Ralph Gonzales, MD, MSPH

Delirium is an acute disturbance in mental status characterized by fluctuations in cognition and attention that affects more than 2.6 million hospitalized older adults in the United States annually, a rate that is expected to increase as the population ages.^1-4 Hospital-acquired delirium is associated with poor outcomes, including prolonged hospital length of stay (LOS), loss of independence, cognitive impairment, and even death.^5-10 Individuals who develop delirium do poorly after hospital discharge and are more likely to be readmitted within 30 days.¹¹ Approximately 30% to 40% of hospital-acquired delirium cases are preventable.^10,12 However, programs designed to prevent delirium and associated complications, such as increased LOS, have demonstrated variable success.^12-14 Many studies are limited by small sample sizes, lack of generalizability to different hospitalized patient populations, poor adherence, or reliance on outside funding.^12,13,15-18

Delirium prevention programs face several challenges because delirium could be caused by a variety of risk factors and precipitants.^19,20 Some risk factors that occur frequently among hospitalized patients can be mitigated, such as sensory impairment, immobility from physical restraints or urinary catheters, and polypharmacy.^20,21 Effective delirium care pathways targeting these risk factors must be multifaceted, interdisciplinary, and interprofessional. Accurate risk assessment is critical to allocate resources to high-risk patients. Delirium affects patients in all medical and surgical disciplines, and often is underdiagnosed.^19,22 Comprehensive screening is necessary to identify cases early and track outcomes, and educational efforts must reach all providers in the hospital. These challenges require a systematic, pragmatic approach to change.

The purpose of this study was to evaluate the association between a delirium care pathway and clinical outcomes for hospitalized patients. We hypothesized that this program would be associated with reduced hospital LOS, with secondary benefits to hospitalization costs, odds of 30-day readmission, and delirium rates.

Study Design

In this retrospective cohort study, we compared clinical outcomes the year before and after implementation of a delirium care pathway across seven hospital units. The study period spanned October 1, 2015, through February 28, 2019. The study was approved by the University of California, San Francisco Institutional Review Board (#13-12500).

Multicomponent Delirium Care Pathway

The delirium care pathway was developed collaboratively among geriatrics, hospital medicine, neurology, anesthesiology, surgery, and psychiatry services, with an interprofessional team of physicians, nurses, pharmacists, and physical and occupational therapists. This pathway was implemented in units consecutively, approximately every 4 months in the following order: neurosciences, medicine, cardiology, general surgery, specialty surgery, hematology-oncology, and transplant. The same implementation education protocols were performed in each unit. The pathway consisted of several components targeting delirium prevention and management (Appendix Figure 1 and Appendix Figure 2). Systematic screening for delirium was introduced as part of the multicomponent intervention. Nursing staff assessed each patient’s risk of developing delirium at admission using the AWOL score, a validated delirium prediction tool.²³ AWOL consists of: patient Age, spelling “World” backwards correctly, Orientation, and assessment of iLlness severity by the nurse. For patients who spoke a language other than English, spelling of “world” backwards was translated to his or her primary language, or if this was not possible, the task was modified to serial 7s (subtracting 7 from 100 in a serial fashion). This modification has been validated for use in other languages.²⁴ Patients at high risk for delirium based on an AWOL score ≥2 received a multidisciplinary intervention with four components: (1) notifying the primary team by pager and electronic medical record (EMR), (2) a nurse-led, evidence-based, nonpharmacologic multicomponent intervention,²⁵ (3) placement of a delirium order set by the physician, and (4) review of medications by the unit pharmacist who adjusted administration timing to occur during waking hours and placed a note in the EMR notifying the primary team of potentially deliriogenic medications. The delirium order set reinforced the nonpharmacologic multicomponent intervention through a nursing order, placed an automatic consult to occupational therapy, and included options to order physical therapy, order speech/language therapy, obtain vital signs three times daily with minimal night interruptions, remove an indwelling bladder catheter, and prescribe melatonin as a sleep aid.

The bedside nurse screened all patients for active delirium every 12-hour shift using the Nursing Delirium Screening Scale (NuDESC) and entered the results into the EMR.^23,26 Capturing NuDESC results in the EMR allowed communication across medical providers as well as monitoring of screening adherence. Each nurse received two in-person trainings in staff meetings and one-to-one instruction during the first week of implementation. All nurses were required to complete a 15-minute training module and had the option of completing an additional 1-hour continuing medical education module. If a patient was transferred to the intensive care unit (ICU), delirium was identified through use of the ICU-specific Confusion Assessment Method (CAM-ICU) assessments, which the bedside nurse performed each shift throughout the intervention period.²⁷ Nurses were instructed to call the primary team physician after every positive screen. Before each unit’s implementation start date, physicians with patients on that unit received education through a combination of grand rounds, resident lectures and seminars, and a pocket card on delirium evaluation and management.

Participants and Eligibility Criteria

We included all patients aged ≥50 years hospitalized for >1 day on each hospital unit (Figure). We included adults aged ≥50 years to maximize the number of participants for this study while also capturing a population at risk for delirium. Because the delirium care pathway was unit-based and the pathway was rolled out sequentially across units, only patients who were admitted to and discharged from the same unit were included to better isolate the effect of the pathway. Patients who were transferred to the ICU were only included if they were discharged from the original unit of admission. Only the first hospitalization was included for patients with multiple hospitalizations during the study period.

Patient Characteristics

Patient demographics and clinical data were collected after discharge through Clarity and Vizient electronic databases (Table 1 and Table 2). All Elixhauser comorbidities were included except for the following International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10) codes that overlapped with a delirium diagnosis: G31.2, G93.89, G93.9, G94, R41.0, and R41.82 (Appendix Table 1). Severity of illness was obtained from Vizient, which calculates illness severity based on clinical and claims data (Appendix Table 1).

Delirium Metrics

Delirium screening was introduced as part of the multicomponent intervention, and therefore delirium rates before the intervention could not be determined. Trends in delirium prevalence and incidence after the intervention are reported. Prevalent delirium was defined as a single score of ≥2 on the nurse-administered NuDESC or a positive CAM-ICU at any point during the hospital stay. Incident delirium was identified if the first NuDESC score was negative and any subsequent NuDESC or CAM-ICU score was positive.

Outcomes

The primary study outcome was hospital LOS across all participants. Secondary outcomes included total direct cost and odds of 30-day hospital readmission. Readmissions tracked as part of hospital quality reporting were obtained from Vizient and were not captured if they occurred at another hospital. We also examined rates of safety attendant and restraint use during the study period, defined as the number of safety attendant days or restraint days per 1,000 patient days.

Because previous studies have demonstrated the effectiveness of multicomponent delirium interventions among elderly general medical patients,¹² we also investigated these same outcomes in the medicine unit alone.

Statistical Analysis

The date of intervention implementation was determined for each hospital unit, which was defined as time(0) [t(0)]. The 12-month postintervention period was divided into four 3-month epochs to assess for trends. Data were aggregated across the seven units using t(0) as the start date, agnostic to the calendar month. Demographic and clinical characteristics were collected for the 12-months before t(0) and the four 3-month epochs after t(0). Univariate analysis of outcome variables comparing trends across the same epochs were conducted in the same manner, except for the rate of delirium, which was measured after t(0) and therefore could not be compared with the preintervention period.

Multivariable models were adjusted for age, sex, race/ethnicity, admission category, Elixhauser comorbidities, severity of illness quartile, and number days spent in the ICU. Admission category referred to whether the admission was emergent, urgent, or elective/unknown. Because it took 3 months after t(0) for each unit to reach a delirium screening compliance rate of 90%, the intervention was only considered fully implemented after this period. A ramp-up variable was set to 0 for admissions occurring prior to the intervention to t(0), 1/3 for admissions occurring 1 month post intervention, 2/3 for 2 months post intervention, and 1 for admissions occurring 3 to 12 months post intervention. In this way, the coefficient for the ramp-up variable estimated the postintervention versus preintervention effect. Numerical outcomes (LOS, cost) were log transformed to reduce skewness and analyzed using linear models. Coefficients were back-transformed to provide interpretations as proportional change in the median outcomes.

For LOS and readmission, we assessed secular trends by including admission date and admission date squared, in case the trend was nonlinear, as possible predictors; admission date was the specific date—not time from t(0)—to account for secular trends and allow contemporaneous controls in the analysis. To be conservative, we retained secular terms (first considering the quadratic and then the linear) if P <.10. The categorical outcome (30-day readmission) was analyzed using a logistic model. Count variables (delirium, safety attendants, restraints) were analyzed using Poisson regression models with a log link, and coefficients were back-transformed to provide rate ratio interpretations. Because delirium was not measured before t(0), and because the intervention was considered to take 3 months to become fully effective, baseline delirium rates were defined as those in the first 3 months adjusted by the ramp-up variable. For each outcome we included hospital unit, a ramp-up variable (measuring the pre- vs postintervention effect), and their interaction. If there was no statistically significant interaction, we presented the outcome for all units combined. If the interaction was statistically significant, we looked for consistency across units and reported results for all units combined when consistent, along with site-specific results. If the results were not consistent across the units, we provided site-specific results only. All statistical analyses were performed using SAS software, version 9.4 (SAS Institute Inc).

Participant Demographics and Clinical Characteristics

A total of 22,708 individuals were included in this study, with 11,018 in the preintervention period (Table 1 and Table 2). Most patients were cared for on the general surgery unit (n = 5,899), followed by the medicine unit (n = 4,923). The smallest number of patients were cared for on the hematology-oncology unit (n = 1,709). Across the five epochs, patients were of similar age and sex, and spent a similar number of days in the ICU. The population was diverse with regard to race and ethnicity; there were minor differences in admission category. There were also minor differences in severity of illness and some comorbidities between timepoints (Appendix Table 1).

Delirium Metrics

Delirium prevalence was 13.0% during the first epoch post intervention, followed by 12.0%, 11.7%, and 13.0% in the subsequent epochs (P = .91). Incident delirium occurred in 6.1% of patients during the first epoch post intervention, followed by 5.3%, 5.3%, and 5.8% in the subsequent epochs (P = .63).

Primary Outcome

Epoch-level data for LOS before and after the intervention is shown in Appendix Table 2. The mean unadjusted LOS for all units combined did not decrease after the intervention, but in the adjusted model, the mean LOS decreased by 2% after the intervention (P = .0087; Table 3).

Secondary Outcomes

The odds of 30-day readmission decreased by 14% (P = .0002) in the adjusted models for all units combined (Table 3). There was no statistically significant reduction in adjusted total direct hospitalization cost or rate of restraint use. The safety attendant results showed strong effect modification across sites; the site-specific estimates are provided in Appendix Table 3. However, the estimated values all showed reductions, and a number were large and statistically significant.

Medicine Unit Outcomes

On the medicine unit alone, we observed a statistically significant reduction in LOS of 9% after implementation of the delirium care pathway (P = .028) in the adjusted model (Table 3). There was an associated 7% proportional decrease in total direct cost (P = .0002). Reductions in 30-day readmission and safety attendant use did not remain statistically significant in the adjusted models.

Implementation of a hospital-wide multicomponent delirium care pathway was associated with reduced hospital LOS and 30-day hospital readmission in a study of 22,708 hospitalized adults at a tertiary care, university hospital in Northern California, encompassing both medical and surgical acute care patients. When evaluating general medicine patients alone, pathway implementation was associated with reductions in LOS and total direct cost. The cost savings of 7% among medical patients translates to median savings of $1,237 per hospitalization. This study—one of the largest to date examining implementation of a hospital-wide delirium care pathway—supports use of a multicomponent delirium care pathway for older adults hospitalized for a range of conditions.

Multicomponent pathways for delirium prevention and management are increasingly being used in hospital settings. The United Kingdom National Institute for Health and Care Excellence guidelines recommend delirium assessment and intervention by a multidisciplinary team within 24 hours of hospital admission for those at risk.²⁵ These guidelines are based on evidence accumulated in clinical studies over the past 30 years suggesting that multicomponent interventions reduce incident delirium by 30% to 40% among medical and surgical patients.^12,13,25,28

Although multicomponent delirium care pathways are associated with improved patient outcomes, the specific clinical benefits might vary across patient populations. Here, we found larger reductions in LOS and total direct cost among medicine patients. Medical patients might respond more robustly to nonpharmacologic multicomponent delirium interventions because of differing delirium etiologies (eg, constipation and sleep deprivation in a medical patient vs seizures or encephalitis in a neurosciences patient). Another explanation for the difference observed in total direct cost might be the inclusion of surgical units in the total study population. For example, not all hospital days are equivalent in cost for patients on a surgical unit.²⁹ For patients requiring surgical care, most of the hospitalization cost might be incurred during the initial days of hospitalization, when there are perioperative costs; therefore, reduced LOS might have a lower economic impact.²⁹ Multicomponent, nonpharmacologic delirium interventions encourage discontinuing restraints. As a result, one might expect a need for more frequent safety attendant use and an associated cost increase. However, we found that the estimated unit-specific values for safety attendant use showed reductions, which were large and highly statistically significant. For all units combined and the medicine unit alone, we found that the rate of restraint use decreased, although the change was not statistically significant. It is possible that some of the interventions taught to nurses and physicians as part of care pathway implementation, such as the use of family support for at-risk and delirious patients, led to a reduction in both safety attendants and restraints.

Our study had several strengths. This is one of the largest hospital-based delirium interventions studied, both in terms of its scope across seven diverse medical and surgical hospital units and the number of hospitalized patients studied. This intervention did not require additional staff or creating a specialized ward. Adherence to the pathway, as measured by risk assessment and delirium screening, was high (>90%) 3 months after implementation. This allowed for robust outcome ascertainment. The patient population’s characteristics and rates of delirium were stable over time. Because different hospital units incorporated the multicomponent delirium care pathway at different times, limiting enrollment to patients admitted and discharged from the same unit isolated the analysis to patients exposed to the pathway on each unit. This design also limited potential influence of other hospital quality improvement projects that might have occurred at the same time.

The primary limitation of this study is that screening for delirium was introduced as part of the multicomponent intervention. This decision was made to maximize buy-in from bedside nurses performing delirium screening because this addition to their workflow was explicitly linked to delirium prevention and management measures. Delirium could not be ascertained preintervention from the EMR because it is a clinical diagnosis and is coded inadequately.³⁰ We could only measure the change in delirium metrics after implementation of the delirium care pathway. Because baseline delirium rates before the intervention were not measured systematically, conclusions about the intervention’s association with delirium metrics are limited. All other outcomes were measured before and after the intervention.

Although the comprehensive delirium screening program and high rate of adherence are a methodologic strength of this study, a second limitation is the use of the NuDESC. Our previous research demonstrated that the NuDESC has low sensitivity but high specificity and positive predictive value,²⁶ which might underestimate delirium rates in this study. However, any underestimation should be stable over time and temporal trends should remain meaningful. This could allow more widespread study of delirium among hospitalized individuals. Because this care pathway was hospital-wide, it was important to ensure both consistency of screening and longevity of the initiative, and it was necessary to select a delirium assessment tool that was efficient and validated for nursing implementation. For these reasons, the NuDESC was an appropriate choice.

It is possible that our results could be influenced by unmeasured confounders. For example, although we incorporated Elixhauser medical comorbidities and illness severity into our model, we were unable to adjust for baseline functional status or frailty. Baseline functional status and frailty were not reliably recorded in the EMR, although these are potential confounders when investigating clinical outcomes including hospital readmission.

Implementation of a systematic, hospital-wide multicomponent delirium care pathway is associated with reductions in hospital LOS and 30-day readmission. In general medicine units, the reduction in LOS and associated cost savings were robust. These results demonstrate the feasibility and effectiveness of implementing an interprofessional, multidisciplinary multicomponent delirium care pathway through medical center funding to benefit patients and the hospital system.

Acknowledgments

The authors thank the many hospital staff members, especially the nurses, pharmacists, therapists, and patient care assistants, who helped implement the multicomponent delirium care pathway. All persons who have contributed significantly to this work are listed as authors of this work.

Delirium is an acute disturbance in mental status characterized by fluctuations in cognition and attention that affects more than 2.6 million hospitalized older adults in the United States annually, a rate that is expected to increase as the population ages.^1-4 Hospital-acquired delirium is associated with poor outcomes, including prolonged hospital length of stay (LOS), loss of independence, cognitive impairment, and even death.^5-10 Individuals who develop delirium do poorly after hospital discharge and are more likely to be readmitted within 30 days.¹¹ Approximately 30% to 40% of hospital-acquired delirium cases are preventable.^10,12 However, programs designed to prevent delirium and associated complications, such as increased LOS, have demonstrated variable success.^12-14 Many studies are limited by small sample sizes, lack of generalizability to different hospitalized patient populations, poor adherence, or reliance on outside funding.^12,13,15-18

Delirium prevention programs face several challenges because delirium could be caused by a variety of risk factors and precipitants.^19,20 Some risk factors that occur frequently among hospitalized patients can be mitigated, such as sensory impairment, immobility from physical restraints or urinary catheters, and polypharmacy.^20,21 Effective delirium care pathways targeting these risk factors must be multifaceted, interdisciplinary, and interprofessional. Accurate risk assessment is critical to allocate resources to high-risk patients. Delirium affects patients in all medical and surgical disciplines, and often is underdiagnosed.^19,22 Comprehensive screening is necessary to identify cases early and track outcomes, and educational efforts must reach all providers in the hospital. These challenges require a systematic, pragmatic approach to change.

The purpose of this study was to evaluate the association between a delirium care pathway and clinical outcomes for hospitalized patients. We hypothesized that this program would be associated with reduced hospital LOS, with secondary benefits to hospitalization costs, odds of 30-day readmission, and delirium rates.

Study Design

In this retrospective cohort study, we compared clinical outcomes the year before and after implementation of a delirium care pathway across seven hospital units. The study period spanned October 1, 2015, through February 28, 2019. The study was approved by the University of California, San Francisco Institutional Review Board (#13-12500).

Multicomponent Delirium Care Pathway

The delirium care pathway was developed collaboratively among geriatrics, hospital medicine, neurology, anesthesiology, surgery, and psychiatry services, with an interprofessional team of physicians, nurses, pharmacists, and physical and occupational therapists. This pathway was implemented in units consecutively, approximately every 4 months in the following order: neurosciences, medicine, cardiology, general surgery, specialty surgery, hematology-oncology, and transplant. The same implementation education protocols were performed in each unit. The pathway consisted of several components targeting delirium prevention and management (Appendix Figure 1 and Appendix Figure 2). Systematic screening for delirium was introduced as part of the multicomponent intervention. Nursing staff assessed each patient’s risk of developing delirium at admission using the AWOL score, a validated delirium prediction tool.²³ AWOL consists of: patient Age, spelling “World” backwards correctly, Orientation, and assessment of iLlness severity by the nurse. For patients who spoke a language other than English, spelling of “world” backwards was translated to his or her primary language, or if this was not possible, the task was modified to serial 7s (subtracting 7 from 100 in a serial fashion). This modification has been validated for use in other languages.²⁴ Patients at high risk for delirium based on an AWOL score ≥2 received a multidisciplinary intervention with four components: (1) notifying the primary team by pager and electronic medical record (EMR), (2) a nurse-led, evidence-based, nonpharmacologic multicomponent intervention,²⁵ (3) placement of a delirium order set by the physician, and (4) review of medications by the unit pharmacist who adjusted administration timing to occur during waking hours and placed a note in the EMR notifying the primary team of potentially deliriogenic medications. The delirium order set reinforced the nonpharmacologic multicomponent intervention through a nursing order, placed an automatic consult to occupational therapy, and included options to order physical therapy, order speech/language therapy, obtain vital signs three times daily with minimal night interruptions, remove an indwelling bladder catheter, and prescribe melatonin as a sleep aid.

The bedside nurse screened all patients for active delirium every 12-hour shift using the Nursing Delirium Screening Scale (NuDESC) and entered the results into the EMR.^23,26 Capturing NuDESC results in the EMR allowed communication across medical providers as well as monitoring of screening adherence. Each nurse received two in-person trainings in staff meetings and one-to-one instruction during the first week of implementation. All nurses were required to complete a 15-minute training module and had the option of completing an additional 1-hour continuing medical education module. If a patient was transferred to the intensive care unit (ICU), delirium was identified through use of the ICU-specific Confusion Assessment Method (CAM-ICU) assessments, which the bedside nurse performed each shift throughout the intervention period.²⁷ Nurses were instructed to call the primary team physician after every positive screen. Before each unit’s implementation start date, physicians with patients on that unit received education through a combination of grand rounds, resident lectures and seminars, and a pocket card on delirium evaluation and management.

Participants and Eligibility Criteria

We included all patients aged ≥50 years hospitalized for >1 day on each hospital unit (Figure). We included adults aged ≥50 years to maximize the number of participants for this study while also capturing a population at risk for delirium. Because the delirium care pathway was unit-based and the pathway was rolled out sequentially across units, only patients who were admitted to and discharged from the same unit were included to better isolate the effect of the pathway. Patients who were transferred to the ICU were only included if they were discharged from the original unit of admission. Only the first hospitalization was included for patients with multiple hospitalizations during the study period.

Patient Characteristics

Patient demographics and clinical data were collected after discharge through Clarity and Vizient electronic databases (Table 1 and Table 2). All Elixhauser comorbidities were included except for the following International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10) codes that overlapped with a delirium diagnosis: G31.2, G93.89, G93.9, G94, R41.0, and R41.82 (Appendix Table 1). Severity of illness was obtained from Vizient, which calculates illness severity based on clinical and claims data (Appendix Table 1).

Delirium Metrics

Delirium screening was introduced as part of the multicomponent intervention, and therefore delirium rates before the intervention could not be determined. Trends in delirium prevalence and incidence after the intervention are reported. Prevalent delirium was defined as a single score of ≥2 on the nurse-administered NuDESC or a positive CAM-ICU at any point during the hospital stay. Incident delirium was identified if the first NuDESC score was negative and any subsequent NuDESC or CAM-ICU score was positive.

Outcomes

The primary study outcome was hospital LOS across all participants. Secondary outcomes included total direct cost and odds of 30-day hospital readmission. Readmissions tracked as part of hospital quality reporting were obtained from Vizient and were not captured if they occurred at another hospital. We also examined rates of safety attendant and restraint use during the study period, defined as the number of safety attendant days or restraint days per 1,000 patient days.

Because previous studies have demonstrated the effectiveness of multicomponent delirium interventions among elderly general medical patients,¹² we also investigated these same outcomes in the medicine unit alone.

Statistical Analysis

The date of intervention implementation was determined for each hospital unit, which was defined as time(0) [t(0)]. The 12-month postintervention period was divided into four 3-month epochs to assess for trends. Data were aggregated across the seven units using t(0) as the start date, agnostic to the calendar month. Demographic and clinical characteristics were collected for the 12-months before t(0) and the four 3-month epochs after t(0). Univariate analysis of outcome variables comparing trends across the same epochs were conducted in the same manner, except for the rate of delirium, which was measured after t(0) and therefore could not be compared with the preintervention period.

Multivariable models were adjusted for age, sex, race/ethnicity, admission category, Elixhauser comorbidities, severity of illness quartile, and number days spent in the ICU. Admission category referred to whether the admission was emergent, urgent, or elective/unknown. Because it took 3 months after t(0) for each unit to reach a delirium screening compliance rate of 90%, the intervention was only considered fully implemented after this period. A ramp-up variable was set to 0 for admissions occurring prior to the intervention to t(0), 1/3 for admissions occurring 1 month post intervention, 2/3 for 2 months post intervention, and 1 for admissions occurring 3 to 12 months post intervention. In this way, the coefficient for the ramp-up variable estimated the postintervention versus preintervention effect. Numerical outcomes (LOS, cost) were log transformed to reduce skewness and analyzed using linear models. Coefficients were back-transformed to provide interpretations as proportional change in the median outcomes.

For LOS and readmission, we assessed secular trends by including admission date and admission date squared, in case the trend was nonlinear, as possible predictors; admission date was the specific date—not time from t(0)—to account for secular trends and allow contemporaneous controls in the analysis. To be conservative, we retained secular terms (first considering the quadratic and then the linear) if P <.10. The categorical outcome (30-day readmission) was analyzed using a logistic model. Count variables (delirium, safety attendants, restraints) were analyzed using Poisson regression models with a log link, and coefficients were back-transformed to provide rate ratio interpretations. Because delirium was not measured before t(0), and because the intervention was considered to take 3 months to become fully effective, baseline delirium rates were defined as those in the first 3 months adjusted by the ramp-up variable. For each outcome we included hospital unit, a ramp-up variable (measuring the pre- vs postintervention effect), and their interaction. If there was no statistically significant interaction, we presented the outcome for all units combined. If the interaction was statistically significant, we looked for consistency across units and reported results for all units combined when consistent, along with site-specific results. If the results were not consistent across the units, we provided site-specific results only. All statistical analyses were performed using SAS software, version 9.4 (SAS Institute Inc).

Participant Demographics and Clinical Characteristics

A total of 22,708 individuals were included in this study, with 11,018 in the preintervention period (Table 1 and Table 2). Most patients were cared for on the general surgery unit (n = 5,899), followed by the medicine unit (n = 4,923). The smallest number of patients were cared for on the hematology-oncology unit (n = 1,709). Across the five epochs, patients were of similar age and sex, and spent a similar number of days in the ICU. The population was diverse with regard to race and ethnicity; there were minor differences in admission category. There were also minor differences in severity of illness and some comorbidities between timepoints (Appendix Table 1).

Delirium Metrics

Delirium prevalence was 13.0% during the first epoch post intervention, followed by 12.0%, 11.7%, and 13.0% in the subsequent epochs (P = .91). Incident delirium occurred in 6.1% of patients during the first epoch post intervention, followed by 5.3%, 5.3%, and 5.8% in the subsequent epochs (P = .63).

Primary Outcome

Epoch-level data for LOS before and after the intervention is shown in Appendix Table 2. The mean unadjusted LOS for all units combined did not decrease after the intervention, but in the adjusted model, the mean LOS decreased by 2% after the intervention (P = .0087; Table 3).

Secondary Outcomes

The odds of 30-day readmission decreased by 14% (P = .0002) in the adjusted models for all units combined (Table 3). There was no statistically significant reduction in adjusted total direct hospitalization cost or rate of restraint use. The safety attendant results showed strong effect modification across sites; the site-specific estimates are provided in Appendix Table 3. However, the estimated values all showed reductions, and a number were large and statistically significant.

Medicine Unit Outcomes

On the medicine unit alone, we observed a statistically significant reduction in LOS of 9% after implementation of the delirium care pathway (P = .028) in the adjusted model (Table 3). There was an associated 7% proportional decrease in total direct cost (P = .0002). Reductions in 30-day readmission and safety attendant use did not remain statistically significant in the adjusted models.

Implementation of a hospital-wide multicomponent delirium care pathway was associated with reduced hospital LOS and 30-day hospital readmission in a study of 22,708 hospitalized adults at a tertiary care, university hospital in Northern California, encompassing both medical and surgical acute care patients. When evaluating general medicine patients alone, pathway implementation was associated with reductions in LOS and total direct cost. The cost savings of 7% among medical patients translates to median savings of $1,237 per hospitalization. This study—one of the largest to date examining implementation of a hospital-wide delirium care pathway—supports use of a multicomponent delirium care pathway for older adults hospitalized for a range of conditions.

Multicomponent pathways for delirium prevention and management are increasingly being used in hospital settings. The United Kingdom National Institute for Health and Care Excellence guidelines recommend delirium assessment and intervention by a multidisciplinary team within 24 hours of hospital admission for those at risk.²⁵ These guidelines are based on evidence accumulated in clinical studies over the past 30 years suggesting that multicomponent interventions reduce incident delirium by 30% to 40% among medical and surgical patients.^12,13,25,28

Although multicomponent delirium care pathways are associated with improved patient outcomes, the specific clinical benefits might vary across patient populations. Here, we found larger reductions in LOS and total direct cost among medicine patients. Medical patients might respond more robustly to nonpharmacologic multicomponent delirium interventions because of differing delirium etiologies (eg, constipation and sleep deprivation in a medical patient vs seizures or encephalitis in a neurosciences patient). Another explanation for the difference observed in total direct cost might be the inclusion of surgical units in the total study population. For example, not all hospital days are equivalent in cost for patients on a surgical unit.²⁹ For patients requiring surgical care, most of the hospitalization cost might be incurred during the initial days of hospitalization, when there are perioperative costs; therefore, reduced LOS might have a lower economic impact.²⁹ Multicomponent, nonpharmacologic delirium interventions encourage discontinuing restraints. As a result, one might expect a need for more frequent safety attendant use and an associated cost increase. However, we found that the estimated unit-specific values for safety attendant use showed reductions, which were large and highly statistically significant. For all units combined and the medicine unit alone, we found that the rate of restraint use decreased, although the change was not statistically significant. It is possible that some of the interventions taught to nurses and physicians as part of care pathway implementation, such as the use of family support for at-risk and delirious patients, led to a reduction in both safety attendants and restraints.

Our study had several strengths. This is one of the largest hospital-based delirium interventions studied, both in terms of its scope across seven diverse medical and surgical hospital units and the number of hospitalized patients studied. This intervention did not require additional staff or creating a specialized ward. Adherence to the pathway, as measured by risk assessment and delirium screening, was high (>90%) 3 months after implementation. This allowed for robust outcome ascertainment. The patient population’s characteristics and rates of delirium were stable over time. Because different hospital units incorporated the multicomponent delirium care pathway at different times, limiting enrollment to patients admitted and discharged from the same unit isolated the analysis to patients exposed to the pathway on each unit. This design also limited potential influence of other hospital quality improvement projects that might have occurred at the same time.

The primary limitation of this study is that screening for delirium was introduced as part of the multicomponent intervention. This decision was made to maximize buy-in from bedside nurses performing delirium screening because this addition to their workflow was explicitly linked to delirium prevention and management measures. Delirium could not be ascertained preintervention from the EMR because it is a clinical diagnosis and is coded inadequately.³⁰ We could only measure the change in delirium metrics after implementation of the delirium care pathway. Because baseline delirium rates before the intervention were not measured systematically, conclusions about the intervention’s association with delirium metrics are limited. All other outcomes were measured before and after the intervention.

Although the comprehensive delirium screening program and high rate of adherence are a methodologic strength of this study, a second limitation is the use of the NuDESC. Our previous research demonstrated that the NuDESC has low sensitivity but high specificity and positive predictive value,²⁶ which might underestimate delirium rates in this study. However, any underestimation should be stable over time and temporal trends should remain meaningful. This could allow more widespread study of delirium among hospitalized individuals. Because this care pathway was hospital-wide, it was important to ensure both consistency of screening and longevity of the initiative, and it was necessary to select a delirium assessment tool that was efficient and validated for nursing implementation. For these reasons, the NuDESC was an appropriate choice.

It is possible that our results could be influenced by unmeasured confounders. For example, although we incorporated Elixhauser medical comorbidities and illness severity into our model, we were unable to adjust for baseline functional status or frailty. Baseline functional status and frailty were not reliably recorded in the EMR, although these are potential confounders when investigating clinical outcomes including hospital readmission.

Implementation of a systematic, hospital-wide multicomponent delirium care pathway is associated with reductions in hospital LOS and 30-day readmission. In general medicine units, the reduction in LOS and associated cost savings were robust. These results demonstrate the feasibility and effectiveness of implementing an interprofessional, multidisciplinary multicomponent delirium care pathway through medical center funding to benefit patients and the hospital system.

Acknowledgments

The authors thank the many hospital staff members, especially the nurses, pharmacists, therapists, and patient care assistants, who helped implement the multicomponent delirium care pathway. All persons who have contributed significantly to this work are listed as authors of this work.

Delirium is an acute disturbance in mental status characterized by fluctuations in cognition and attention that affects more than 2.6 million hospitalized older adults in the United States annually, a rate that is expected to increase as the population ages.^1-4 Hospital-acquired delirium is associated with poor outcomes, including prolonged hospital length of stay (LOS), loss of independence, cognitive impairment, and even death.^5-10 Individuals who develop delirium do poorly after hospital discharge and are more likely to be readmitted within 30 days.¹¹ Approximately 30% to 40% of hospital-acquired delirium cases are preventable.^10,12 However, programs designed to prevent delirium and associated complications, such as increased LOS, have demonstrated variable success.^12-14 Many studies are limited by small sample sizes, lack of generalizability to different hospitalized patient populations, poor adherence, or reliance on outside funding.^12,13,15-18

Delirium prevention programs face several challenges because delirium could be caused by a variety of risk factors and precipitants.^19,20 Some risk factors that occur frequently among hospitalized patients can be mitigated, such as sensory impairment, immobility from physical restraints or urinary catheters, and polypharmacy.^20,21 Effective delirium care pathways targeting these risk factors must be multifaceted, interdisciplinary, and interprofessional. Accurate risk assessment is critical to allocate resources to high-risk patients. Delirium affects patients in all medical and surgical disciplines, and often is underdiagnosed.^19,22 Comprehensive screening is necessary to identify cases early and track outcomes, and educational efforts must reach all providers in the hospital. These challenges require a systematic, pragmatic approach to change.

The purpose of this study was to evaluate the association between a delirium care pathway and clinical outcomes for hospitalized patients. We hypothesized that this program would be associated with reduced hospital LOS, with secondary benefits to hospitalization costs, odds of 30-day readmission, and delirium rates.

Study Design

In this retrospective cohort study, we compared clinical outcomes the year before and after implementation of a delirium care pathway across seven hospital units. The study period spanned October 1, 2015, through February 28, 2019. The study was approved by the University of California, San Francisco Institutional Review Board (#13-12500).

Multicomponent Delirium Care Pathway

The delirium care pathway was developed collaboratively among geriatrics, hospital medicine, neurology, anesthesiology, surgery, and psychiatry services, with an interprofessional team of physicians, nurses, pharmacists, and physical and occupational therapists. This pathway was implemented in units consecutively, approximately every 4 months in the following order: neurosciences, medicine, cardiology, general surgery, specialty surgery, hematology-oncology, and transplant. The same implementation education protocols were performed in each unit. The pathway consisted of several components targeting delirium prevention and management (Appendix Figure 1 and Appendix Figure 2). Systematic screening for delirium was introduced as part of the multicomponent intervention. Nursing staff assessed each patient’s risk of developing delirium at admission using the AWOL score, a validated delirium prediction tool.²³ AWOL consists of: patient Age, spelling “World” backwards correctly, Orientation, and assessment of iLlness severity by the nurse. For patients who spoke a language other than English, spelling of “world” backwards was translated to his or her primary language, or if this was not possible, the task was modified to serial 7s (subtracting 7 from 100 in a serial fashion). This modification has been validated for use in other languages.²⁴ Patients at high risk for delirium based on an AWOL score ≥2 received a multidisciplinary intervention with four components: (1) notifying the primary team by pager and electronic medical record (EMR), (2) a nurse-led, evidence-based, nonpharmacologic multicomponent intervention,²⁵ (3) placement of a delirium order set by the physician, and (4) review of medications by the unit pharmacist who adjusted administration timing to occur during waking hours and placed a note in the EMR notifying the primary team of potentially deliriogenic medications. The delirium order set reinforced the nonpharmacologic multicomponent intervention through a nursing order, placed an automatic consult to occupational therapy, and included options to order physical therapy, order speech/language therapy, obtain vital signs three times daily with minimal night interruptions, remove an indwelling bladder catheter, and prescribe melatonin as a sleep aid.

The bedside nurse screened all patients for active delirium every 12-hour shift using the Nursing Delirium Screening Scale (NuDESC) and entered the results into the EMR.^23,26 Capturing NuDESC results in the EMR allowed communication across medical providers as well as monitoring of screening adherence. Each nurse received two in-person trainings in staff meetings and one-to-one instruction during the first week of implementation. All nurses were required to complete a 15-minute training module and had the option of completing an additional 1-hour continuing medical education module. If a patient was transferred to the intensive care unit (ICU), delirium was identified through use of the ICU-specific Confusion Assessment Method (CAM-ICU) assessments, which the bedside nurse performed each shift throughout the intervention period.²⁷ Nurses were instructed to call the primary team physician after every positive screen. Before each unit’s implementation start date, physicians with patients on that unit received education through a combination of grand rounds, resident lectures and seminars, and a pocket card on delirium evaluation and management.

Participants and Eligibility Criteria

We included all patients aged ≥50 years hospitalized for >1 day on each hospital unit (Figure). We included adults aged ≥50 years to maximize the number of participants for this study while also capturing a population at risk for delirium. Because the delirium care pathway was unit-based and the pathway was rolled out sequentially across units, only patients who were admitted to and discharged from the same unit were included to better isolate the effect of the pathway. Patients who were transferred to the ICU were only included if they were discharged from the original unit of admission. Only the first hospitalization was included for patients with multiple hospitalizations during the study period.

Patient Characteristics

Patient demographics and clinical data were collected after discharge through Clarity and Vizient electronic databases (Table 1 and Table 2). All Elixhauser comorbidities were included except for the following International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10) codes that overlapped with a delirium diagnosis: G31.2, G93.89, G93.9, G94, R41.0, and R41.82 (Appendix Table 1). Severity of illness was obtained from Vizient, which calculates illness severity based on clinical and claims data (Appendix Table 1).

Delirium Metrics

Delirium screening was introduced as part of the multicomponent intervention, and therefore delirium rates before the intervention could not be determined. Trends in delirium prevalence and incidence after the intervention are reported. Prevalent delirium was defined as a single score of ≥2 on the nurse-administered NuDESC or a positive CAM-ICU at any point during the hospital stay. Incident delirium was identified if the first NuDESC score was negative and any subsequent NuDESC or CAM-ICU score was positive.

Outcomes

The primary study outcome was hospital LOS across all participants. Secondary outcomes included total direct cost and odds of 30-day hospital readmission. Readmissions tracked as part of hospital quality reporting were obtained from Vizient and were not captured if they occurred at another hospital. We also examined rates of safety attendant and restraint use during the study period, defined as the number of safety attendant days or restraint days per 1,000 patient days.

Because previous studies have demonstrated the effectiveness of multicomponent delirium interventions among elderly general medical patients,¹² we also investigated these same outcomes in the medicine unit alone.

Statistical Analysis

The date of intervention implementation was determined for each hospital unit, which was defined as time(0) [t(0)]. The 12-month postintervention period was divided into four 3-month epochs to assess for trends. Data were aggregated across the seven units using t(0) as the start date, agnostic to the calendar month. Demographic and clinical characteristics were collected for the 12-months before t(0) and the four 3-month epochs after t(0). Univariate analysis of outcome variables comparing trends across the same epochs were conducted in the same manner, except for the rate of delirium, which was measured after t(0) and therefore could not be compared with the preintervention period.

Multivariable models were adjusted for age, sex, race/ethnicity, admission category, Elixhauser comorbidities, severity of illness quartile, and number days spent in the ICU. Admission category referred to whether the admission was emergent, urgent, or elective/unknown. Because it took 3 months after t(0) for each unit to reach a delirium screening compliance rate of 90%, the intervention was only considered fully implemented after this period. A ramp-up variable was set to 0 for admissions occurring prior to the intervention to t(0), 1/3 for admissions occurring 1 month post intervention, 2/3 for 2 months post intervention, and 1 for admissions occurring 3 to 12 months post intervention. In this way, the coefficient for the ramp-up variable estimated the postintervention versus preintervention effect. Numerical outcomes (LOS, cost) were log transformed to reduce skewness and analyzed using linear models. Coefficients were back-transformed to provide interpretations as proportional change in the median outcomes.

For LOS and readmission, we assessed secular trends by including admission date and admission date squared, in case the trend was nonlinear, as possible predictors; admission date was the specific date—not time from t(0)—to account for secular trends and allow contemporaneous controls in the analysis. To be conservative, we retained secular terms (first considering the quadratic and then the linear) if P <.10. The categorical outcome (30-day readmission) was analyzed using a logistic model. Count variables (delirium, safety attendants, restraints) were analyzed using Poisson regression models with a log link, and coefficients were back-transformed to provide rate ratio interpretations. Because delirium was not measured before t(0), and because the intervention was considered to take 3 months to become fully effective, baseline delirium rates were defined as those in the first 3 months adjusted by the ramp-up variable. For each outcome we included hospital unit, a ramp-up variable (measuring the pre- vs postintervention effect), and their interaction. If there was no statistically significant interaction, we presented the outcome for all units combined. If the interaction was statistically significant, we looked for consistency across units and reported results for all units combined when consistent, along with site-specific results. If the results were not consistent across the units, we provided site-specific results only. All statistical analyses were performed using SAS software, version 9.4 (SAS Institute Inc).

Participant Demographics and Clinical Characteristics

A total of 22,708 individuals were included in this study, with 11,018 in the preintervention period (Table 1 and Table 2). Most patients were cared for on the general surgery unit (n = 5,899), followed by the medicine unit (n = 4,923). The smallest number of patients were cared for on the hematology-oncology unit (n = 1,709). Across the five epochs, patients were of similar age and sex, and spent a similar number of days in the ICU. The population was diverse with regard to race and ethnicity; there were minor differences in admission category. There were also minor differences in severity of illness and some comorbidities between timepoints (Appendix Table 1).

Delirium Metrics

Delirium prevalence was 13.0% during the first epoch post intervention, followed by 12.0%, 11.7%, and 13.0% in the subsequent epochs (P = .91). Incident delirium occurred in 6.1% of patients during the first epoch post intervention, followed by 5.3%, 5.3%, and 5.8% in the subsequent epochs (P = .63).

Primary Outcome

Epoch-level data for LOS before and after the intervention is shown in Appendix Table 2. The mean unadjusted LOS for all units combined did not decrease after the intervention, but in the adjusted model, the mean LOS decreased by 2% after the intervention (P = .0087; Table 3).

Secondary Outcomes

The odds of 30-day readmission decreased by 14% (P = .0002) in the adjusted models for all units combined (Table 3). There was no statistically significant reduction in adjusted total direct hospitalization cost or rate of restraint use. The safety attendant results showed strong effect modification across sites; the site-specific estimates are provided in Appendix Table 3. However, the estimated values all showed reductions, and a number were large and statistically significant.

Medicine Unit Outcomes

On the medicine unit alone, we observed a statistically significant reduction in LOS of 9% after implementation of the delirium care pathway (P = .028) in the adjusted model (Table 3). There was an associated 7% proportional decrease in total direct cost (P = .0002). Reductions in 30-day readmission and safety attendant use did not remain statistically significant in the adjusted models.

Implementation of a hospital-wide multicomponent delirium care pathway was associated with reduced hospital LOS and 30-day hospital readmission in a study of 22,708 hospitalized adults at a tertiary care, university hospital in Northern California, encompassing both medical and surgical acute care patients. When evaluating general medicine patients alone, pathway implementation was associated with reductions in LOS and total direct cost. The cost savings of 7% among medical patients translates to median savings of $1,237 per hospitalization. This study—one of the largest to date examining implementation of a hospital-wide delirium care pathway—supports use of a multicomponent delirium care pathway for older adults hospitalized for a range of conditions.

Multicomponent pathways for delirium prevention and management are increasingly being used in hospital settings. The United Kingdom National Institute for Health and Care Excellence guidelines recommend delirium assessment and intervention by a multidisciplinary team within 24 hours of hospital admission for those at risk.²⁵ These guidelines are based on evidence accumulated in clinical studies over the past 30 years suggesting that multicomponent interventions reduce incident delirium by 30% to 40% among medical and surgical patients.^12,13,25,28

Although multicomponent delirium care pathways are associated with improved patient outcomes, the specific clinical benefits might vary across patient populations. Here, we found larger reductions in LOS and total direct cost among medicine patients. Medical patients might respond more robustly to nonpharmacologic multicomponent delirium interventions because of differing delirium etiologies (eg, constipation and sleep deprivation in a medical patient vs seizures or encephalitis in a neurosciences patient). Another explanation for the difference observed in total direct cost might be the inclusion of surgical units in the total study population. For example, not all hospital days are equivalent in cost for patients on a surgical unit.²⁹ For patients requiring surgical care, most of the hospitalization cost might be incurred during the initial days of hospitalization, when there are perioperative costs; therefore, reduced LOS might have a lower economic impact.²⁹ Multicomponent, nonpharmacologic delirium interventions encourage discontinuing restraints. As a result, one might expect a need for more frequent safety attendant use and an associated cost increase. However, we found that the estimated unit-specific values for safety attendant use showed reductions, which were large and highly statistically significant. For all units combined and the medicine unit alone, we found that the rate of restraint use decreased, although the change was not statistically significant. It is possible that some of the interventions taught to nurses and physicians as part of care pathway implementation, such as the use of family support for at-risk and delirious patients, led to a reduction in both safety attendants and restraints.

Our study had several strengths. This is one of the largest hospital-based delirium interventions studied, both in terms of its scope across seven diverse medical and surgical hospital units and the number of hospitalized patients studied. This intervention did not require additional staff or creating a specialized ward. Adherence to the pathway, as measured by risk assessment and delirium screening, was high (>90%) 3 months after implementation. This allowed for robust outcome ascertainment. The patient population’s characteristics and rates of delirium were stable over time. Because different hospital units incorporated the multicomponent delirium care pathway at different times, limiting enrollment to patients admitted and discharged from the same unit isolated the analysis to patients exposed to the pathway on each unit. This design also limited potential influence of other hospital quality improvement projects that might have occurred at the same time.

The primary limitation of this study is that screening for delirium was introduced as part of the multicomponent intervention. This decision was made to maximize buy-in from bedside nurses performing delirium screening because this addition to their workflow was explicitly linked to delirium prevention and management measures. Delirium could not be ascertained preintervention from the EMR because it is a clinical diagnosis and is coded inadequately.³⁰ We could only measure the change in delirium metrics after implementation of the delirium care pathway. Because baseline delirium rates before the intervention were not measured systematically, conclusions about the intervention’s association with delirium metrics are limited. All other outcomes were measured before and after the intervention.

Although the comprehensive delirium screening program and high rate of adherence are a methodologic strength of this study, a second limitation is the use of the NuDESC. Our previous research demonstrated that the NuDESC has low sensitivity but high specificity and positive predictive value,²⁶ which might underestimate delirium rates in this study. However, any underestimation should be stable over time and temporal trends should remain meaningful. This could allow more widespread study of delirium among hospitalized individuals. Because this care pathway was hospital-wide, it was important to ensure both consistency of screening and longevity of the initiative, and it was necessary to select a delirium assessment tool that was efficient and validated for nursing implementation. For these reasons, the NuDESC was an appropriate choice.

It is possible that our results could be influenced by unmeasured confounders. For example, although we incorporated Elixhauser medical comorbidities and illness severity into our model, we were unable to adjust for baseline functional status or frailty. Baseline functional status and frailty were not reliably recorded in the EMR, although these are potential confounders when investigating clinical outcomes including hospital readmission.

Implementation of a systematic, hospital-wide multicomponent delirium care pathway is associated with reductions in hospital LOS and 30-day readmission. In general medicine units, the reduction in LOS and associated cost savings were robust. These results demonstrate the feasibility and effectiveness of implementing an interprofessional, multidisciplinary multicomponent delirium care pathway through medical center funding to benefit patients and the hospital system.

Acknowledgments

The authors thank the many hospital staff members, especially the nurses, pharmacists, therapists, and patient care assistants, who helped implement the multicomponent delirium care pathway. All persons who have contributed significantly to this work are listed as authors of this work.

Medication reconciliation at hospital discharge is a critical component of the posthospital transition of care.¹ Effective reconciliation involves a clear process for documenting a current medication list, identifying and resolving discrepancies, and then documenting decisions and instructions around which medications should be continued, modified, or stopped.² Existing studies^3-5 suggest that medication discrepancies are common during hospital discharge transitions of care and lead to preventable adverse drug events, patient disability, and increased healthcare utilization following hospital discharge, including physician office visits, emergency department (ED) visits, and hospitalizations.^6-8

While the majority of studies of medication discrepancies have been conducted in general medical patients, few have examined how gaps in discharge medication reconciliation might affect surgical patients.^9,10 Two prior studies^9,10 suggest that medication discrepancies may occur more frequently for surgical patients, compared with medical patients, particularly discrepancies in reordering home medications postoperatively, raising patient safety concerns for more than 50 million patients hospitalized for surgery each year.¹¹ In particular, little is known about the appropriate discharge reconciliation of chronic cardiovascular medications, such as beta-blockers, renin-angiotensin system inhibitors, and statins in surgical patients, despite perioperative practice guidelines recommending continuation or rapid reinitiation of these medications after noncardiac surgery.¹² Problems with chronic cardiovascular medications have been implicated as major contributors to ED visits and hospitalizations for adverse drug events,^13,14 further highlighting the importance of safe and appropriate management of these medications.

To better understand the current state and impact of postoperative discharge medication reconciliation of chronic cardiovascular medications in surgical patients, we examined (1) the appropriate discharge reconciliation of 4 cardiovascular medication classes, and (2) the associations between the appropriate discharge reconciliation of these medication classes and postdischarge acute hospital and ambulatory visits in patients hospitalized for elective noncardiac surgery at an academic medical center.

Study Design and Patient Selection

We performed a retrospective analysis of data collected as part of a cohort study of hospitalized surgical patients admitted between January 2007 and December 2011. The original study was designed to assess the impact of a social marketing intervention on guideline-appropriate perioperative beta-blocker use in surgical patients. The study was conducted at 1 academic medical center that had 2 campuses with full noncardiac operative facilities and a 600-bed total capacity. Both sites had preoperative clinics, and patients were recruited by review of preoperative clinic records. Institutional review boards responsible for all sites approved the study.

For this analysis, we included adults (age >18 years) undergoing elective noncardiac surgery, who were expected to remain hospitalized for at least 1 day and were taking antiplatelet agents (aspirin, aspirin-dipyridamole, or clopidogrel), beta-blockers, renin-angiotensin system inhibitors (angiotensin-converting-enzyme inhibitors or angiotensin-receptor blockers), or statin lipid-lowering agents.

Data Collection

Data Sources. We collected data from a structured review of medical records as well as from discharge abstract information obtained from administrative data systems. Data regarding patient demographics (age, sex, and race/ethnicity), medical history, preoperative cardiovascular medications, surgical procedure and service, and attending surgeon were obtained from a medical record review of comprehensive preoperative clinic evaluations. Data regarding complications during hospitalization were obtained from medical record review and administrative data (Supplement for International Classification of Diseases, Ninth Revision codes).¹⁵ Research assistants abstracting data were trained by using a comprehensive reference manual providing specific criteria for classifying chart abstraction data. Research assistants also were directly observed during initial chart abstractions and underwent random chart validation audits by a senior investigator to ensure consistency. Any discrepancies in coding were resolved by consensus among senior investigators.

Definition of Key Predictor: Appropriate Reconciliation. We abstracted discharge medication lists from the electronic medical record. We defined the appropriate reconciliation of cardiovascular medications at discharge as documentation in discharge instructions, medication reconciliation tools, or discharge summaries that a preadmission cardiovascular medication was being continued at discharge, or, if the medication was not continued, documentation of a new contraindication to the medication or complication precluding its use during hospitalization. Medication continuity was considered appropriate if it was continued at discharge irrespective of changes in dosage. By using this measure for individual medications, we also assessed appropriate reconciliation as an “all-or-none” complete versus incomplete measure (appropriate reconciliation of all preoperative cardiovascular medication classes the patient was taking).¹⁶

Definition of Outcomes. Our coprimary outcomes were acute hospital visits (ED visits or hospitalizations) and unplanned ambulatory visits (primary care or surgical) at 30 days after surgery. Postoperative ambulatory visits that were not planned prior to surgery were defined as unplanned. Outcomes were ascertained by patient reports during follow-up telephone questionnaires administered by trained research staff and verified by medical record review.

Definition of Covariates. Using these data, we calculated a Revised Cardiac Risk Index (RCRI) score,¹⁷ which estimates the risk of perioperative cardiac complications in patients undergoing surgery. Through chart abstraction data supplemented by diagnosis codes from administrative data, we also constructed variables indicating occurrences of postoperative complications anytime during hospitalization that might pose contraindications to continuation of the 4 cardiovascular medication classes studied. For example, if a chart indicated that the patient had an acute rise in creatinine (elevation of baseline creatinine by 50% or absolute rise of 1 mg/dL in patients with baseline creatinine greater than 3 mg/dL) during hospitalization and a preoperative renin-angiotensin system inhibitor was not prescribed at discharge, we would have considered discontinuation appropriate. Other complications we abstracted were hypotension (systolic blood pressure less than 90 mmHg) for beta-blockers and renin-angiotensin system inhibitors, bradycardia (heart rate less than 50 bpm) for beta-blockers, acute kidney injury (defined above) and hyperkalemia for renin-angiotensin system inhibitors, and bleeding (any site) for antiplatelet agents.

Statistical Analysis

We used χ² and Kruskal-Wallis tests to compare baseline patient characteristics. To assess associations between appropriate medication reconciliation and patient outcomes, we used multilevel mixed-effects logistic regression to account for the clustering of patients by the attending surgeon. We adjusted for baseline patient demographics, surgical service, the number of baseline cardiovascular medications, and individual RCRI criteria. We constructed separate models for all-or-none appropriate reconciliation and for each individual medication class.

As a sensitivity analysis, we constructed similar models by using a simplified definition of appropriate reconciliation based entirely on medication continuity (continued or not continued at discharge) without taking potential contraindications during hospitalization into account. For complete versus incomplete reconciliation, we also constructed models with an interaction term between the number of baseline cardiovascular medications and appropriate medication reconciliation to test the hypothesis that inappropriate reconciliation would be more likely with an increasing number of preoperative cardiovascular medications. Because this interaction term was not statistically significant, we did not include it in the final models for ease of reporting and interpretability. We performed all statistical analyses using Stata 14 (StataCorp, LLC, College Station, Texas), and used 2-sided statistical tests and a P value of less than .05 to define statistical significance.

Patient Characteristics

A total of 849 patients were enrolled, of which 752 (88.6%) were taking at least 1 of the specified cardiovascular medications in the preoperative period. Their mean age was 61.5; 50.9% were male, 72.6% were non-Hispanic white, and 89.4% had RCRI scores of 0 or 1 (Table 1). The majority (63.8%) were undergoing general surgery, orthopedic surgery, or neurosurgery procedures. In the preoperative period, 327 (43.5%) patients were taking antiplatelet agents, 624 (83.0%) were taking beta-blockers, 361 (48.0%) were taking renin-angiotensin system inhibitors, and 406 (54.0%) were taking statins (Table 2). Among patients taking antiplatelet agents, 271 (82.9%) were taking aspirin alone, 21 (6.4%) were taking clopidogrel alone, and 35 (10.7%) were taking dual antiplatelet therapy with aspirin and clopidogrel. Nearly three-quarters of the patients (551, 73.3%) were taking medications from 2 or more classes, and the proportion of patients with inappropriate reconciliation increased with the number of preoperative cardiovascular medications.

Patients with and without appropriate reconciliation of all preoperative cardiovascular medications were similar in age, sex, and race/ethnicity (Table 1). Patients with inappropriate reconciliation of at least 1 medication were more likely to be on the urology and renal/liver transplant surgical services, have higher RCRI scores, and be taking antiplatelet agents, statins, renin-angiotensin system inhibitors, and 3 or more cardiovascular medications in the preoperative period.

Appropriate Medication Reconciliation

Four hundred thirty-six patients (58.0%) had their baseline cardiovascular medications appropriately reconciled. Among all patients with appropriately reconciled medications, 1 (0.2%) had beta-blockers discontinued due to a documented episode of hypotension; 17 (3.9%) had renin-angiotensin system inhibitors discontinued due to episodes of acute kidney injury, hypotension, or hyperkalemia; and 1 (0.2%) had antiplatelet agents discontinued due to bleeding. For individual medications, appropriate reconciliation between the preoperative and discharge periods occurred for 156 of the 327 patients on antiplatelet agents (47.7%), 507 of the 624 patients on beta-blockers (81.3%), 259 of the 361 patients on renin-angiotensin system inhibitors (71.8%), and 302 of the 406 patients on statins (74.4%; Table 2).

Associations Between Medication Reconciliation and Outcomes

Thirty-day outcome data on acute hospital visits were available for 679 (90.3%) patients. Of these, 146 (21.5%) were seen in the ED or were hospitalized, and 111 (16.3%) were seen for an unplanned primary care or surgical outpatient visit at 30 days after surgery. Patients with incomplete outcome data were more likely to have complete medication reconciliation compared with those with complete outcome data (71.2% vs 56.6%, P = 0.02). As shown in Table 3, the proportion of patients with 30-day acute hospital visits was nonstatistically significantly lower in patients with complete medication reconciliation (20.8% vs 22.4%, P = 0.63) and the appropriate reconciliation of beta-blockers (21.9% vs 23.6%, P = 0.71) and renin-angiotensin system inhibitors (19.6% vs 20.0%, P = 0.93), and nonsignificantly higher with the appropriate reconciliation of antiplatelet agents (23.9% vs 19.9%, P = 0.40). Acute hospital visits were statistically significantly lower with the appropriate reconciliation of statins (17.9% vs 31.9%, P = 0.004).

Sensitivity Analysis

Overall, 430 (57.2%) patients had complete cardiovascular medication continuity without considering potential contraindications during hospitalization. Associations between medication continuity and acute hospital and ambulatory visits were similar to the primary analyses.

In this study of 752 patients hospitalized for elective noncardiac surgery, we found significant gaps in the appropriate reconciliation of commonly prescribed cardiovascular medications, with inappropriate discontinuation ranging from 18.8% to 52.3% for individual medications. Unplanned postdischarge healthcare utilization was high, with acute hospital visits documented in 21.5% of patients and unplanned ambulatory visits in 16.3% at 30 days after surgery. However, medication reconciliation gaps were not consistently associated with ED visits, hospitalizations, or unplanned ambulatory visits.

Our finding of large gaps in postoperative medication reconciliation is consistent with existing studies of medication reconciliation in surgical patients.^9,10,18 One study found medication discrepancies in 40.2% of postoperative patients receiving usual care and discrepancies judged to have the potential to cause harm (such as the omission of beta-blockers) in 29.9%.⁹ Consistent with our findings, this study also found that most postoperative medication discrepancies were omissions in reordering home medications, though at a rate somewhat higher than those seen in medical patients at discharge.⁵ While hospitalization by itself increases the risk of unintentional discontinuation of chronic medications,³ our results, along with existing literature, suggest that the risk for omission of chronic medications is unacceptably high.

We also found significant variation in reconciliation among cardiovascular medications, with appropriate reconciliation occurring least frequently for antiplatelet agents and most frequently for beta-blockers. The low rates of appropriate reconciliation for antiplatelet agents may be attributable to deliberate withholding of antiplatelet therapy in the postoperative period based on clinical assessments of surgical bleeding risk in the absence of active bleeding. Perioperative management of antiplatelet agents for noncardiac surgery remains an unclear and controversial topic, which may also contribute to the variation noted.¹⁹ Conversely, beta-blockers demonstrated high rates of preoperative use (over 80% of patients) and appropriate reconciliation. Both findings are likely attributable in part to the timing of the study, which began prior to the publication of the Perioperative Ischemic Evaluation trial, which more definitively demonstrated the potential harms of perioperative beta-blocker therapy.²⁰

Despite a high proportion of patients with discontinuous medications at discharge, we found no associations between the appropriate reconciliation of beta-blockers, renin-angiotensin system inhibitors, and antiplatelet agents and acute hospital or ambulatory visits in the first 30 days after discharge. One explanation for this discrepancy is that, although we focused on cardiovascular medications commonly implicated in acute hospital visits, the vast majority of patients in our study had low perioperative cardiovascular risk as assessed by the RCRI. Previous studies have demonstrated that the benefit of perioperative beta-blocker therapy is predominantly in patients with moderate to high perioperative cardiovascular risk.^21,22 It is possible that the detrimental effects of the discontinuation of chronic cardiovascular medications are more prominent in populations at a higher risk of perioperative cardiovascular complications or that complications will occur later than 30 days after discharge. Similarly, while the benefits of continuation of renin-angiotensin system inhibitors are less clear,²³ few patients in our cohort had a history of congestive heart failure (6.3%) or coronary artery disease (13.0%), 2 conditions in which the impact of perioperative discontinuation of renin-angiotensin inhibitor or beta-blocker therapy would likely be more pronounced.^24,25 An additional explanation for the lack of associations is that, while multiple studies have demonstrated that medication errors are common, the proportion of errors with the potential for harm is much lower, and the proportion that causes actual harm is lower still.^5,26,27 Thus, while we likely captured high-severity medication errors leading to acute hospital or unplanned ambulatory visits, we would not have captured medication errors with lower severity clinical consequences that did not result in medical encounters.

We did find an association between the continuation of statin therapy and reduced ED visits and hospitalizations. This finding is supported by previous studies of patients undergoing noncardiac surgery, including 1 demonstrating an association between immediate postoperative statin therapy and reduced in-hospital mortality²⁸ and another study demonstrating an association between postoperative statin therapy and reductions in a composite endpoint of 30-day mortality, atrial fibrillation, and nonfatal myocardial infarction.²⁹ Alternatively, this finding could reflect the effects of unaddressed confounding by factors contributing to statin discontinuation and poor health outcomes leading to acute hospital visits, such as acute elevations in liver enzymes.

Our study has important implications for patients undergoing elective noncardiac surgery and the healthcare providers caring for them. First, inappropriate omissions of chronic cardiovascular medications at discharge are common; clinicians should increase their general awareness and focus on appropriately reconciling these medications, for even if our results do not connect medication discontinuity to readmissions or unexpected clinical encounters, their impact on patients’ understanding of their medications remains a potential concern. Second, the overall high rates of unplanned postdischarge healthcare utilization in this study highlight the need for close postdischarge monitoring of patients undergoing elective surgical procedures and for further research to identify preventable etiologies of postdischarge healthcare utilization in this population. Third, further study is needed to identify specific patient populations and medication classes, in which appropriate reconciliation is associated with patient outcomes that may benefit from more intensive discharge medication reconciliation interventions.

Our study has limitations. First, the majority of patients in this single-center study were at low risk of perioperative cardiovascular events, and our results may not be generalizable to higher-risk patients undergoing elective surgery. Second, discharge reconciliation was based on documentation of medication reconciliation and not on patient-reported medication adherence. In addition, the ability to judge the accuracy of discharge medication reconciliation is in part dependent on the accuracy of the admission medication reconciliation. Thus, although we used preoperative medication regimens documented during preadmission visits to comprehensive preoperative clinics for comparison, discrepancies in these preoperative regimens could have affected our analysis of appropriate discharge reconciliation. Third, inadequate documentation of clinical reasons for discontinuing medications may have led to residual confounding by indication in our observational study. Finally, the outcomes available to us may have been relatively insensitive to other adverse effects of medication discontinuity, such as patient symptoms (eg, angina severity), patient awareness of medications, or work placed on primary care physicians needing to “clean up” erroneous medication lists.

In conclusion, gaps in appropriate discharge reconciliation of chronic cardiovascular medications were common but not consistently associated with postdischarge acute hospital or unplanned ambulatory visits in a relatively low-risk cohort of patients undergoing elective surgery. While appropriate medication reconciliation should always be a priority, further study is needed to identify medication reconciliation approaches associated with postdischarge healthcare utilization and other patient outcomes.

Disclosure

Dr. Lee reports receiving grant support from the Health Resources and Services Administration (T32HP19025). Dr. Vittinghoff reports receiving grant support from the Agency for Healthcare Research and Quality. Dr. Auerbach and Dr. Fleischmann report receiving grant support from the National Institutes of Health. Dr. Auerbach also reports receiving honorarium as Editor-in-Chief of the Journal of Hospital Medicine. Dr. Corbett reports receiving grant and travel support from Simon Fraser University. The remaining authors have no disclosures to report.

Medication reconciliation at hospital discharge is a critical component of the posthospital transition of care.¹ Effective reconciliation involves a clear process for documenting a current medication list, identifying and resolving discrepancies, and then documenting decisions and instructions around which medications should be continued, modified, or stopped.² Existing studies^3-5 suggest that medication discrepancies are common during hospital discharge transitions of care and lead to preventable adverse drug events, patient disability, and increased healthcare utilization following hospital discharge, including physician office visits, emergency department (ED) visits, and hospitalizations.^6-8

While the majority of studies of medication discrepancies have been conducted in general medical patients, few have examined how gaps in discharge medication reconciliation might affect surgical patients.^9,10 Two prior studies^9,10 suggest that medication discrepancies may occur more frequently for surgical patients, compared with medical patients, particularly discrepancies in reordering home medications postoperatively, raising patient safety concerns for more than 50 million patients hospitalized for surgery each year.¹¹ In particular, little is known about the appropriate discharge reconciliation of chronic cardiovascular medications, such as beta-blockers, renin-angiotensin system inhibitors, and statins in surgical patients, despite perioperative practice guidelines recommending continuation or rapid reinitiation of these medications after noncardiac surgery.¹² Problems with chronic cardiovascular medications have been implicated as major contributors to ED visits and hospitalizations for adverse drug events,^13,14 further highlighting the importance of safe and appropriate management of these medications.

To better understand the current state and impact of postoperative discharge medication reconciliation of chronic cardiovascular medications in surgical patients, we examined (1) the appropriate discharge reconciliation of 4 cardiovascular medication classes, and (2) the associations between the appropriate discharge reconciliation of these medication classes and postdischarge acute hospital and ambulatory visits in patients hospitalized for elective noncardiac surgery at an academic medical center.

Study Design and Patient Selection

We performed a retrospective analysis of data collected as part of a cohort study of hospitalized surgical patients admitted between January 2007 and December 2011. The original study was designed to assess the impact of a social marketing intervention on guideline-appropriate perioperative beta-blocker use in surgical patients. The study was conducted at 1 academic medical center that had 2 campuses with full noncardiac operative facilities and a 600-bed total capacity. Both sites had preoperative clinics, and patients were recruited by review of preoperative clinic records. Institutional review boards responsible for all sites approved the study.

For this analysis, we included adults (age >18 years) undergoing elective noncardiac surgery, who were expected to remain hospitalized for at least 1 day and were taking antiplatelet agents (aspirin, aspirin-dipyridamole, or clopidogrel), beta-blockers, renin-angiotensin system inhibitors (angiotensin-converting-enzyme inhibitors or angiotensin-receptor blockers), or statin lipid-lowering agents.

Data Collection

Data Sources. We collected data from a structured review of medical records as well as from discharge abstract information obtained from administrative data systems. Data regarding patient demographics (age, sex, and race/ethnicity), medical history, preoperative cardiovascular medications, surgical procedure and service, and attending surgeon were obtained from a medical record review of comprehensive preoperative clinic evaluations. Data regarding complications during hospitalization were obtained from medical record review and administrative data (Supplement for International Classification of Diseases, Ninth Revision codes).¹⁵ Research assistants abstracting data were trained by using a comprehensive reference manual providing specific criteria for classifying chart abstraction data. Research assistants also were directly observed during initial chart abstractions and underwent random chart validation audits by a senior investigator to ensure consistency. Any discrepancies in coding were resolved by consensus among senior investigators.

Definition of Key Predictor: Appropriate Reconciliation. We abstracted discharge medication lists from the electronic medical record. We defined the appropriate reconciliation of cardiovascular medications at discharge as documentation in discharge instructions, medication reconciliation tools, or discharge summaries that a preadmission cardiovascular medication was being continued at discharge, or, if the medication was not continued, documentation of a new contraindication to the medication or complication precluding its use during hospitalization. Medication continuity was considered appropriate if it was continued at discharge irrespective of changes in dosage. By using this measure for individual medications, we also assessed appropriate reconciliation as an “all-or-none” complete versus incomplete measure (appropriate reconciliation of all preoperative cardiovascular medication classes the patient was taking).¹⁶

Definition of Outcomes. Our coprimary outcomes were acute hospital visits (ED visits or hospitalizations) and unplanned ambulatory visits (primary care or surgical) at 30 days after surgery. Postoperative ambulatory visits that were not planned prior to surgery were defined as unplanned. Outcomes were ascertained by patient reports during follow-up telephone questionnaires administered by trained research staff and verified by medical record review.

Definition of Covariates. Using these data, we calculated a Revised Cardiac Risk Index (RCRI) score,¹⁷ which estimates the risk of perioperative cardiac complications in patients undergoing surgery. Through chart abstraction data supplemented by diagnosis codes from administrative data, we also constructed variables indicating occurrences of postoperative complications anytime during hospitalization that might pose contraindications to continuation of the 4 cardiovascular medication classes studied. For example, if a chart indicated that the patient had an acute rise in creatinine (elevation of baseline creatinine by 50% or absolute rise of 1 mg/dL in patients with baseline creatinine greater than 3 mg/dL) during hospitalization and a preoperative renin-angiotensin system inhibitor was not prescribed at discharge, we would have considered discontinuation appropriate. Other complications we abstracted were hypotension (systolic blood pressure less than 90 mmHg) for beta-blockers and renin-angiotensin system inhibitors, bradycardia (heart rate less than 50 bpm) for beta-blockers, acute kidney injury (defined above) and hyperkalemia for renin-angiotensin system inhibitors, and bleeding (any site) for antiplatelet agents.

Statistical Analysis

We used χ² and Kruskal-Wallis tests to compare baseline patient characteristics. To assess associations between appropriate medication reconciliation and patient outcomes, we used multilevel mixed-effects logistic regression to account for the clustering of patients by the attending surgeon. We adjusted for baseline patient demographics, surgical service, the number of baseline cardiovascular medications, and individual RCRI criteria. We constructed separate models for all-or-none appropriate reconciliation and for each individual medication class.

As a sensitivity analysis, we constructed similar models by using a simplified definition of appropriate reconciliation based entirely on medication continuity (continued or not continued at discharge) without taking potential contraindications during hospitalization into account. For complete versus incomplete reconciliation, we also constructed models with an interaction term between the number of baseline cardiovascular medications and appropriate medication reconciliation to test the hypothesis that inappropriate reconciliation would be more likely with an increasing number of preoperative cardiovascular medications. Because this interaction term was not statistically significant, we did not include it in the final models for ease of reporting and interpretability. We performed all statistical analyses using Stata 14 (StataCorp, LLC, College Station, Texas), and used 2-sided statistical tests and a P value of less than .05 to define statistical significance.

Patient Characteristics

A total of 849 patients were enrolled, of which 752 (88.6%) were taking at least 1 of the specified cardiovascular medications in the preoperative period. Their mean age was 61.5; 50.9% were male, 72.6% were non-Hispanic white, and 89.4% had RCRI scores of 0 or 1 (Table 1). The majority (63.8%) were undergoing general surgery, orthopedic surgery, or neurosurgery procedures. In the preoperative period, 327 (43.5%) patients were taking antiplatelet agents, 624 (83.0%) were taking beta-blockers, 361 (48.0%) were taking renin-angiotensin system inhibitors, and 406 (54.0%) were taking statins (Table 2). Among patients taking antiplatelet agents, 271 (82.9%) were taking aspirin alone, 21 (6.4%) were taking clopidogrel alone, and 35 (10.7%) were taking dual antiplatelet therapy with aspirin and clopidogrel. Nearly three-quarters of the patients (551, 73.3%) were taking medications from 2 or more classes, and the proportion of patients with inappropriate reconciliation increased with the number of preoperative cardiovascular medications.

Patients with and without appropriate reconciliation of all preoperative cardiovascular medications were similar in age, sex, and race/ethnicity (Table 1). Patients with inappropriate reconciliation of at least 1 medication were more likely to be on the urology and renal/liver transplant surgical services, have higher RCRI scores, and be taking antiplatelet agents, statins, renin-angiotensin system inhibitors, and 3 or more cardiovascular medications in the preoperative period.

Appropriate Medication Reconciliation

Four hundred thirty-six patients (58.0%) had their baseline cardiovascular medications appropriately reconciled. Among all patients with appropriately reconciled medications, 1 (0.2%) had beta-blockers discontinued due to a documented episode of hypotension; 17 (3.9%) had renin-angiotensin system inhibitors discontinued due to episodes of acute kidney injury, hypotension, or hyperkalemia; and 1 (0.2%) had antiplatelet agents discontinued due to bleeding. For individual medications, appropriate reconciliation between the preoperative and discharge periods occurred for 156 of the 327 patients on antiplatelet agents (47.7%), 507 of the 624 patients on beta-blockers (81.3%), 259 of the 361 patients on renin-angiotensin system inhibitors (71.8%), and 302 of the 406 patients on statins (74.4%; Table 2).

Associations Between Medication Reconciliation and Outcomes

Thirty-day outcome data on acute hospital visits were available for 679 (90.3%) patients. Of these, 146 (21.5%) were seen in the ED or were hospitalized, and 111 (16.3%) were seen for an unplanned primary care or surgical outpatient visit at 30 days after surgery. Patients with incomplete outcome data were more likely to have complete medication reconciliation compared with those with complete outcome data (71.2% vs 56.6%, P = 0.02). As shown in Table 3, the proportion of patients with 30-day acute hospital visits was nonstatistically significantly lower in patients with complete medication reconciliation (20.8% vs 22.4%, P = 0.63) and the appropriate reconciliation of beta-blockers (21.9% vs 23.6%, P = 0.71) and renin-angiotensin system inhibitors (19.6% vs 20.0%, P = 0.93), and nonsignificantly higher with the appropriate reconciliation of antiplatelet agents (23.9% vs 19.9%, P = 0.40). Acute hospital visits were statistically significantly lower with the appropriate reconciliation of statins (17.9% vs 31.9%, P = 0.004).

Sensitivity Analysis

Overall, 430 (57.2%) patients had complete cardiovascular medication continuity without considering potential contraindications during hospitalization. Associations between medication continuity and acute hospital and ambulatory visits were similar to the primary analyses.

In this study of 752 patients hospitalized for elective noncardiac surgery, we found significant gaps in the appropriate reconciliation of commonly prescribed cardiovascular medications, with inappropriate discontinuation ranging from 18.8% to 52.3% for individual medications. Unplanned postdischarge healthcare utilization was high, with acute hospital visits documented in 21.5% of patients and unplanned ambulatory visits in 16.3% at 30 days after surgery. However, medication reconciliation gaps were not consistently associated with ED visits, hospitalizations, or unplanned ambulatory visits.

Our finding of large gaps in postoperative medication reconciliation is consistent with existing studies of medication reconciliation in surgical patients.^9,10,18 One study found medication discrepancies in 40.2% of postoperative patients receiving usual care and discrepancies judged to have the potential to cause harm (such as the omission of beta-blockers) in 29.9%.⁹ Consistent with our findings, this study also found that most postoperative medication discrepancies were omissions in reordering home medications, though at a rate somewhat higher than those seen in medical patients at discharge.⁵ While hospitalization by itself increases the risk of unintentional discontinuation of chronic medications,³ our results, along with existing literature, suggest that the risk for omission of chronic medications is unacceptably high.

We also found significant variation in reconciliation among cardiovascular medications, with appropriate reconciliation occurring least frequently for antiplatelet agents and most frequently for beta-blockers. The low rates of appropriate reconciliation for antiplatelet agents may be attributable to deliberate withholding of antiplatelet therapy in the postoperative period based on clinical assessments of surgical bleeding risk in the absence of active bleeding. Perioperative management of antiplatelet agents for noncardiac surgery remains an unclear and controversial topic, which may also contribute to the variation noted.¹⁹ Conversely, beta-blockers demonstrated high rates of preoperative use (over 80% of patients) and appropriate reconciliation. Both findings are likely attributable in part to the timing of the study, which began prior to the publication of the Perioperative Ischemic Evaluation trial, which more definitively demonstrated the potential harms of perioperative beta-blocker therapy.²⁰

Despite a high proportion of patients with discontinuous medications at discharge, we found no associations between the appropriate reconciliation of beta-blockers, renin-angiotensin system inhibitors, and antiplatelet agents and acute hospital or ambulatory visits in the first 30 days after discharge. One explanation for this discrepancy is that, although we focused on cardiovascular medications commonly implicated in acute hospital visits, the vast majority of patients in our study had low perioperative cardiovascular risk as assessed by the RCRI. Previous studies have demonstrated that the benefit of perioperative beta-blocker therapy is predominantly in patients with moderate to high perioperative cardiovascular risk.^21,22 It is possible that the detrimental effects of the discontinuation of chronic cardiovascular medications are more prominent in populations at a higher risk of perioperative cardiovascular complications or that complications will occur later than 30 days after discharge. Similarly, while the benefits of continuation of renin-angiotensin system inhibitors are less clear,²³ few patients in our cohort had a history of congestive heart failure (6.3%) or coronary artery disease (13.0%), 2 conditions in which the impact of perioperative discontinuation of renin-angiotensin inhibitor or beta-blocker therapy would likely be more pronounced.^24,25 An additional explanation for the lack of associations is that, while multiple studies have demonstrated that medication errors are common, the proportion of errors with the potential for harm is much lower, and the proportion that causes actual harm is lower still.^5,26,27 Thus, while we likely captured high-severity medication errors leading to acute hospital or unplanned ambulatory visits, we would not have captured medication errors with lower severity clinical consequences that did not result in medical encounters.

We did find an association between the continuation of statin therapy and reduced ED visits and hospitalizations. This finding is supported by previous studies of patients undergoing noncardiac surgery, including 1 demonstrating an association between immediate postoperative statin therapy and reduced in-hospital mortality²⁸ and another study demonstrating an association between postoperative statin therapy and reductions in a composite endpoint of 30-day mortality, atrial fibrillation, and nonfatal myocardial infarction.²⁹ Alternatively, this finding could reflect the effects of unaddressed confounding by factors contributing to statin discontinuation and poor health outcomes leading to acute hospital visits, such as acute elevations in liver enzymes.

Our study has important implications for patients undergoing elective noncardiac surgery and the healthcare providers caring for them. First, inappropriate omissions of chronic cardiovascular medications at discharge are common; clinicians should increase their general awareness and focus on appropriately reconciling these medications, for even if our results do not connect medication discontinuity to readmissions or unexpected clinical encounters, their impact on patients’ understanding of their medications remains a potential concern. Second, the overall high rates of unplanned postdischarge healthcare utilization in this study highlight the need for close postdischarge monitoring of patients undergoing elective surgical procedures and for further research to identify preventable etiologies of postdischarge healthcare utilization in this population. Third, further study is needed to identify specific patient populations and medication classes, in which appropriate reconciliation is associated with patient outcomes that may benefit from more intensive discharge medication reconciliation interventions.

Our study has limitations. First, the majority of patients in this single-center study were at low risk of perioperative cardiovascular events, and our results may not be generalizable to higher-risk patients undergoing elective surgery. Second, discharge reconciliation was based on documentation of medication reconciliation and not on patient-reported medication adherence. In addition, the ability to judge the accuracy of discharge medication reconciliation is in part dependent on the accuracy of the admission medication reconciliation. Thus, although we used preoperative medication regimens documented during preadmission visits to comprehensive preoperative clinics for comparison, discrepancies in these preoperative regimens could have affected our analysis of appropriate discharge reconciliation. Third, inadequate documentation of clinical reasons for discontinuing medications may have led to residual confounding by indication in our observational study. Finally, the outcomes available to us may have been relatively insensitive to other adverse effects of medication discontinuity, such as patient symptoms (eg, angina severity), patient awareness of medications, or work placed on primary care physicians needing to “clean up” erroneous medication lists.

In conclusion, gaps in appropriate discharge reconciliation of chronic cardiovascular medications were common but not consistently associated with postdischarge acute hospital or unplanned ambulatory visits in a relatively low-risk cohort of patients undergoing elective surgery. While appropriate medication reconciliation should always be a priority, further study is needed to identify medication reconciliation approaches associated with postdischarge healthcare utilization and other patient outcomes.

Disclosure

Dr. Lee reports receiving grant support from the Health Resources and Services Administration (T32HP19025). Dr. Vittinghoff reports receiving grant support from the Agency for Healthcare Research and Quality. Dr. Auerbach and Dr. Fleischmann report receiving grant support from the National Institutes of Health. Dr. Auerbach also reports receiving honorarium as Editor-in-Chief of the Journal of Hospital Medicine. Dr. Corbett reports receiving grant and travel support from Simon Fraser University. The remaining authors have no disclosures to report.

Medication reconciliation at hospital discharge is a critical component of the posthospital transition of care.¹ Effective reconciliation involves a clear process for documenting a current medication list, identifying and resolving discrepancies, and then documenting decisions and instructions around which medications should be continued, modified, or stopped.² Existing studies^3-5 suggest that medication discrepancies are common during hospital discharge transitions of care and lead to preventable adverse drug events, patient disability, and increased healthcare utilization following hospital discharge, including physician office visits, emergency department (ED) visits, and hospitalizations.^6-8

While the majority of studies of medication discrepancies have been conducted in general medical patients, few have examined how gaps in discharge medication reconciliation might affect surgical patients.^9,10 Two prior studies^9,10 suggest that medication discrepancies may occur more frequently for surgical patients, compared with medical patients, particularly discrepancies in reordering home medications postoperatively, raising patient safety concerns for more than 50 million patients hospitalized for surgery each year.¹¹ In particular, little is known about the appropriate discharge reconciliation of chronic cardiovascular medications, such as beta-blockers, renin-angiotensin system inhibitors, and statins in surgical patients, despite perioperative practice guidelines recommending continuation or rapid reinitiation of these medications after noncardiac surgery.¹² Problems with chronic cardiovascular medications have been implicated as major contributors to ED visits and hospitalizations for adverse drug events,^13,14 further highlighting the importance of safe and appropriate management of these medications.

To better understand the current state and impact of postoperative discharge medication reconciliation of chronic cardiovascular medications in surgical patients, we examined (1) the appropriate discharge reconciliation of 4 cardiovascular medication classes, and (2) the associations between the appropriate discharge reconciliation of these medication classes and postdischarge acute hospital and ambulatory visits in patients hospitalized for elective noncardiac surgery at an academic medical center.

Study Design and Patient Selection

We performed a retrospective analysis of data collected as part of a cohort study of hospitalized surgical patients admitted between January 2007 and December 2011. The original study was designed to assess the impact of a social marketing intervention on guideline-appropriate perioperative beta-blocker use in surgical patients. The study was conducted at 1 academic medical center that had 2 campuses with full noncardiac operative facilities and a 600-bed total capacity. Both sites had preoperative clinics, and patients were recruited by review of preoperative clinic records. Institutional review boards responsible for all sites approved the study.

For this analysis, we included adults (age >18 years) undergoing elective noncardiac surgery, who were expected to remain hospitalized for at least 1 day and were taking antiplatelet agents (aspirin, aspirin-dipyridamole, or clopidogrel), beta-blockers, renin-angiotensin system inhibitors (angiotensin-converting-enzyme inhibitors or angiotensin-receptor blockers), or statin lipid-lowering agents.

Data Collection

Data Sources. We collected data from a structured review of medical records as well as from discharge abstract information obtained from administrative data systems. Data regarding patient demographics (age, sex, and race/ethnicity), medical history, preoperative cardiovascular medications, surgical procedure and service, and attending surgeon were obtained from a medical record review of comprehensive preoperative clinic evaluations. Data regarding complications during hospitalization were obtained from medical record review and administrative data (Supplement for International Classification of Diseases, Ninth Revision codes).¹⁵ Research assistants abstracting data were trained by using a comprehensive reference manual providing specific criteria for classifying chart abstraction data. Research assistants also were directly observed during initial chart abstractions and underwent random chart validation audits by a senior investigator to ensure consistency. Any discrepancies in coding were resolved by consensus among senior investigators.

Definition of Key Predictor: Appropriate Reconciliation. We abstracted discharge medication lists from the electronic medical record. We defined the appropriate reconciliation of cardiovascular medications at discharge as documentation in discharge instructions, medication reconciliation tools, or discharge summaries that a preadmission cardiovascular medication was being continued at discharge, or, if the medication was not continued, documentation of a new contraindication to the medication or complication precluding its use during hospitalization. Medication continuity was considered appropriate if it was continued at discharge irrespective of changes in dosage. By using this measure for individual medications, we also assessed appropriate reconciliation as an “all-or-none” complete versus incomplete measure (appropriate reconciliation of all preoperative cardiovascular medication classes the patient was taking).¹⁶

Definition of Outcomes. Our coprimary outcomes were acute hospital visits (ED visits or hospitalizations) and unplanned ambulatory visits (primary care or surgical) at 30 days after surgery. Postoperative ambulatory visits that were not planned prior to surgery were defined as unplanned. Outcomes were ascertained by patient reports during follow-up telephone questionnaires administered by trained research staff and verified by medical record review.

Definition of Covariates. Using these data, we calculated a Revised Cardiac Risk Index (RCRI) score,¹⁷ which estimates the risk of perioperative cardiac complications in patients undergoing surgery. Through chart abstraction data supplemented by diagnosis codes from administrative data, we also constructed variables indicating occurrences of postoperative complications anytime during hospitalization that might pose contraindications to continuation of the 4 cardiovascular medication classes studied. For example, if a chart indicated that the patient had an acute rise in creatinine (elevation of baseline creatinine by 50% or absolute rise of 1 mg/dL in patients with baseline creatinine greater than 3 mg/dL) during hospitalization and a preoperative renin-angiotensin system inhibitor was not prescribed at discharge, we would have considered discontinuation appropriate. Other complications we abstracted were hypotension (systolic blood pressure less than 90 mmHg) for beta-blockers and renin-angiotensin system inhibitors, bradycardia (heart rate less than 50 bpm) for beta-blockers, acute kidney injury (defined above) and hyperkalemia for renin-angiotensin system inhibitors, and bleeding (any site) for antiplatelet agents.

Statistical Analysis

We used χ² and Kruskal-Wallis tests to compare baseline patient characteristics. To assess associations between appropriate medication reconciliation and patient outcomes, we used multilevel mixed-effects logistic regression to account for the clustering of patients by the attending surgeon. We adjusted for baseline patient demographics, surgical service, the number of baseline cardiovascular medications, and individual RCRI criteria. We constructed separate models for all-or-none appropriate reconciliation and for each individual medication class.

As a sensitivity analysis, we constructed similar models by using a simplified definition of appropriate reconciliation based entirely on medication continuity (continued or not continued at discharge) without taking potential contraindications during hospitalization into account. For complete versus incomplete reconciliation, we also constructed models with an interaction term between the number of baseline cardiovascular medications and appropriate medication reconciliation to test the hypothesis that inappropriate reconciliation would be more likely with an increasing number of preoperative cardiovascular medications. Because this interaction term was not statistically significant, we did not include it in the final models for ease of reporting and interpretability. We performed all statistical analyses using Stata 14 (StataCorp, LLC, College Station, Texas), and used 2-sided statistical tests and a P value of less than .05 to define statistical significance.

Patient Characteristics

A total of 849 patients were enrolled, of which 752 (88.6%) were taking at least 1 of the specified cardiovascular medications in the preoperative period. Their mean age was 61.5; 50.9% were male, 72.6% were non-Hispanic white, and 89.4% had RCRI scores of 0 or 1 (Table 1). The majority (63.8%) were undergoing general surgery, orthopedic surgery, or neurosurgery procedures. In the preoperative period, 327 (43.5%) patients were taking antiplatelet agents, 624 (83.0%) were taking beta-blockers, 361 (48.0%) were taking renin-angiotensin system inhibitors, and 406 (54.0%) were taking statins (Table 2). Among patients taking antiplatelet agents, 271 (82.9%) were taking aspirin alone, 21 (6.4%) were taking clopidogrel alone, and 35 (10.7%) were taking dual antiplatelet therapy with aspirin and clopidogrel. Nearly three-quarters of the patients (551, 73.3%) were taking medications from 2 or more classes, and the proportion of patients with inappropriate reconciliation increased with the number of preoperative cardiovascular medications.

Patients with and without appropriate reconciliation of all preoperative cardiovascular medications were similar in age, sex, and race/ethnicity (Table 1). Patients with inappropriate reconciliation of at least 1 medication were more likely to be on the urology and renal/liver transplant surgical services, have higher RCRI scores, and be taking antiplatelet agents, statins, renin-angiotensin system inhibitors, and 3 or more cardiovascular medications in the preoperative period.

Appropriate Medication Reconciliation

Four hundred thirty-six patients (58.0%) had their baseline cardiovascular medications appropriately reconciled. Among all patients with appropriately reconciled medications, 1 (0.2%) had beta-blockers discontinued due to a documented episode of hypotension; 17 (3.9%) had renin-angiotensin system inhibitors discontinued due to episodes of acute kidney injury, hypotension, or hyperkalemia; and 1 (0.2%) had antiplatelet agents discontinued due to bleeding. For individual medications, appropriate reconciliation between the preoperative and discharge periods occurred for 156 of the 327 patients on antiplatelet agents (47.7%), 507 of the 624 patients on beta-blockers (81.3%), 259 of the 361 patients on renin-angiotensin system inhibitors (71.8%), and 302 of the 406 patients on statins (74.4%; Table 2).

Associations Between Medication Reconciliation and Outcomes

Thirty-day outcome data on acute hospital visits were available for 679 (90.3%) patients. Of these, 146 (21.5%) were seen in the ED or were hospitalized, and 111 (16.3%) were seen for an unplanned primary care or surgical outpatient visit at 30 days after surgery. Patients with incomplete outcome data were more likely to have complete medication reconciliation compared with those with complete outcome data (71.2% vs 56.6%, P = 0.02). As shown in Table 3, the proportion of patients with 30-day acute hospital visits was nonstatistically significantly lower in patients with complete medication reconciliation (20.8% vs 22.4%, P = 0.63) and the appropriate reconciliation of beta-blockers (21.9% vs 23.6%, P = 0.71) and renin-angiotensin system inhibitors (19.6% vs 20.0%, P = 0.93), and nonsignificantly higher with the appropriate reconciliation of antiplatelet agents (23.9% vs 19.9%, P = 0.40). Acute hospital visits were statistically significantly lower with the appropriate reconciliation of statins (17.9% vs 31.9%, P = 0.004).

Sensitivity Analysis

Overall, 430 (57.2%) patients had complete cardiovascular medication continuity without considering potential contraindications during hospitalization. Associations between medication continuity and acute hospital and ambulatory visits were similar to the primary analyses.

In this study of 752 patients hospitalized for elective noncardiac surgery, we found significant gaps in the appropriate reconciliation of commonly prescribed cardiovascular medications, with inappropriate discontinuation ranging from 18.8% to 52.3% for individual medications. Unplanned postdischarge healthcare utilization was high, with acute hospital visits documented in 21.5% of patients and unplanned ambulatory visits in 16.3% at 30 days after surgery. However, medication reconciliation gaps were not consistently associated with ED visits, hospitalizations, or unplanned ambulatory visits.

Our finding of large gaps in postoperative medication reconciliation is consistent with existing studies of medication reconciliation in surgical patients.^9,10,18 One study found medication discrepancies in 40.2% of postoperative patients receiving usual care and discrepancies judged to have the potential to cause harm (such as the omission of beta-blockers) in 29.9%.⁹ Consistent with our findings, this study also found that most postoperative medication discrepancies were omissions in reordering home medications, though at a rate somewhat higher than those seen in medical patients at discharge.⁵ While hospitalization by itself increases the risk of unintentional discontinuation of chronic medications,³ our results, along with existing literature, suggest that the risk for omission of chronic medications is unacceptably high.

We also found significant variation in reconciliation among cardiovascular medications, with appropriate reconciliation occurring least frequently for antiplatelet agents and most frequently for beta-blockers. The low rates of appropriate reconciliation for antiplatelet agents may be attributable to deliberate withholding of antiplatelet therapy in the postoperative period based on clinical assessments of surgical bleeding risk in the absence of active bleeding. Perioperative management of antiplatelet agents for noncardiac surgery remains an unclear and controversial topic, which may also contribute to the variation noted.¹⁹ Conversely, beta-blockers demonstrated high rates of preoperative use (over 80% of patients) and appropriate reconciliation. Both findings are likely attributable in part to the timing of the study, which began prior to the publication of the Perioperative Ischemic Evaluation trial, which more definitively demonstrated the potential harms of perioperative beta-blocker therapy.²⁰

Despite a high proportion of patients with discontinuous medications at discharge, we found no associations between the appropriate reconciliation of beta-blockers, renin-angiotensin system inhibitors, and antiplatelet agents and acute hospital or ambulatory visits in the first 30 days after discharge. One explanation for this discrepancy is that, although we focused on cardiovascular medications commonly implicated in acute hospital visits, the vast majority of patients in our study had low perioperative cardiovascular risk as assessed by the RCRI. Previous studies have demonstrated that the benefit of perioperative beta-blocker therapy is predominantly in patients with moderate to high perioperative cardiovascular risk.^21,22 It is possible that the detrimental effects of the discontinuation of chronic cardiovascular medications are more prominent in populations at a higher risk of perioperative cardiovascular complications or that complications will occur later than 30 days after discharge. Similarly, while the benefits of continuation of renin-angiotensin system inhibitors are less clear,²³ few patients in our cohort had a history of congestive heart failure (6.3%) or coronary artery disease (13.0%), 2 conditions in which the impact of perioperative discontinuation of renin-angiotensin inhibitor or beta-blocker therapy would likely be more pronounced.^24,25 An additional explanation for the lack of associations is that, while multiple studies have demonstrated that medication errors are common, the proportion of errors with the potential for harm is much lower, and the proportion that causes actual harm is lower still.^5,26,27 Thus, while we likely captured high-severity medication errors leading to acute hospital or unplanned ambulatory visits, we would not have captured medication errors with lower severity clinical consequences that did not result in medical encounters.

We did find an association between the continuation of statin therapy and reduced ED visits and hospitalizations. This finding is supported by previous studies of patients undergoing noncardiac surgery, including 1 demonstrating an association between immediate postoperative statin therapy and reduced in-hospital mortality²⁸ and another study demonstrating an association between postoperative statin therapy and reductions in a composite endpoint of 30-day mortality, atrial fibrillation, and nonfatal myocardial infarction.²⁹ Alternatively, this finding could reflect the effects of unaddressed confounding by factors contributing to statin discontinuation and poor health outcomes leading to acute hospital visits, such as acute elevations in liver enzymes.

Our study has important implications for patients undergoing elective noncardiac surgery and the healthcare providers caring for them. First, inappropriate omissions of chronic cardiovascular medications at discharge are common; clinicians should increase their general awareness and focus on appropriately reconciling these medications, for even if our results do not connect medication discontinuity to readmissions or unexpected clinical encounters, their impact on patients’ understanding of their medications remains a potential concern. Second, the overall high rates of unplanned postdischarge healthcare utilization in this study highlight the need for close postdischarge monitoring of patients undergoing elective surgical procedures and for further research to identify preventable etiologies of postdischarge healthcare utilization in this population. Third, further study is needed to identify specific patient populations and medication classes, in which appropriate reconciliation is associated with patient outcomes that may benefit from more intensive discharge medication reconciliation interventions.

Our study has limitations. First, the majority of patients in this single-center study were at low risk of perioperative cardiovascular events, and our results may not be generalizable to higher-risk patients undergoing elective surgery. Second, discharge reconciliation was based on documentation of medication reconciliation and not on patient-reported medication adherence. In addition, the ability to judge the accuracy of discharge medication reconciliation is in part dependent on the accuracy of the admission medication reconciliation. Thus, although we used preoperative medication regimens documented during preadmission visits to comprehensive preoperative clinics for comparison, discrepancies in these preoperative regimens could have affected our analysis of appropriate discharge reconciliation. Third, inadequate documentation of clinical reasons for discontinuing medications may have led to residual confounding by indication in our observational study. Finally, the outcomes available to us may have been relatively insensitive to other adverse effects of medication discontinuity, such as patient symptoms (eg, angina severity), patient awareness of medications, or work placed on primary care physicians needing to “clean up” erroneous medication lists.

In conclusion, gaps in appropriate discharge reconciliation of chronic cardiovascular medications were common but not consistently associated with postdischarge acute hospital or unplanned ambulatory visits in a relatively low-risk cohort of patients undergoing elective surgery. While appropriate medication reconciliation should always be a priority, further study is needed to identify medication reconciliation approaches associated with postdischarge healthcare utilization and other patient outcomes.

Disclosure

Dr. Lee reports receiving grant support from the Health Resources and Services Administration (T32HP19025). Dr. Vittinghoff reports receiving grant support from the Agency for Healthcare Research and Quality. Dr. Auerbach and Dr. Fleischmann report receiving grant support from the National Institutes of Health. Dr. Auerbach also reports receiving honorarium as Editor-in-Chief of the Journal of Hospital Medicine. Dr. Corbett reports receiving grant and travel support from Simon Fraser University. The remaining authors have no disclosures to report.

© 2017 Society of Hospital Medicine

Strategies are needed to empower frontline clinicians to work with organizational leadership to reduce healthcare costs and improve high-value care. Caring Wisely^® is a program developed by the University of California, San Francisco’s (UCSF) Center for Healthcare Value (CHV), aimed at engaging frontline clinicians and staff, connecting them with implementation experts, and supporting the development of targeted interventions to improve value. Financial savings from the program more than cover program costs. Caring Wisely^® provides an institutional model for implementing robust interventions to address areas of low-value care.

Launched in 2013, the annual Caring Wisely^® program consists of 3 stages for identifying projects that meet the following criteria:

• Potential to measurably reduce UCSF Health’s costs of care without transferring costs to patients, insurers, or other providers
• Plan for ensuring that health outcomes are maintained or improved
• Envision disseminating the intervention within and beyond UCSF
• Demonstrate commitment and engagement of clinical leadership and frontline staff.

The first stage is the Ideas Contest, a UCSF Health-wide call (to learn more about UCSF Health: https://www.ucsf.edu/sites/default/files/052516_About_UCSF.pdf) to identify areas that may be targeted to reduce unnecessary services, inefficiencies, and healthcare costs. We use a crowdsourcing platform—Open Proposals—to solicit the best ideas from frontline clinicians and staff.¹ Open Proposals is a secure, web-based platform for transparent and collaborative proposal development that displays threads of comments, responses, and revisions, and allows submissions to be “liked.” Open Proposals is managed by the UCSF Clinical and Translational Science Institute, funded by the National Center for Advancing Translational Sciences (Grant Number UL1 TR000004) at the National Institutes of Health. Using institutional e-mail lists for faculty, staff and residents, as well as described at monthly managers and directors meetings, the Ideas Contest is announced each year by the Chief Medical Officer and the CHV leadership. The Caring Wisely^® Executive Steering Committee, which consists of CHV and senior UCSF Health system leaders, selects the top 5-10 ideas based on the above criteria. Each winning idea receives a $100 gift certificate for a popular restaurant in San Francisco, and the list of winners is announced to the entire UCSF community.

The second stage is the Request for Proposals. The Caring Wisely^® program solicits proposals that outline implementation plans to target specific areas identified through the Ideas Contest. Finalists from the Ideas Contest are encouraged to submit proposals that address the problem they identified, but anyone affiliated with UCSF Health may submit a proposal on a winning idea. There is an approximately 4-week open submission period during which applicants submit brief 2-page proposals on the Open Proposal platform. This is followed by a period of optimization that leverages the social media aspect of the Open Proposals platform in which the UCSF Health community asks clarifying questions, make suggestions, and modifications can be made to the proposals. All submissions receive written feedback from at least one Steering Committee member. In addition, the Caring Wisely^® Director directly invites relevant UCSF colleagues, administrators, or program leaders to comment on proposals and make suggestions for improvement. Plans for assessing financial and health care delivery impacts are developed in collaboration with the UCSF Health Finance department. UCSF Health managers and leaders who are stakeholders in project proposal areas are consulted to provide input and finalize proposal plans, including the identification of existing personnel who can support and drive the project forward. Proposers use this feedback to revise their applications throughout this stage.

The third stage is Project Implementation. The Caring Wisely^® Executive Steering Committee selects up to 3 winners from the submitted proposals. Using the program criteria above, each project is scored independently, discussed in committee, and rescored to identify the top proposals. Each selected project receives a maximum budget of $50,000 that can be used for project materials, activities, and salary support for project leaders or staff. In addition to funding, each project team receives input from the implementation science team to co-develop and implement the intervention with a goal of creating a first-test-of-change within 3-6 months. A key feature of Caring Wisely^® is the partnership between project teams and the Caring Wisely^® implementation team, which includes a director, program manager, data analysts, and implementation scientists (Table 1).

LIST OF ABBREVIATIONS

UCSF—University of California, San Francisco; PRECEDE—Predisposing, Reinforcing, and Enabling Constructs in Educational Diagnosis and Evaluation; PROCEED—Policy, Regulatory and Organizational Constructs in Educational and Environmental Development

Acknowledgments

Other participants in blood transfusion reduction project (D. Johnson, K. Curcione); IV-to-PO Switch (C. Tsourounis, A. Pollock); Surgical Supply Cost Reduction (C. Zygourakis); Perioperative Efficiency (L. Hampson); CT for PE Risk Prediction (E. Weber); ERAS Pathways (L. Chen); Neonatal Sepsis Risk Prediction (T. Newman); Post-Operative Debrief (S. Imershein). Caring Wisely Executive Steering Committee (J. Adler, S. Antrum, A Auerbach, J. Bennan, M. Blum, C. Ritchie, C. Tsourounis). This Center for Healthcare Value is funded in part by a grant from the Grove Foundation. We appreciate additional review and comments to the manuscript provided by George Sawaya and Adams Dudley.

Disclosures 

Christopher Moriates has accepted royalties from McGraw-Hill for textbook, Understanding Value-Based Healthcare. Alvin Rajkomar has received fees as a research adviser from Google, Inc.

© 2017 Society of Hospital Medicine

Strategies are needed to empower frontline clinicians to work with organizational leadership to reduce healthcare costs and improve high-value care. Caring Wisely^® is a program developed by the University of California, San Francisco’s (UCSF) Center for Healthcare Value (CHV), aimed at engaging frontline clinicians and staff, connecting them with implementation experts, and supporting the development of targeted interventions to improve value. Financial savings from the program more than cover program costs. Caring Wisely^® provides an institutional model for implementing robust interventions to address areas of low-value care.

Launched in 2013, the annual Caring Wisely^® program consists of 3 stages for identifying projects that meet the following criteria:

• Potential to measurably reduce UCSF Health’s costs of care without transferring costs to patients, insurers, or other providers
• Plan for ensuring that health outcomes are maintained or improved
• Envision disseminating the intervention within and beyond UCSF
• Demonstrate commitment and engagement of clinical leadership and frontline staff.

The first stage is the Ideas Contest, a UCSF Health-wide call (to learn more about UCSF Health: https://www.ucsf.edu/sites/default/files/052516_About_UCSF.pdf) to identify areas that may be targeted to reduce unnecessary services, inefficiencies, and healthcare costs. We use a crowdsourcing platform—Open Proposals—to solicit the best ideas from frontline clinicians and staff.¹ Open Proposals is a secure, web-based platform for transparent and collaborative proposal development that displays threads of comments, responses, and revisions, and allows submissions to be “liked.” Open Proposals is managed by the UCSF Clinical and Translational Science Institute, funded by the National Center for Advancing Translational Sciences (Grant Number UL1 TR000004) at the National Institutes of Health. Using institutional e-mail lists for faculty, staff and residents, as well as described at monthly managers and directors meetings, the Ideas Contest is announced each year by the Chief Medical Officer and the CHV leadership. The Caring Wisely^® Executive Steering Committee, which consists of CHV and senior UCSF Health system leaders, selects the top 5-10 ideas based on the above criteria. Each winning idea receives a $100 gift certificate for a popular restaurant in San Francisco, and the list of winners is announced to the entire UCSF community.

The second stage is the Request for Proposals. The Caring Wisely^® program solicits proposals that outline implementation plans to target specific areas identified through the Ideas Contest. Finalists from the Ideas Contest are encouraged to submit proposals that address the problem they identified, but anyone affiliated with UCSF Health may submit a proposal on a winning idea. There is an approximately 4-week open submission period during which applicants submit brief 2-page proposals on the Open Proposal platform. This is followed by a period of optimization that leverages the social media aspect of the Open Proposals platform in which the UCSF Health community asks clarifying questions, make suggestions, and modifications can be made to the proposals. All submissions receive written feedback from at least one Steering Committee member. In addition, the Caring Wisely^® Director directly invites relevant UCSF colleagues, administrators, or program leaders to comment on proposals and make suggestions for improvement. Plans for assessing financial and health care delivery impacts are developed in collaboration with the UCSF Health Finance department. UCSF Health managers and leaders who are stakeholders in project proposal areas are consulted to provide input and finalize proposal plans, including the identification of existing personnel who can support and drive the project forward. Proposers use this feedback to revise their applications throughout this stage.

The third stage is Project Implementation. The Caring Wisely^® Executive Steering Committee selects up to 3 winners from the submitted proposals. Using the program criteria above, each project is scored independently, discussed in committee, and rescored to identify the top proposals. Each selected project receives a maximum budget of $50,000 that can be used for project materials, activities, and salary support for project leaders or staff. In addition to funding, each project team receives input from the implementation science team to co-develop and implement the intervention with a goal of creating a first-test-of-change within 3-6 months. A key feature of Caring Wisely^® is the partnership between project teams and the Caring Wisely^® implementation team, which includes a director, program manager, data analysts, and implementation scientists (Table 1).

LIST OF ABBREVIATIONS

UCSF—University of California, San Francisco; PRECEDE—Predisposing, Reinforcing, and Enabling Constructs in Educational Diagnosis and Evaluation; PROCEED—Policy, Regulatory and Organizational Constructs in Educational and Environmental Development

Acknowledgments

Other participants in blood transfusion reduction project (D. Johnson, K. Curcione); IV-to-PO Switch (C. Tsourounis, A. Pollock); Surgical Supply Cost Reduction (C. Zygourakis); Perioperative Efficiency (L. Hampson); CT for PE Risk Prediction (E. Weber); ERAS Pathways (L. Chen); Neonatal Sepsis Risk Prediction (T. Newman); Post-Operative Debrief (S. Imershein). Caring Wisely Executive Steering Committee (J. Adler, S. Antrum, A Auerbach, J. Bennan, M. Blum, C. Ritchie, C. Tsourounis). This Center for Healthcare Value is funded in part by a grant from the Grove Foundation. We appreciate additional review and comments to the manuscript provided by George Sawaya and Adams Dudley.

Disclosures 

Christopher Moriates has accepted royalties from McGraw-Hill for textbook, Understanding Value-Based Healthcare. Alvin Rajkomar has received fees as a research adviser from Google, Inc.

© 2017 Society of Hospital Medicine

Strategies are needed to empower frontline clinicians to work with organizational leadership to reduce healthcare costs and improve high-value care. Caring Wisely^® is a program developed by the University of California, San Francisco’s (UCSF) Center for Healthcare Value (CHV), aimed at engaging frontline clinicians and staff, connecting them with implementation experts, and supporting the development of targeted interventions to improve value. Financial savings from the program more than cover program costs. Caring Wisely^® provides an institutional model for implementing robust interventions to address areas of low-value care.

Launched in 2013, the annual Caring Wisely^® program consists of 3 stages for identifying projects that meet the following criteria:

• Potential to measurably reduce UCSF Health’s costs of care without transferring costs to patients, insurers, or other providers
• Plan for ensuring that health outcomes are maintained or improved
• Envision disseminating the intervention within and beyond UCSF
• Demonstrate commitment and engagement of clinical leadership and frontline staff.

The first stage is the Ideas Contest, a UCSF Health-wide call (to learn more about UCSF Health: https://www.ucsf.edu/sites/default/files/052516_About_UCSF.pdf) to identify areas that may be targeted to reduce unnecessary services, inefficiencies, and healthcare costs. We use a crowdsourcing platform—Open Proposals—to solicit the best ideas from frontline clinicians and staff.¹ Open Proposals is a secure, web-based platform for transparent and collaborative proposal development that displays threads of comments, responses, and revisions, and allows submissions to be “liked.” Open Proposals is managed by the UCSF Clinical and Translational Science Institute, funded by the National Center for Advancing Translational Sciences (Grant Number UL1 TR000004) at the National Institutes of Health. Using institutional e-mail lists for faculty, staff and residents, as well as described at monthly managers and directors meetings, the Ideas Contest is announced each year by the Chief Medical Officer and the CHV leadership. The Caring Wisely^® Executive Steering Committee, which consists of CHV and senior UCSF Health system leaders, selects the top 5-10 ideas based on the above criteria. Each winning idea receives a $100 gift certificate for a popular restaurant in San Francisco, and the list of winners is announced to the entire UCSF community.

The second stage is the Request for Proposals. The Caring Wisely^® program solicits proposals that outline implementation plans to target specific areas identified through the Ideas Contest. Finalists from the Ideas Contest are encouraged to submit proposals that address the problem they identified, but anyone affiliated with UCSF Health may submit a proposal on a winning idea. There is an approximately 4-week open submission period during which applicants submit brief 2-page proposals on the Open Proposal platform. This is followed by a period of optimization that leverages the social media aspect of the Open Proposals platform in which the UCSF Health community asks clarifying questions, make suggestions, and modifications can be made to the proposals. All submissions receive written feedback from at least one Steering Committee member. In addition, the Caring Wisely^® Director directly invites relevant UCSF colleagues, administrators, or program leaders to comment on proposals and make suggestions for improvement. Plans for assessing financial and health care delivery impacts are developed in collaboration with the UCSF Health Finance department. UCSF Health managers and leaders who are stakeholders in project proposal areas are consulted to provide input and finalize proposal plans, including the identification of existing personnel who can support and drive the project forward. Proposers use this feedback to revise their applications throughout this stage.

The third stage is Project Implementation. The Caring Wisely^® Executive Steering Committee selects up to 3 winners from the submitted proposals. Using the program criteria above, each project is scored independently, discussed in committee, and rescored to identify the top proposals. Each selected project receives a maximum budget of $50,000 that can be used for project materials, activities, and salary support for project leaders or staff. In addition to funding, each project team receives input from the implementation science team to co-develop and implement the intervention with a goal of creating a first-test-of-change within 3-6 months. A key feature of Caring Wisely^® is the partnership between project teams and the Caring Wisely^® implementation team, which includes a director, program manager, data analysts, and implementation scientists (Table 1).

LIST OF ABBREVIATIONS

UCSF—University of California, San Francisco; PRECEDE—Predisposing, Reinforcing, and Enabling Constructs in Educational Diagnosis and Evaluation; PROCEED—Policy, Regulatory and Organizational Constructs in Educational and Environmental Development

Acknowledgments

Other participants in blood transfusion reduction project (D. Johnson, K. Curcione); IV-to-PO Switch (C. Tsourounis, A. Pollock); Surgical Supply Cost Reduction (C. Zygourakis); Perioperative Efficiency (L. Hampson); CT for PE Risk Prediction (E. Weber); ERAS Pathways (L. Chen); Neonatal Sepsis Risk Prediction (T. Newman); Post-Operative Debrief (S. Imershein). Caring Wisely Executive Steering Committee (J. Adler, S. Antrum, A Auerbach, J. Bennan, M. Blum, C. Ritchie, C. Tsourounis). This Center for Healthcare Value is funded in part by a grant from the Grove Foundation. We appreciate additional review and comments to the manuscript provided by George Sawaya and Adams Dudley.

Disclosures 

Christopher Moriates has accepted royalties from McGraw-Hill for textbook, Understanding Value-Based Healthcare. Alvin Rajkomar has received fees as a research adviser from Google, Inc.