CAP, UTI, or COPD. Therefore, hyponatremia, at least for patients with these latter conditions, may not be predictive of readmission, but remains associated with increased healthcare utilization during the initial hospitalization. Further evaluation of the incremental burden of hyponatremia among patients with specific disease conditions is needed to validate the findings.

The difference in hospital LOS at first admission between HN and non‐HN patients in this study was 1.1 days, and comparable to that reported by Shorr et al.23 In the study by Shorr et al, patients hospitalized for congestive heart failure (CHF), who were HN, had a 0.5 day increased LOS, and those who were severely HN had a 1.3 day increased LOS.23 Two other retrospective cohort analyses reported a 1.4 day5 and 2.0 day22 increased LOS for HN patients in comparison to non‐HN patients. Zilberberg et al and Callahan et al additionally reported that HN patients had a significantly greater need for ICU (4%10%).5, 22 In the present study, LOS in the ICU and associated costs were also compared among HN and non‐HN cohorts and, after adjustment for key patient characteristics, hyponatremia was associated with an incremental increase of 10.2% for ICU LOS and an 8.9% increase in ICU cost.

In this study, patients with hyponatremia of any severity were found to have a mean increase in hospital cost per admission of $1842, with multivariate analysis demonstrating an associated incremental increase of 8.2%. These results are also comparable to that reported in other retrospective cohort analyses. Shorr et al reported that in‐hospital costs attributed to hyponatremia were $509, and for severe hyponatremia were $1132.23 Callahan et al reported a $1200 increase in costs per admission for patients with mild‐to‐moderate hyponatremia, and a $3540 increase for patients with moderate‐to‐severe hyponatremia, in comparison to non‐HN patients.22

The Institute for Healthcare Improvement reports that hospitalizations account for nearly one‐third of the $882 billion spent on healthcare in the US in 2011, and that a substantial fraction of all hospitalizations are readmissions.13, 24 A recent meta‐analysis of 30‐day hospital readmission rates found 23.1% were classified as avoidable.25 Readmission rates of HN patients in comparison to non‐HN patients have been reported in 3 other studies, all of which were conducted on clinical trial patients with heart failure.7, 8, 26 Two of these studies, which evaluated only patients with acute class IV heart failure,8, 26 reported that hyponatremia was associated with a significant increase in readmission rates (up to 20% higher), and the other, which evaluated any patients hospitalized for heart failure, did not.7 The differences there may be partially attributed to the differences in heart failure severity among patient populations, as hyponatremia is an independent predictor of worsening heart failure. The only published study to date on the influence of hyponatremia on readmission rates in the real‐world was conducted by Scherz et al, who reported that the co‐occurrence of hyponatremia in patients with acute pulmonary embolism, discharged from 185 hospitals in Pennsylvania, was independently associated with an increased readmission rate of 19.3%.27 In the present study, hyponatremia was associated with an incremental increase ranging between 14% and 17% for hospital readmission for any cause. It was conducted on a patient population in which hyponatremia was resultant from many causes, and not all patients had a serious comorbid condition. Also, the HN and non‐HN cohorts were matched for comorbidity prevalence and disease severity, and in other studies they were not. Therefore, the results of this study importantly imply that hyponatremia, whether it is resultant from a serious disease or any other cause substantially increases the healthcare burden. The implementation of strategies to prevent hospital readmissions may play an important role in reducing the healthcare burden of hyponatremia, and future studies are warranted to evaluate this hypothesis alongside evaluation of the outpatient hyponatremia burden.

The limitations of this study include, firstly, that it is only representative of inpatient hospital costs, and excludes outpatient healthcare utilization and costs. Secondly, this study utilized the Premier Hospital Database for patient selection, and laboratory testing data for serum sodium level are not available in this database; therefore, the severity of hyponatremia could not be accurately established in the HN patient population. Thirdly, the occurrence of hyponatremia in patients with some diseases is a marker of disease severity, as is the case with congestive heart failure and cirrhosis.23, 28 Our study did not adjust for the specific disease (eg, CHF) severity, which may influence the results. Future research is needed to evaluate the impact of hyponatremia on underlying disease severity of other diseases, and how its co‐occurrence may influence healthcare resource utilization and cost in each case. Although the Premier Hospital Database contains information from a large number of hospitals across the US, it is possible that it may not be representative of the entire US population of HN patients. Additionally, billing and coding errors and missing data could potentially have occurred, although the large patient population size likely precludes a large impact on the results of this study. Finally, the frequency of use of fluid restriction in these hospital settings could not be chronicled, thus limiting the ability to assess therapies and treatment modalities in use.

Hyponatremia is an electrolyte disorder most commonly defined as a serum sodium concentration <135 mEq/L.1 Its exact definition can vary across studies, but typically ranges between <130 and <138 mEq/L.2, 3 Signs and symptoms of hyponatremia can include malaise, headache, disorientation, confusion, muscle weakness, and cramps. If severe, seizures, respiratory arrest, brainstem herniation, coma, and death may result.

The incidence of hyponatremia in the general hospitalized population has been reported to range between 1% and 6% when defined as <130135 mEq/L,4, 5 and its occurrence increases with a more prolonged hospital stay to 13%.6 A recent study reported that when hyponatremia was defined with a less stringent threshold of <138 mEq/L, the incidence at admission rose to 38%.3 Hyponatremia is a comorbid condition of multiple diseases, occurring in approximately 20% of patients with heart failure,7, 8 and 40% to 57% of patients with advanced cirrhosis.9, 10 The syndrome of the inappropriate release of antidiuretic hormone (SIADH) is additionally a predominant cause of hyponatremia, with a prevalence reported as high as 35% in hospitalized patients.11

Hyponatremia is not only widespread, but also an independent predictor of mortality. In a retrospective cohort analysis, Waikar et al reported that in comparison to patients who were normonatremic, patients with serum sodium concentrations <135 mEq/L had a risk of in‐hospital mortality as high as 47%, and that this risk doubled for patients with serum sodium concentrations between 125 and 129 mEq/L.6 In the study by Wald et al, which defined hyponatremia as <138 mEq/L, the risk of in‐hospital mortality was similar.3 In both of these studies, even mild hyponatremia (130137 mEq/L) was associated with increased risk of in‐hospital mortality.3, 6

The overall cost of hyponatremia is estimated to range between $1.6 and $3.6 billion for 2011.12, 13 Hospital readmissions are a significant contributor to total healthcare costs, with some being entirely avoidable with increased standards of care. The Centers for Medicare and Medicaid Services has begun to not only publicly report hospital readmission rates, but also penalize hospitals for early readmissions.14 Strategies to reduce hospital readmissions are currently being integrated into healthcare reform policy.14 In the present study, the incremental burden of hospitalized hyponatremic (HN) versus non‐HN patients in terms of hospital resource utilization, costs, and early hospital readmission in the real‐world was evaluated.

METHODS

RESULTS

DISCUSSION

This large‐scale, real‐world hospital database study provides healthcare utilization and cost data on the largest population of HN patients that has currently been studied. The results of this study are consistent with others in showing that HN patients use healthcare services more extensively, and represent a patient population which is more expensive to treat in the inpatient setting.5, 22 Additionally, this study yields new findings in that patients in the real‐world with hyponatremia resulting from various etiologies are more likely to be readmitted to the hospital than patients with similar demographics and characteristics who do not have hyponatremia. The results of the subgroup analysis were generally consistent with the results for the overall matched population, as the incremental burden estimates were directionally consistent. However, among the specific subgroups of patients, although it appears that hyponatremia is predictive of hospital readmission in patients with CHF, it did not necessarily correspond with hospital readmission rates among patients with CAP, UTI, or COPD. Therefore, hyponatremia, at least for patients with these latter conditions, may not be predictive of readmission, but remains associated with increased healthcare utilization during the initial hospitalization. Further evaluation of the incremental burden of hyponatremia among patients with specific disease conditions is needed to validate the findings.

The difference in hospital LOS at first admission between HN and non‐HN patients in this study was 1.1 days, and comparable to that reported by Shorr et al.23 In the study by Shorr et al, patients hospitalized for congestive heart failure (CHF), who were HN, had a 0.5 day increased LOS, and those who were severely HN had a 1.3 day increased LOS.23 Two other retrospective cohort analyses reported a 1.4 day5 and 2.0 day22 increased LOS for HN patients in comparison to non‐HN patients. Zilberberg et al and Callahan et al additionally reported that HN patients had a significantly greater need for ICU (4%10%).5, 22 In the present study, LOS in the ICU and associated costs were also compared among HN and non‐HN cohorts and, after adjustment for key patient characteristics, hyponatremia was associated with an incremental increase of 10.2% for ICU LOS and an 8.9% increase in ICU cost.

In this study, patients with hyponatremia of any severity were found to have a mean increase in hospital cost per admission of $1842, with multivariate analysis demonstrating an associated incremental increase of 8.2%. These results are also comparable to that reported in other retrospective cohort analyses. Shorr et al reported that in‐hospital costs attributed to hyponatremia were $509, and for severe hyponatremia were $1132.23 Callahan et al reported a $1200 increase in costs per admission for patients with mild‐to‐moderate hyponatremia, and a $3540 increase for patients with moderate‐to‐severe hyponatremia, in comparison to non‐HN patients.22

The Institute for Healthcare Improvement reports that hospitalizations account for nearly one‐third of the $882 billion spent on healthcare in the US in 2011, and that a substantial fraction of all hospitalizations are readmissions.13, 24 A recent meta‐analysis of 30‐day hospital readmission rates found 23.1% were classified as avoidable.25 Readmission rates of HN patients in comparison to non‐HN patients have been reported in 3 other studies, all of which were conducted on clinical trial patients with heart failure.7, 8, 26 Two of these studies, which evaluated only patients with acute class IV heart failure,8, 26 reported that hyponatremia was associated with a significant increase in readmission rates (up to 20% higher), and the other, which evaluated any patients hospitalized for heart failure, did not.7 The differences there may be partially attributed to the differences in heart failure severity among patient populations, as hyponatremia is an independent predictor of worsening heart failure. The only published study to date on the influence of hyponatremia on readmission rates in the real‐world was conducted by Scherz et al, who reported that the co‐occurrence of hyponatremia in patients with acute pulmonary embolism, discharged from 185 hospitals in Pennsylvania, was independently associated with an increased readmission rate of 19.3%.27 In the present study, hyponatremia was associated with an incremental increase ranging between 14% and 17% for hospital readmission for any cause. It was conducted on a patient population in which hyponatremia was resultant from many causes, and not all patients had a serious comorbid condition. Also, the HN and non‐HN cohorts were matched for comorbidity prevalence and disease severity, and in other studies they were not. Therefore, the results of this study importantly imply that hyponatremia, whether it is resultant from a serious disease or any other cause substantially increases the healthcare burden. The implementation of strategies to prevent hospital readmissions may play an important role in reducing the healthcare burden of hyponatremia, and future studies are warranted to evaluate this hypothesis alongside evaluation of the outpatient hyponatremia burden.

The limitations of this study include, firstly, that it is only representative of inpatient hospital costs, and excludes outpatient healthcare utilization and costs. Secondly, this study utilized the Premier Hospital Database for patient selection, and laboratory testing data for serum sodium level are not available in this database; therefore, the severity of hyponatremia could not be accurately established in the HN patient population. Thirdly, the occurrence of hyponatremia in patients with some diseases is a marker of disease severity, as is the case with congestive heart failure and cirrhosis.23, 28 Our study did not adjust for the specific disease (eg, CHF) severity, which may influence the results. Future research is needed to evaluate the impact of hyponatremia on underlying disease severity of other diseases, and how its co‐occurrence may influence healthcare resource utilization and cost in each case. Although the Premier Hospital Database contains information from a large number of hospitals across the US, it is possible that it may not be representative of the entire US population of HN patients. Additionally, billing and coding errors and missing data could potentially have occurred, although the large patient population size likely precludes a large impact on the results of this study. Finally, the frequency of use of fluid restriction in these hospital settings could not be chronicled, thus limiting the ability to assess therapies and treatment modalities in use.

Teamwork is essential to delivering safe and effective hospital care,15 yet the fluidity and geographic dispersion of team members in the hospital setting presents a significant barrier to teamwork.6 Physicians, nurses, and other hospital professionals frequently lack convenient and reliable opportunities to interact, and may struggle in efforts to discuss the care of their patients in person. Research studies show that nurses and physicians on patient care units do not communicate consistently and frequently do not agree on key aspects of their patients' plans of care.7, 8

Interdisciplinary rounds (IDR), also known as multidisciplinary rounds, provide a means to assemble hospital care team members and improve collaboration.913 Prior research on the use of IDR has demonstrated improved ratings of collaboration,11, 12 but inconsistent effects on length of stay and cost.10, 12, 13 Notably, the format, frequency, and duration of IDR in prior studies has been variable and no studies, to our knowledge, have evaluated teamwork performance during IDR. Lamb and colleagues conducted observations of cancer teams during multidisciplinary meetings.14 Trained observers used a validated observation tool to rate teamwork and found significant variation in performance by subteams. However, the study focused mainly on discussion among physician team members during meetings to plan longitudinal care for oncology patients.

We recently reported on the use of structured interdisciplinary rounds (SIDR) on 2 medical units in our hospital.15, 16 SIDR combines a structured format for communication, similar to a goals‐of‐care form,17, 18 with a forum for daily interdisciplinary meetings. Though no effect was seen on length of stay or cost, SIDR was associated with significantly higher ratings of the quality of collaboration and teamwork climate, and a reduction in the rate of adverse events.19 In March 2010, we implemented SIDR across all medical units in our hospital. We subjectively noted variation in teamwork performance during SIDR after a modification of nurse manager roles. We sought to evaluate teamwork during SIDR and to determine whether variation in performance existed and, if present, to characterize it.

METHODS

RESULTS

DISCUSSION

We found that the adapted OTAS instrument demonstrated acceptable reliability for assessing teamwork during SIDR across units, domains, and most subteams. Although teamwork scores during SIDR were generally high, we found variation in performance across units, domains, and subteams. Variation in performance is notable in light of our efforts to implement a consistent format for SIDR across units. Specifically, all units have similar timing, duration, frequency, and location of SIDR, use a structured communication tool for new patients, expect the same professions to be represented, and use coleaders to facilitate discussion. We believe teamwork within IDR likely varies across units in other hospitals, and perhaps to a larger degree, given the emphasis on purposeful design and implementation of SIDR in our hospital.

Our findings are important for several reasons. First, though commonly used in hospital settings, the effectiveness of IDR is seldom assessed. Hospitalists and other professionals may not be able to identify or characterize deficiencies in teamwork during IDR without objective assessment. The adapted OTAS instrument provides a useful tool to evaluate team performance during IDR. Second, professionals may conclude that the mere implementation of an intervention such as SIDR will improve teamwork ratings and improve patient safety. Importantly, published studies evaluating the benefits of SIDR reflected a pilot study occurring on 2 units.15, 16, 19 The current study emphasizes the need to ensure that interventions proven to be effective on a small scale are implemented consistently when put into place on a larger scale.

Despite good reliability for assessing teamwork during SIDR across units, domains, and most subteams, we found poor inter‐rater reliability for the physician subteam. The explanation for this finding is not entirely clear. We reviewed the anchors for the physician subteam behaviors and were unable to identify ambiguity in anchor definitions. An analysis of domain scores within the physician subteam did not reveal any specific pattern to explain the poor correlation.

We found that the leadership domain and coleader subteam received particularly low scores. The explanation for this finding likely relates to changes in the nurse management structure shortly before our study, which reduced attendance by nurse managers and created a need for clinical coordinators to take on a leadership role during SIDR. Although we provided simulation‐based training to unit medical directors and nurse managers prior to implementing SIDR in March 2010, clinical coordinators were not part of the initial training. Our study suggests a need to provide additional training to coleaders, including clinical coordinators, to enhance their ability to facilitate discussion in SIDR.

We found no difference in overall teamwork scores when comparing teaching service units to nonteaching hospitalist service units. Duration of SIDR was significantly longer on teaching service units, but there was no association between duration of discussion and overall team score. The difference in duration of SIDR is likely explained by less succinct discussions on the part of housestaff physicians compared to more experienced hospitalists. Importantly, the quality of input, and its impact on teamwork during SIDR, does not appear to suffer when physician discussion is less efficient.

Our study has several limitations. First, we evaluated IDR in a single, urban, academic institution, which may limit generalizability. Our version of IDR (ie, SIDR) was designed to improve teamwork and incorporate a structured communication tool with regularly held interdisciplinary meetings. Features of IDR may differ in other hospitals. Second, the high teamwork scores seen in our study may not be generalizable to hospitals which have used a less rigorous, less standardized approach to IDR. Third, SIDR did not include patients or caregivers. Research is needed to test strategies to include patients and caregivers as active team members and participants in clinical decisions during hospitalization. Finally, we used the term interdisciplinary rounds to be consistent with prior published research. The term interprofessional may be more appropriate, as it specifically describes interactions among members of different professions (eg, physicians, nurses, social workers) rather than among different disciplines within a profession (eg, cardiologists, hospitalists, surgeons).

In summary, we found that teamwork during IDR could be reliably assessed using an adapted OTAS instrument. Although scores were generally high, we found variation in performance across units and domains suggesting a need to improve consistency of teamwork performance across units, domains, and subteams. Our study fills an important gap in the literature. Although IDR is commonly used in hospitals, and research shows improvements in ratings of collaboration,11, 12 little if any research has evaluated teamwork during IDR. Beyond the mere implementation of IDR, our study suggests the need to confirm that teamwork is optimal and consistent. Furthermore, hospital leaders should consider specific training for clinicians leading discussion during IDR.

Teamwork is essential to delivering safe and effective hospital care,15 yet the fluidity and geographic dispersion of team members in the hospital setting presents a significant barrier to teamwork.6 Physicians, nurses, and other hospital professionals frequently lack convenient and reliable opportunities to interact, and may struggle in efforts to discuss the care of their patients in person. Research studies show that nurses and physicians on patient care units do not communicate consistently and frequently do not agree on key aspects of their patients' plans of care.7, 8

Interdisciplinary rounds (IDR), also known as multidisciplinary rounds, provide a means to assemble hospital care team members and improve collaboration.913 Prior research on the use of IDR has demonstrated improved ratings of collaboration,11, 12 but inconsistent effects on length of stay and cost.10, 12, 13 Notably, the format, frequency, and duration of IDR in prior studies has been variable and no studies, to our knowledge, have evaluated teamwork performance during IDR. Lamb and colleagues conducted observations of cancer teams during multidisciplinary meetings.14 Trained observers used a validated observation tool to rate teamwork and found significant variation in performance by subteams. However, the study focused mainly on discussion among physician team members during meetings to plan longitudinal care for oncology patients.

We recently reported on the use of structured interdisciplinary rounds (SIDR) on 2 medical units in our hospital.15, 16 SIDR combines a structured format for communication, similar to a goals‐of‐care form,17, 18 with a forum for daily interdisciplinary meetings. Though no effect was seen on length of stay or cost, SIDR was associated with significantly higher ratings of the quality of collaboration and teamwork climate, and a reduction in the rate of adverse events.19 In March 2010, we implemented SIDR across all medical units in our hospital. We subjectively noted variation in teamwork performance during SIDR after a modification of nurse manager roles. We sought to evaluate teamwork during SIDR and to determine whether variation in performance existed and, if present, to characterize it.

METHODS

RESULTS

DISCUSSION

We found that the adapted OTAS instrument demonstrated acceptable reliability for assessing teamwork during SIDR across units, domains, and most subteams. Although teamwork scores during SIDR were generally high, we found variation in performance across units, domains, and subteams. Variation in performance is notable in light of our efforts to implement a consistent format for SIDR across units. Specifically, all units have similar timing, duration, frequency, and location of SIDR, use a structured communication tool for new patients, expect the same professions to be represented, and use coleaders to facilitate discussion. We believe teamwork within IDR likely varies across units in other hospitals, and perhaps to a larger degree, given the emphasis on purposeful design and implementation of SIDR in our hospital.

Our findings are important for several reasons. First, though commonly used in hospital settings, the effectiveness of IDR is seldom assessed. Hospitalists and other professionals may not be able to identify or characterize deficiencies in teamwork during IDR without objective assessment. The adapted OTAS instrument provides a useful tool to evaluate team performance during IDR. Second, professionals may conclude that the mere implementation of an intervention such as SIDR will improve teamwork ratings and improve patient safety. Importantly, published studies evaluating the benefits of SIDR reflected a pilot study occurring on 2 units.15, 16, 19 The current study emphasizes the need to ensure that interventions proven to be effective on a small scale are implemented consistently when put into place on a larger scale.

Despite good reliability for assessing teamwork during SIDR across units, domains, and most subteams, we found poor inter‐rater reliability for the physician subteam. The explanation for this finding is not entirely clear. We reviewed the anchors for the physician subteam behaviors and were unable to identify ambiguity in anchor definitions. An analysis of domain scores within the physician subteam did not reveal any specific pattern to explain the poor correlation.

We found that the leadership domain and coleader subteam received particularly low scores. The explanation for this finding likely relates to changes in the nurse management structure shortly before our study, which reduced attendance by nurse managers and created a need for clinical coordinators to take on a leadership role during SIDR. Although we provided simulation‐based training to unit medical directors and nurse managers prior to implementing SIDR in March 2010, clinical coordinators were not part of the initial training. Our study suggests a need to provide additional training to coleaders, including clinical coordinators, to enhance their ability to facilitate discussion in SIDR.

We found no difference in overall teamwork scores when comparing teaching service units to nonteaching hospitalist service units. Duration of SIDR was significantly longer on teaching service units, but there was no association between duration of discussion and overall team score. The difference in duration of SIDR is likely explained by less succinct discussions on the part of housestaff physicians compared to more experienced hospitalists. Importantly, the quality of input, and its impact on teamwork during SIDR, does not appear to suffer when physician discussion is less efficient.

Our study has several limitations. First, we evaluated IDR in a single, urban, academic institution, which may limit generalizability. Our version of IDR (ie, SIDR) was designed to improve teamwork and incorporate a structured communication tool with regularly held interdisciplinary meetings. Features of IDR may differ in other hospitals. Second, the high teamwork scores seen in our study may not be generalizable to hospitals which have used a less rigorous, less standardized approach to IDR. Third, SIDR did not include patients or caregivers. Research is needed to test strategies to include patients and caregivers as active team members and participants in clinical decisions during hospitalization. Finally, we used the term interdisciplinary rounds to be consistent with prior published research. The term interprofessional may be more appropriate, as it specifically describes interactions among members of different professions (eg, physicians, nurses, social workers) rather than among different disciplines within a profession (eg, cardiologists, hospitalists, surgeons).

In summary, we found that teamwork during IDR could be reliably assessed using an adapted OTAS instrument. Although scores were generally high, we found variation in performance across units and domains suggesting a need to improve consistency of teamwork performance across units, domains, and subteams. Our study fills an important gap in the literature. Although IDR is commonly used in hospitals, and research shows improvements in ratings of collaboration,11, 12 little if any research has evaluated teamwork during IDR. Beyond the mere implementation of IDR, our study suggests the need to confirm that teamwork is optimal and consistent. Furthermore, hospital leaders should consider specific training for clinicians leading discussion during IDR.

Teamwork is essential to delivering safe and effective hospital care,15 yet the fluidity and geographic dispersion of team members in the hospital setting presents a significant barrier to teamwork.6 Physicians, nurses, and other hospital professionals frequently lack convenient and reliable opportunities to interact, and may struggle in efforts to discuss the care of their patients in person. Research studies show that nurses and physicians on patient care units do not communicate consistently and frequently do not agree on key aspects of their patients' plans of care.7, 8

Interdisciplinary rounds (IDR), also known as multidisciplinary rounds, provide a means to assemble hospital care team members and improve collaboration.913 Prior research on the use of IDR has demonstrated improved ratings of collaboration,11, 12 but inconsistent effects on length of stay and cost.10, 12, 13 Notably, the format, frequency, and duration of IDR in prior studies has been variable and no studies, to our knowledge, have evaluated teamwork performance during IDR. Lamb and colleagues conducted observations of cancer teams during multidisciplinary meetings.14 Trained observers used a validated observation tool to rate teamwork and found significant variation in performance by subteams. However, the study focused mainly on discussion among physician team members during meetings to plan longitudinal care for oncology patients.

We recently reported on the use of structured interdisciplinary rounds (SIDR) on 2 medical units in our hospital.15, 16 SIDR combines a structured format for communication, similar to a goals‐of‐care form,17, 18 with a forum for daily interdisciplinary meetings. Though no effect was seen on length of stay or cost, SIDR was associated with significantly higher ratings of the quality of collaboration and teamwork climate, and a reduction in the rate of adverse events.19 In March 2010, we implemented SIDR across all medical units in our hospital. We subjectively noted variation in teamwork performance during SIDR after a modification of nurse manager roles. We sought to evaluate teamwork during SIDR and to determine whether variation in performance existed and, if present, to characterize it.

METHODS

RESULTS

DISCUSSION

We found that the adapted OTAS instrument demonstrated acceptable reliability for assessing teamwork during SIDR across units, domains, and most subteams. Although teamwork scores during SIDR were generally high, we found variation in performance across units, domains, and subteams. Variation in performance is notable in light of our efforts to implement a consistent format for SIDR across units. Specifically, all units have similar timing, duration, frequency, and location of SIDR, use a structured communication tool for new patients, expect the same professions to be represented, and use coleaders to facilitate discussion. We believe teamwork within IDR likely varies across units in other hospitals, and perhaps to a larger degree, given the emphasis on purposeful design and implementation of SIDR in our hospital.

Our findings are important for several reasons. First, though commonly used in hospital settings, the effectiveness of IDR is seldom assessed. Hospitalists and other professionals may not be able to identify or characterize deficiencies in teamwork during IDR without objective assessment. The adapted OTAS instrument provides a useful tool to evaluate team performance during IDR. Second, professionals may conclude that the mere implementation of an intervention such as SIDR will improve teamwork ratings and improve patient safety. Importantly, published studies evaluating the benefits of SIDR reflected a pilot study occurring on 2 units.15, 16, 19 The current study emphasizes the need to ensure that interventions proven to be effective on a small scale are implemented consistently when put into place on a larger scale.

Despite good reliability for assessing teamwork during SIDR across units, domains, and most subteams, we found poor inter‐rater reliability for the physician subteam. The explanation for this finding is not entirely clear. We reviewed the anchors for the physician subteam behaviors and were unable to identify ambiguity in anchor definitions. An analysis of domain scores within the physician subteam did not reveal any specific pattern to explain the poor correlation.

We found that the leadership domain and coleader subteam received particularly low scores. The explanation for this finding likely relates to changes in the nurse management structure shortly before our study, which reduced attendance by nurse managers and created a need for clinical coordinators to take on a leadership role during SIDR. Although we provided simulation‐based training to unit medical directors and nurse managers prior to implementing SIDR in March 2010, clinical coordinators were not part of the initial training. Our study suggests a need to provide additional training to coleaders, including clinical coordinators, to enhance their ability to facilitate discussion in SIDR.

We found no difference in overall teamwork scores when comparing teaching service units to nonteaching hospitalist service units. Duration of SIDR was significantly longer on teaching service units, but there was no association between duration of discussion and overall team score. The difference in duration of SIDR is likely explained by less succinct discussions on the part of housestaff physicians compared to more experienced hospitalists. Importantly, the quality of input, and its impact on teamwork during SIDR, does not appear to suffer when physician discussion is less efficient.

Our study has several limitations. First, we evaluated IDR in a single, urban, academic institution, which may limit generalizability. Our version of IDR (ie, SIDR) was designed to improve teamwork and incorporate a structured communication tool with regularly held interdisciplinary meetings. Features of IDR may differ in other hospitals. Second, the high teamwork scores seen in our study may not be generalizable to hospitals which have used a less rigorous, less standardized approach to IDR. Third, SIDR did not include patients or caregivers. Research is needed to test strategies to include patients and caregivers as active team members and participants in clinical decisions during hospitalization. Finally, we used the term interdisciplinary rounds to be consistent with prior published research. The term interprofessional may be more appropriate, as it specifically describes interactions among members of different professions (eg, physicians, nurses, social workers) rather than among different disciplines within a profession (eg, cardiologists, hospitalists, surgeons).

In summary, we found that teamwork during IDR could be reliably assessed using an adapted OTAS instrument. Although scores were generally high, we found variation in performance across units and domains suggesting a need to improve consistency of teamwork performance across units, domains, and subteams. Our study fills an important gap in the literature. Although IDR is commonly used in hospitals, and research shows improvements in ratings of collaboration,11, 12 little if any research has evaluated teamwork during IDR. Beyond the mere implementation of IDR, our study suggests the need to confirm that teamwork is optimal and consistent. Furthermore, hospital leaders should consider specific training for clinicians leading discussion during IDR.

Anesthesiologists typically initiate an assessment in the immediate preoperative period, focused on management of the airway, physiologic parameters, and choice of anesthetic. Given the growing complexity of medical issues in the surgical patient, the preoperative assessment may need to be initiated weeks to months prior to surgery. Early evaluation allows time to implement required interventions, optimize medical conditions, adjust medications, and collaborate with the surgical team.

Most studies of Preoperative clinics are in the Anesthesiology literature.1 Anesthesia‐run Preoperative clinics have demonstrated a reduction in surgical cancellations and length of stay (LOS).2 Auerbach and colleagues found medical consultation to have inconsistent effects on quality of care in surgical patients, but consultations occurred, at the earliest, 1 day prior to surgery.3 A randomized trial, performed at the Pittsburgh Veterans Administration (VA) medical center using an outpatient Internal Medicine Preoperative clinic, demonstrated a shortening of preoperative LOS but no change in total LOS, and increased use of consultants. However, there were reduced numbers of unnecessary admissions, defined as patients who were discharged without having had surgery.4 An analysis of a population‐based administrative database found that voluntary preoperative consultations were associated with a significant, albeit small, increase in mortality. Although this study used a matched cohort, the unmatched cohort that underwent consultation was higher risk; also, selection bias was possible, as the reasons for initial consultation were unknown.5

Historically, the Preoperative clinic at VA Greater Los Angeles Healthcare System (VAGLAHS) was supervised by the Department of Anesthesiology. In July 2004, the Preoperative clinic was restructured with Hospitalist oversight. The Anesthesia staff continued to evaluate all surgical patients, but did so only on the day of surgery, and after the patient was deemed an acceptable risk by the Preoperative clinic.

We undertook this study to measure the institutional impact of the addition of a Hospitalist‐run Preoperative clinic to our standard practice. The VA is an ideal setting, given the closed system with reliable longitudinal data. The VA electronic medical record also allows for comprehensive calculations of clinical covariants and outcomes.

MATERIALS AND METHODS

RESULTS

Table 1 describes the demographics and clinical characteristics of the patients evaluated in the Preoperative clinic. Number of surgeries performed in Periods A and B were 3568 and 3337, respectively, with an average of 1.3 surgeries per patient for both periods. The most common surgical specialties were Ophthalmology, Orthopedics, Urology, and General Surgery. The average ages of patients in Periods A and B were 63.9 and 61.4 years, respectively (P < 0.0001). The patients were predominantly male. ASA classifications were similar in the 2 periods, with over 60% of patients having an ASA score of 3 or higher.

Table 2 presents the selected preoperative resource utilization. Less than 3% of patients referred to the Preoperative clinic were referred for Cardiology consultation during both time periods. However, during Period A, some patients required multiple Cardiology referrals resulting in 85 referrals in Period A and 64 referrals in Period B. In contrast, Preoperative clinic providers ordered more cardiac studies in Period B than in Period A (P = 0.012). There was a significant increase in the number of patients on perioperative beta blockers, with 26% in Period A and 33% in Period B (P < 0.0001). Although there was no significant difference in the number of same‐day surgical cancellations between the 2 periods, there was a trend towards a reduction of cancellations for medically avoidable reasons, 34 (8.5%) and 18 (4.9%) cases during Periods A and B, respectively (P = 0.065).

Table 3 describes the clinical characteristics, inpatient LOS, and inpatient mortality for the surgical inpatients assessed in the Preoperative clinic. There were 1101 patients with 1200 inpatient surgeries in Period A, and 1126 patients with 1245 inpatient surgeries in Period B. The mean ages were 63.3 and 61.4 years in Periods A and B, respectively (P = 0.0004). More than 90% of patients were male. Over 62% of patients had ASA scores of 3 or higher in both periods. Both mean and median LOS was reduced in Period B. Results from the mixed‐effects regression model indicated no age and gender effects. ASA classification was significantly associated with LOS (P < 0.0001). There were reductions in LOS from Period A to Period B across all ASA classifications, however, the levels of reduction were different among them (ie, significant interaction effect, P = 0.0005). Patients who were ASA 3 or higher had a significantly shorter LOS in Period B as compared to those in Period A (P < 0.0001).

The LOS on the Cardiothoracic services was also evaluated. No significant difference in LOS was observed between the 2 periods (average LOS of 18 days) after adjusting for the patients' age and ASA score.

Inpatient mortality was reduced in Period B, from 14 cases (1.27%) to 4 cases (0.36%) (P = 0.0158). No patients who were ASA 2 or less died. Deaths in each period were evenly split between ASA categories 3 and 4 (Table 3). Subgroup analysis on inpatient deaths showed no age effect, but a significant period effect (odds ratio [OR] = 3.13, 95% confidence interval [CI]: 1.019.73 for Periods A vs B; P = 0.0488) and ASA status effect (OR = 0.06, 95% CI: 0.020.16 for ASA severe vs life‐threatening; P < 0.0001).

DISCUSSION

The addition of a Hospitalist‐run, medical Preoperative clinic was associated with more perioperative beta blocker use, shortened LOS, and lower mortality rates for our veteran patients undergoing noncardiac surgery. Such LOS reduction was not apparent in our internal control of cardiothoracic surgery patients or in the VA National Surgical Quality Improvement Program (NSQIP), a national representative sample of a similar patient population. While median unadjusted LOS in the VA NSQIP did not change over the same time periods, surgical mortality rates decreased, but by a smaller magnitude (15%) than seen in our study. While mortality in our study was reduced, the absolute numbers are relatively small. However, a subgroup analysis accounting for age and ASA score demonstrated a reduction in mortality.

As multiple structure and process changes were made in the Preoperative program, it is not definitively known which specific factor or factors could have affected inpatient surgical care. The Preoperative clinic evaluation was a one‐time consult, but included recommendations for perioperative management, including medication adjustments and infrequent suggestions for perioperative consultation. The decision to follow such recommendations was voluntary on the part of the surgical team. Alternatively, preoperative optimization may have played a role. By performing a multisystem evaluation with evidence‐based protocols, we possibly identified patients that were at increased risk of perioperative harm, and were able to intervene or recommend deferral of the procedure. This could have resulted in better surgical candidate selection with fewer postoperative complications, especially among patients with significant medical comorbidities.

Better patient selection is also suggested by a trend toward fewer same‐day cancellations for medically avoidable reasons during Period B. The distinction between medical versus patient‐related causes and avoidable versus unavoidable causes may be imprecise; however, the same Anesthesia staff assigned the categories over both periods and therefore any possible inconsistencies should have averaged out.

Increased usage of perioperative beta blockers may also have contributed to reduced mortality rates. We anticipated that more patients in Period B would be placed on perioperative beta blockers, given the guidelines in place at the time. More recently, the evidence for perioperative beta blockade has been further refined,16, 17 but during study Periods A and B, it was considered best practice for wider patient populations.

Fewer repeat referrals to Cardiology clinic and more cardiac testing were ordered by the Preoperative clinic providers during Period B. Ordering cardiac studies from Preoperative clinic and referring only when guideline‐driven could have streamlined the evaluation process and prevented the need for repeat referrals. We expect the number of stress tests and Cardiology consultations to have decreased even more in recent years as the 2007 ACC/AHA guidelines further emphasize medical optimization and de‐emphasize cardiac testing and prophylactic revascularization prior to surgery.18

Our results suggest that similar healthcare systems may benefit from adding medical expertise to their preoperative clinical operations. As the LOS reduction was most noticeable in patients with higher ASA scores, the largest impact would likely be with healthcare environments with medically complex patients and variable access to primary care. The shortage of primary care physicians and the increase in chronic disease burden in the US population may cause more patients to present to a surgeon in a nonoptimized condition. Arguably, such clinics could be supervised by any discipline that is familiar with the perioperative literature, chronic disease management, and postoperative inpatient care. Other options include clinics in which Anesthesiologists jointly collaborate with Hospitalists19 or General Internists with expertise in perioperative management.

Our study has many limitations. The VA has a largely male population and an electronic medical record, and thus results are not generalizable. Patients were younger in Period B than in Period A; however, the 2‐ to 3‐year difference might not be clinically significant, and the standard deviation was wide in both groups. This study is a retrospective observational study, and thus we cannot identify the specific processes that could have lead to any associated outcomes. There was no ideal contemporaneous control group, but examination of trends in cardiothoracic surgery at our institution and the national VA database does not reveal changes of this magnitude. Unforeseen biases could have resulted in upcoding of ASA scores by the mid‐level providers. Beta blocker usage was determined by patients prescribed beta blockers perioperatively, and did not exclude those on the medication prior to presentation. However, the significant increase in usage in Period B points to an increase in prescriptions originating from the Preoperative clinic. We do not have a breakdown of postoperative days in the intensive care unit (ICU) or ward settings, or the readmission rates. Thus, a true cost‐effectiveness analysis cannot be done. However, the reduction in postoperative LOS and decline in same‐day cancellations suggests that our institution benefited to some degree. Since the mid‐level providers were present prior to the change from Anesthesia to Hospitalist leadership, the only cost of the intervention was the hiring of a Hospitalist. However, the change freed an Anesthesiologist to work in the operating room or procedure suite. We do not have precise data regarding the number of surgeries delayed or canceled by the Preoperative clinic, but surgical workload was similar between both periods. Hopefully future studies will include richer data to minimize study limitations.

CONCLUSION

The addition of a Hospitalist‐run, medical Preoperative clinic was associated with improvements in perioperative processes and outcomes. Postoperative LOS was reduced in the sickest patients, as was inpatient mortality. Perioperative beta blocker use was increased. Adding Hospitalist expertise to preoperative clinical operations may be a viable model to improve perioperative care.

Anesthesiologists typically initiate an assessment in the immediate preoperative period, focused on management of the airway, physiologic parameters, and choice of anesthetic. Given the growing complexity of medical issues in the surgical patient, the preoperative assessment may need to be initiated weeks to months prior to surgery. Early evaluation allows time to implement required interventions, optimize medical conditions, adjust medications, and collaborate with the surgical team.

Most studies of Preoperative clinics are in the Anesthesiology literature.1 Anesthesia‐run Preoperative clinics have demonstrated a reduction in surgical cancellations and length of stay (LOS).2 Auerbach and colleagues found medical consultation to have inconsistent effects on quality of care in surgical patients, but consultations occurred, at the earliest, 1 day prior to surgery.3 A randomized trial, performed at the Pittsburgh Veterans Administration (VA) medical center using an outpatient Internal Medicine Preoperative clinic, demonstrated a shortening of preoperative LOS but no change in total LOS, and increased use of consultants. However, there were reduced numbers of unnecessary admissions, defined as patients who were discharged without having had surgery.4 An analysis of a population‐based administrative database found that voluntary preoperative consultations were associated with a significant, albeit small, increase in mortality. Although this study used a matched cohort, the unmatched cohort that underwent consultation was higher risk; also, selection bias was possible, as the reasons for initial consultation were unknown.5

Historically, the Preoperative clinic at VA Greater Los Angeles Healthcare System (VAGLAHS) was supervised by the Department of Anesthesiology. In July 2004, the Preoperative clinic was restructured with Hospitalist oversight. The Anesthesia staff continued to evaluate all surgical patients, but did so only on the day of surgery, and after the patient was deemed an acceptable risk by the Preoperative clinic.

We undertook this study to measure the institutional impact of the addition of a Hospitalist‐run Preoperative clinic to our standard practice. The VA is an ideal setting, given the closed system with reliable longitudinal data. The VA electronic medical record also allows for comprehensive calculations of clinical covariants and outcomes.

MATERIALS AND METHODS

RESULTS

Table 1 describes the demographics and clinical characteristics of the patients evaluated in the Preoperative clinic. Number of surgeries performed in Periods A and B were 3568 and 3337, respectively, with an average of 1.3 surgeries per patient for both periods. The most common surgical specialties were Ophthalmology, Orthopedics, Urology, and General Surgery. The average ages of patients in Periods A and B were 63.9 and 61.4 years, respectively (P < 0.0001). The patients were predominantly male. ASA classifications were similar in the 2 periods, with over 60% of patients having an ASA score of 3 or higher.

Table 2 presents the selected preoperative resource utilization. Less than 3% of patients referred to the Preoperative clinic were referred for Cardiology consultation during both time periods. However, during Period A, some patients required multiple Cardiology referrals resulting in 85 referrals in Period A and 64 referrals in Period B. In contrast, Preoperative clinic providers ordered more cardiac studies in Period B than in Period A (P = 0.012). There was a significant increase in the number of patients on perioperative beta blockers, with 26% in Period A and 33% in Period B (P < 0.0001). Although there was no significant difference in the number of same‐day surgical cancellations between the 2 periods, there was a trend towards a reduction of cancellations for medically avoidable reasons, 34 (8.5%) and 18 (4.9%) cases during Periods A and B, respectively (P = 0.065).

Table 3 describes the clinical characteristics, inpatient LOS, and inpatient mortality for the surgical inpatients assessed in the Preoperative clinic. There were 1101 patients with 1200 inpatient surgeries in Period A, and 1126 patients with 1245 inpatient surgeries in Period B. The mean ages were 63.3 and 61.4 years in Periods A and B, respectively (P = 0.0004). More than 90% of patients were male. Over 62% of patients had ASA scores of 3 or higher in both periods. Both mean and median LOS was reduced in Period B. Results from the mixed‐effects regression model indicated no age and gender effects. ASA classification was significantly associated with LOS (P < 0.0001). There were reductions in LOS from Period A to Period B across all ASA classifications, however, the levels of reduction were different among them (ie, significant interaction effect, P = 0.0005). Patients who were ASA 3 or higher had a significantly shorter LOS in Period B as compared to those in Period A (P < 0.0001).

The LOS on the Cardiothoracic services was also evaluated. No significant difference in LOS was observed between the 2 periods (average LOS of 18 days) after adjusting for the patients' age and ASA score.

Inpatient mortality was reduced in Period B, from 14 cases (1.27%) to 4 cases (0.36%) (P = 0.0158). No patients who were ASA 2 or less died. Deaths in each period were evenly split between ASA categories 3 and 4 (Table 3). Subgroup analysis on inpatient deaths showed no age effect, but a significant period effect (odds ratio [OR] = 3.13, 95% confidence interval [CI]: 1.019.73 for Periods A vs B; P = 0.0488) and ASA status effect (OR = 0.06, 95% CI: 0.020.16 for ASA severe vs life‐threatening; P < 0.0001).

DISCUSSION

The addition of a Hospitalist‐run, medical Preoperative clinic was associated with more perioperative beta blocker use, shortened LOS, and lower mortality rates for our veteran patients undergoing noncardiac surgery. Such LOS reduction was not apparent in our internal control of cardiothoracic surgery patients or in the VA National Surgical Quality Improvement Program (NSQIP), a national representative sample of a similar patient population. While median unadjusted LOS in the VA NSQIP did not change over the same time periods, surgical mortality rates decreased, but by a smaller magnitude (15%) than seen in our study. While mortality in our study was reduced, the absolute numbers are relatively small. However, a subgroup analysis accounting for age and ASA score demonstrated a reduction in mortality.

As multiple structure and process changes were made in the Preoperative program, it is not definitively known which specific factor or factors could have affected inpatient surgical care. The Preoperative clinic evaluation was a one‐time consult, but included recommendations for perioperative management, including medication adjustments and infrequent suggestions for perioperative consultation. The decision to follow such recommendations was voluntary on the part of the surgical team. Alternatively, preoperative optimization may have played a role. By performing a multisystem evaluation with evidence‐based protocols, we possibly identified patients that were at increased risk of perioperative harm, and were able to intervene or recommend deferral of the procedure. This could have resulted in better surgical candidate selection with fewer postoperative complications, especially among patients with significant medical comorbidities.

Better patient selection is also suggested by a trend toward fewer same‐day cancellations for medically avoidable reasons during Period B. The distinction between medical versus patient‐related causes and avoidable versus unavoidable causes may be imprecise; however, the same Anesthesia staff assigned the categories over both periods and therefore any possible inconsistencies should have averaged out.

Increased usage of perioperative beta blockers may also have contributed to reduced mortality rates. We anticipated that more patients in Period B would be placed on perioperative beta blockers, given the guidelines in place at the time. More recently, the evidence for perioperative beta blockade has been further refined,16, 17 but during study Periods A and B, it was considered best practice for wider patient populations.

Fewer repeat referrals to Cardiology clinic and more cardiac testing were ordered by the Preoperative clinic providers during Period B. Ordering cardiac studies from Preoperative clinic and referring only when guideline‐driven could have streamlined the evaluation process and prevented the need for repeat referrals. We expect the number of stress tests and Cardiology consultations to have decreased even more in recent years as the 2007 ACC/AHA guidelines further emphasize medical optimization and de‐emphasize cardiac testing and prophylactic revascularization prior to surgery.18

Our results suggest that similar healthcare systems may benefit from adding medical expertise to their preoperative clinical operations. As the LOS reduction was most noticeable in patients with higher ASA scores, the largest impact would likely be with healthcare environments with medically complex patients and variable access to primary care. The shortage of primary care physicians and the increase in chronic disease burden in the US population may cause more patients to present to a surgeon in a nonoptimized condition. Arguably, such clinics could be supervised by any discipline that is familiar with the perioperative literature, chronic disease management, and postoperative inpatient care. Other options include clinics in which Anesthesiologists jointly collaborate with Hospitalists19 or General Internists with expertise in perioperative management.

Our study has many limitations. The VA has a largely male population and an electronic medical record, and thus results are not generalizable. Patients were younger in Period B than in Period A; however, the 2‐ to 3‐year difference might not be clinically significant, and the standard deviation was wide in both groups. This study is a retrospective observational study, and thus we cannot identify the specific processes that could have lead to any associated outcomes. There was no ideal contemporaneous control group, but examination of trends in cardiothoracic surgery at our institution and the national VA database does not reveal changes of this magnitude. Unforeseen biases could have resulted in upcoding of ASA scores by the mid‐level providers. Beta blocker usage was determined by patients prescribed beta blockers perioperatively, and did not exclude those on the medication prior to presentation. However, the significant increase in usage in Period B points to an increase in prescriptions originating from the Preoperative clinic. We do not have a breakdown of postoperative days in the intensive care unit (ICU) or ward settings, or the readmission rates. Thus, a true cost‐effectiveness analysis cannot be done. However, the reduction in postoperative LOS and decline in same‐day cancellations suggests that our institution benefited to some degree. Since the mid‐level providers were present prior to the change from Anesthesia to Hospitalist leadership, the only cost of the intervention was the hiring of a Hospitalist. However, the change freed an Anesthesiologist to work in the operating room or procedure suite. We do not have precise data regarding the number of surgeries delayed or canceled by the Preoperative clinic, but surgical workload was similar between both periods. Hopefully future studies will include richer data to minimize study limitations.

CONCLUSION

The addition of a Hospitalist‐run, medical Preoperative clinic was associated with improvements in perioperative processes and outcomes. Postoperative LOS was reduced in the sickest patients, as was inpatient mortality. Perioperative beta blocker use was increased. Adding Hospitalist expertise to preoperative clinical operations may be a viable model to improve perioperative care.

Anesthesiologists typically initiate an assessment in the immediate preoperative period, focused on management of the airway, physiologic parameters, and choice of anesthetic. Given the growing complexity of medical issues in the surgical patient, the preoperative assessment may need to be initiated weeks to months prior to surgery. Early evaluation allows time to implement required interventions, optimize medical conditions, adjust medications, and collaborate with the surgical team.

Most studies of Preoperative clinics are in the Anesthesiology literature.1 Anesthesia‐run Preoperative clinics have demonstrated a reduction in surgical cancellations and length of stay (LOS).2 Auerbach and colleagues found medical consultation to have inconsistent effects on quality of care in surgical patients, but consultations occurred, at the earliest, 1 day prior to surgery.3 A randomized trial, performed at the Pittsburgh Veterans Administration (VA) medical center using an outpatient Internal Medicine Preoperative clinic, demonstrated a shortening of preoperative LOS but no change in total LOS, and increased use of consultants. However, there were reduced numbers of unnecessary admissions, defined as patients who were discharged without having had surgery.4 An analysis of a population‐based administrative database found that voluntary preoperative consultations were associated with a significant, albeit small, increase in mortality. Although this study used a matched cohort, the unmatched cohort that underwent consultation was higher risk; also, selection bias was possible, as the reasons for initial consultation were unknown.5

Historically, the Preoperative clinic at VA Greater Los Angeles Healthcare System (VAGLAHS) was supervised by the Department of Anesthesiology. In July 2004, the Preoperative clinic was restructured with Hospitalist oversight. The Anesthesia staff continued to evaluate all surgical patients, but did so only on the day of surgery, and after the patient was deemed an acceptable risk by the Preoperative clinic.

We undertook this study to measure the institutional impact of the addition of a Hospitalist‐run Preoperative clinic to our standard practice. The VA is an ideal setting, given the closed system with reliable longitudinal data. The VA electronic medical record also allows for comprehensive calculations of clinical covariants and outcomes.

MATERIALS AND METHODS

RESULTS

Table 1 describes the demographics and clinical characteristics of the patients evaluated in the Preoperative clinic. Number of surgeries performed in Periods A and B were 3568 and 3337, respectively, with an average of 1.3 surgeries per patient for both periods. The most common surgical specialties were Ophthalmology, Orthopedics, Urology, and General Surgery. The average ages of patients in Periods A and B were 63.9 and 61.4 years, respectively (P < 0.0001). The patients were predominantly male. ASA classifications were similar in the 2 periods, with over 60% of patients having an ASA score of 3 or higher.

Table 2 presents the selected preoperative resource utilization. Less than 3% of patients referred to the Preoperative clinic were referred for Cardiology consultation during both time periods. However, during Period A, some patients required multiple Cardiology referrals resulting in 85 referrals in Period A and 64 referrals in Period B. In contrast, Preoperative clinic providers ordered more cardiac studies in Period B than in Period A (P = 0.012). There was a significant increase in the number of patients on perioperative beta blockers, with 26% in Period A and 33% in Period B (P < 0.0001). Although there was no significant difference in the number of same‐day surgical cancellations between the 2 periods, there was a trend towards a reduction of cancellations for medically avoidable reasons, 34 (8.5%) and 18 (4.9%) cases during Periods A and B, respectively (P = 0.065).

Table 3 describes the clinical characteristics, inpatient LOS, and inpatient mortality for the surgical inpatients assessed in the Preoperative clinic. There were 1101 patients with 1200 inpatient surgeries in Period A, and 1126 patients with 1245 inpatient surgeries in Period B. The mean ages were 63.3 and 61.4 years in Periods A and B, respectively (P = 0.0004). More than 90% of patients were male. Over 62% of patients had ASA scores of 3 or higher in both periods. Both mean and median LOS was reduced in Period B. Results from the mixed‐effects regression model indicated no age and gender effects. ASA classification was significantly associated with LOS (P < 0.0001). There were reductions in LOS from Period A to Period B across all ASA classifications, however, the levels of reduction were different among them (ie, significant interaction effect, P = 0.0005). Patients who were ASA 3 or higher had a significantly shorter LOS in Period B as compared to those in Period A (P < 0.0001).

The LOS on the Cardiothoracic services was also evaluated. No significant difference in LOS was observed between the 2 periods (average LOS of 18 days) after adjusting for the patients' age and ASA score.

Inpatient mortality was reduced in Period B, from 14 cases (1.27%) to 4 cases (0.36%) (P = 0.0158). No patients who were ASA 2 or less died. Deaths in each period were evenly split between ASA categories 3 and 4 (Table 3). Subgroup analysis on inpatient deaths showed no age effect, but a significant period effect (odds ratio [OR] = 3.13, 95% confidence interval [CI]: 1.019.73 for Periods A vs B; P = 0.0488) and ASA status effect (OR = 0.06, 95% CI: 0.020.16 for ASA severe vs life‐threatening; P < 0.0001).

DISCUSSION

The addition of a Hospitalist‐run, medical Preoperative clinic was associated with more perioperative beta blocker use, shortened LOS, and lower mortality rates for our veteran patients undergoing noncardiac surgery. Such LOS reduction was not apparent in our internal control of cardiothoracic surgery patients or in the VA National Surgical Quality Improvement Program (NSQIP), a national representative sample of a similar patient population. While median unadjusted LOS in the VA NSQIP did not change over the same time periods, surgical mortality rates decreased, but by a smaller magnitude (15%) than seen in our study. While mortality in our study was reduced, the absolute numbers are relatively small. However, a subgroup analysis accounting for age and ASA score demonstrated a reduction in mortality.

As multiple structure and process changes were made in the Preoperative program, it is not definitively known which specific factor or factors could have affected inpatient surgical care. The Preoperative clinic evaluation was a one‐time consult, but included recommendations for perioperative management, including medication adjustments and infrequent suggestions for perioperative consultation. The decision to follow such recommendations was voluntary on the part of the surgical team. Alternatively, preoperative optimization may have played a role. By performing a multisystem evaluation with evidence‐based protocols, we possibly identified patients that were at increased risk of perioperative harm, and were able to intervene or recommend deferral of the procedure. This could have resulted in better surgical candidate selection with fewer postoperative complications, especially among patients with significant medical comorbidities.

Better patient selection is also suggested by a trend toward fewer same‐day cancellations for medically avoidable reasons during Period B. The distinction between medical versus patient‐related causes and avoidable versus unavoidable causes may be imprecise; however, the same Anesthesia staff assigned the categories over both periods and therefore any possible inconsistencies should have averaged out.

Increased usage of perioperative beta blockers may also have contributed to reduced mortality rates. We anticipated that more patients in Period B would be placed on perioperative beta blockers, given the guidelines in place at the time. More recently, the evidence for perioperative beta blockade has been further refined,16, 17 but during study Periods A and B, it was considered best practice for wider patient populations.

Fewer repeat referrals to Cardiology clinic and more cardiac testing were ordered by the Preoperative clinic providers during Period B. Ordering cardiac studies from Preoperative clinic and referring only when guideline‐driven could have streamlined the evaluation process and prevented the need for repeat referrals. We expect the number of stress tests and Cardiology consultations to have decreased even more in recent years as the 2007 ACC/AHA guidelines further emphasize medical optimization and de‐emphasize cardiac testing and prophylactic revascularization prior to surgery.18

Our results suggest that similar healthcare systems may benefit from adding medical expertise to their preoperative clinical operations. As the LOS reduction was most noticeable in patients with higher ASA scores, the largest impact would likely be with healthcare environments with medically complex patients and variable access to primary care. The shortage of primary care physicians and the increase in chronic disease burden in the US population may cause more patients to present to a surgeon in a nonoptimized condition. Arguably, such clinics could be supervised by any discipline that is familiar with the perioperative literature, chronic disease management, and postoperative inpatient care. Other options include clinics in which Anesthesiologists jointly collaborate with Hospitalists19 or General Internists with expertise in perioperative management.

Our study has many limitations. The VA has a largely male population and an electronic medical record, and thus results are not generalizable. Patients were younger in Period B than in Period A; however, the 2‐ to 3‐year difference might not be clinically significant, and the standard deviation was wide in both groups. This study is a retrospective observational study, and thus we cannot identify the specific processes that could have lead to any associated outcomes. There was no ideal contemporaneous control group, but examination of trends in cardiothoracic surgery at our institution and the national VA database does not reveal changes of this magnitude. Unforeseen biases could have resulted in upcoding of ASA scores by the mid‐level providers. Beta blocker usage was determined by patients prescribed beta blockers perioperatively, and did not exclude those on the medication prior to presentation. However, the significant increase in usage in Period B points to an increase in prescriptions originating from the Preoperative clinic. We do not have a breakdown of postoperative days in the intensive care unit (ICU) or ward settings, or the readmission rates. Thus, a true cost‐effectiveness analysis cannot be done. However, the reduction in postoperative LOS and decline in same‐day cancellations suggests that our institution benefited to some degree. Since the mid‐level providers were present prior to the change from Anesthesia to Hospitalist leadership, the only cost of the intervention was the hiring of a Hospitalist. However, the change freed an Anesthesiologist to work in the operating room or procedure suite. We do not have precise data regarding the number of surgeries delayed or canceled by the Preoperative clinic, but surgical workload was similar between both periods. Hopefully future studies will include richer data to minimize study limitations.

CONCLUSION

The addition of a Hospitalist‐run, medical Preoperative clinic was associated with improvements in perioperative processes and outcomes. Postoperative LOS was reduced in the sickest patients, as was inpatient mortality. Perioperative beta blocker use was increased. Adding Hospitalist expertise to preoperative clinical operations may be a viable model to improve perioperative care.

Perioperative myocardial infarction (PMI) often remains unrecognized with higher mortality in the aged.13 Perioperative ischemic symptoms are often masked by analgesia, sedation, and transient and subtle electrocardiographic (ECG) changes. Postoperative troponin measurement is not routinely done for PMI diagnosis. Hip fracture surgery is the most common non‐cardiac surgical procedure in the elderly, with limited data on clinical presentation of PMI.46 Moreover, the elderly are significantly underrepresented in clinical studies.7 We therefore examined the clinical presentation of PMI and its outcomes among elderly patients admitted for hip fracture repair.

METHODS

RESULTS

In the cohort of 1212 with hip fracture surgeries, 167 (13.8%) cases of PMI occurred in the first 7 days, of which 153 (92%) occurred within the first 48 hours. A total of 334 controls were matched with 167 cases of PMI. Table 1 summarizes the demographic characteristics of the study participants. Of the patients with PMI, 25.2% experienced symptoms of ischemia; 7% reported chest pain, and 12% reported dyspnea. Only 22.8% of patients with PMI had ECG changes consistent with ischemia. ST elevation MI was present in 7.2% patients. PMI patients had a lower mean hemoglobin compared to the patients without PMI (8.9 mg/dL vs 9.4 mg/dL, P 0.001). Median length of stay (LOS) in the hospital was higher among patients who experienced PMI (11.6 vs 7.4 days, P 0.001). Overall in‐hospital mortality was 5.6%. There were 24 deaths (14.4%) in the PMI group compared to 4 (1.2%) in‐hospital deaths in patients without PMI (P 0.001). A total of 473 (94%) patients survived to discharge. At 30‐day follow‐up, there were 29 (17.4%) deaths in the PMI group and 14 (4.2%) deaths in non‐PMI group. During the follow‐up for 1 year, there were 143 (29%) deaths: PMI 66 (39.5%) and 77 (23%) non‐PMI group (P 0.01).

Table 2 describes the risk factors associated with PMI in‐hospital, 30‐day, and 1‐year mortality. Risk factors for PMI were coronary artery disease (CAD) (odds ratio [OR], 3.5; confidence interval [CI], 2.25.6), and serum creatinine >2 mg/dL (OR, 2.4; CI, 1.34.4). Risk factors for in‐hospital mortality were age 8589 (OR, 5.3; CI, 1.617.7), age 90 (OR, 8.9; CI, 2.630.8), PMI (OR 15.1; CI, 4.648.8), male gender (OR 5.8; CI, 2.215.2), dyspnea (OR 5.4; CI, 1.816.9), and hemoglobin 8.0 gm/dL (OR, 3.5; CI, 1.29.9). PMI was a strong predictor for 30‐day mortality (hazard ratio [HR], 4.3; CI, 2.18.9). Risk factors for 1‐year mortality were: age 90 (HR, 2.0; CI, 1.43.1), male gender (HR, 2.1; CI, 1.53.0), and PMI (HR, 1.9; CI, 1.42.7). Figures 1 and 2 describe the Kaplan‐Meier survival curves for patients with and without PMI.

Figure 1

Figure 2