Hospital Medicine

From Stamford Hospital, Stamford, CT.

Objective: This single-center, quasi-experimental study of adult patients admitted or transferred to a medical step-down unit with alcohol withdrawal diagnoses sought to determine if symptom–triggered therapy (STT) is more effective than combined fixed-scheduled (FS) and STT in severe alcohol withdrawal.

Methods: In the preintervention group (72 episodes), patients were treated with FS and STT based on physician preference. In the postintervention group (69 episodes), providers were required to utilize only the STT protocol.

Results: Implementation of the intervention was associated with a significant reduction in average (per patient) cumulative benzodiazepine dose, from 250 mg to 96 mg (P < .001) and a decrease in average length of stay from 8.0 days to 5.1 days (P < .001). Secondary safety measures included a reduction in the proportion of patients who experienced delirium tremens from 47.5% to 22.5% (P < .001), and a reduction in intubation rates from 13.8% to 1.3% (P = .003).

Conclusion: The STT protocol proved to be more effective and safer in treating severe alcohol withdrawal patients than usual care employing STT with FS. We believe the successful implementation of a STT protocol in high-acuity patients requires frequent monitoring to assess withdrawal severity combined with appropriate and timely dosing of benzodiazepines.

Keywords: alcohol withdrawal delirium; alcohol withdrawal syndrome; treatment protocol; benzodiazepine; lorazepam.

Management of severe alcohol withdrawal and delirium tremens (DT) is challenging and requires significant resources, including close monitoring and intensive treatment, frequently in an intensive care unit (ICU).¹ Early diagnosis and therapeutic intervention are important to limit potential complications associated with DT.² Benzodiazepines are first-line therapeutic agents, but the definition of optimal use and dosing regimens has been limited, due to a lack of randomized controlled trials. In lower acuity patients admitted to a detoxification unit, systematic symptom–triggered benzodiazepine therapy (STT) has been established to be more effective than fixed-schedule (FS) dosing.^3-5 Patients treated using STT require lower total benzodiazepine dosing and achieve shorter treatment durations. However, in higher-acuity patients admitted to general medical services, analyses have not shown an advantage of STT over combined FS and STT.⁶

The purpose of this study was to determine whether implementation of STT is more effective than FS dosing combined with episodic STT in the management of hospitalized high-acuity alcohol withdrawal patients. We conducted a preintervention and postintervention quasi-experimental study in the step-down unit (SDU) of a 305-bed community teaching hospital. The study population consisted of adult inpatients 18 years or older admitted or transferred to the 12-bed SDU with alcohol withdrawal, as defined by primary or secondary International Classification of Diseases, Tenth Revision diagnoses. SDU admission criteria included patients with prior DT or those who had received multiple doses of benzodiazepines in the emergency department. In-hospital transfer to the SDU was at the physician’s discretion, if the patient required escalating doses of benzodiazepines or the use of increasing resources, such as those for behavioral emergencies. The majority of patients admitted or transferred to the SDU were assigned to medical house staff teams under hospitalist supervision, and, on occasion, under community physicians. The nurse-to-patient ratio in the SDU was 1:3.

Study groups

The preintervention group consisted of 80 successive treatment episodes involving patients admitted or transferred to the SDU from December 2, 2015, to July 1, 2017. Patients were treated based upon physician preference, consisting of a scheduled dosing regimen with additional doses as needed. The postintervention group included 80 successive treatment episodes involving patients admitted or transferred to the SDU from October 1, 2017, to March 23, 2019. The STT protocol was used in all patients in the postintervention group.

In the preintervention group, fixed, scheduled doses of lorazepam or chlordiazepoxide and as-needed lorazepam were prescribed and adjusted based upon physician judgment. Monitoring of symptom severity was scored using the revised Clinical Institute Withdrawal Assessment for Alcohol scale (CIWA-Ar). Benzodiazepine dosing occurred if the CIWA-Ar score had increased 2 or more points from the last score.

In the postintervention group, the STT protocol included the creation of a standardized physician order set for benzodiazepine “sliding scale” administration. The STT protocol allowed for escalating doses for higher withdrawal scores. Symptom severity was scored using MINDS (Minnesota Detoxification Scale) criteria.¹ Lorazepam as-needed dosing was based upon MINDS scores. A MINDS score less than 10 resulted in no medication, MINDS 10-12 required 2 mg, MINDS 13-16 required 4 mg, MINDS 17-19 required 6 mg, and MINDS 20 required 8 mg and a call to the physician. Transfer to the ICU was recommended if the MINDS score was ≥ 20 for 3 consecutive hours. Monitoring intervals occurred more frequently at 30 minutes unless the MINDS score was less than 10. After 7 days, the MINDS protocol was recommended to be discontinued, as the patient might have had iatrogenic delirium.

The STT protocol was introduced during a didactic session for the hospitalists and a separate session for internal medicine and family residents. Each registered nurse working in the SDU was trained in the use of the STT protocol and MINDS during nursing huddles.

Patients were excluded from evaluation if they were transferred to the SDU after 7 or more days in the hospital, if they had stayed in the hospital more than 30 days, were chronically on benzodiazepine therapy (to avoid confounding withdrawal symptoms), or if they left the hospital against medical advice (AMA). To avoid bias in the results, the patients with early discontinuation of treatment were included in analyses of secondary outcomes, thus resulting in all 80 episodes analyzed.

Measures and data

The primary outcome measure was benzodiazepine dose intensity, expressed in total lorazepam-equivalents. Secondary measures included average length of stay (including general medical, surgical, and ICU days), seizure incidence, DT incidence, sitter use, behavioral emergency responses, rates of leaving AMA, intubation, transfer to the ICU, and death.

Benzodiazepine dosing and length of stay were obtained from the data warehouse of the hospital’s electronic health record (EHR; Meditech). Benzodiazepine dosing was expressed in total lorazepam-equivalents, with conversion as follows: lorazepam orally and intravenously 1 mg = chlordiazepoxide 25 mg = diazepam 5 mg. All other measures were obtained from chart review of the patients’ EMR entries. The Stamford Hospital Institutional Review Board approved this study.

Analysis

Data analyses for this study were performed using SPSS version 25.0 (IBM). Categorical data were reported as frequency (count) and percent within category. Continuous data were reported as mean (SD). Categorical data were analyzed using χ² analysis; continuous data were analyzed using t-tests. A P value of .05 was considered significant for each analysis.

Results

During the preintervention period, 72 episodes (58 patients) met inclusion criteria, and 69 episodes (55 patients) met inclusion criteria during the postintervention period. Ten patients were represented in both groups. Eight preintervention episodes were excluded from the primary analysis because the patient left AMA. Eleven postintervention episodes were excluded: 9 due to patients leaving AMA, 1 due to chronic benzodiazepine usage, and 1 due to transfer to the SDU unit after 7 days. Baseline characteristics and medication use profiles of the preintervention and postintervention groups are summarized in Table 1.

Implementation of the intervention was associated with a significant reduction in average (per patient) cumulative benzodiazepine dose, from 250 mg to 96 mg (P < .001), as shown in Table 2. Average length of stay decreased from 8.0 days to 5.1 days (P < .001). Secondary safety measures were notable for a reduction in DT incidence, from 47.5% to 22.5% (P < .001), and lower rates of intubation, from 13.8% to 1.3% (P = .003). Seven-day readmission rates were 0% preintervention and 1.4% postintervention.

Discussion

We found that hospitalized patients with severe alcohol withdrawal treated with STT required fewer benzodiazepines and had a lower length of stay than patients treated with a conventional combined STT and FS regimen. Implementation of the change from the STT and FS approach to the STT approach in the SDU resulted in concerns that waiting for symptoms to appear could result in more severe withdrawal and prolonged treatment.³ To address this, the intervention included monitoring and dosing every 30 minutes, as compared to monitoring and dosing every 1 hour preintervention. In addition, a sliding-scale approach to match alcohol withdrawal score with dosage was employed in postintervention patients.

Employment of the STT protocol also resulted in decreased complications, including lower rates of DT and transfer to the ICU. This new intervention resulted in significantly decreased time required to control severe symptoms. In the preintervention phase, if a patient’s symptoms escalated despite administration of the as-needed dose of benzodiazepine, there was often a delay in administration of additional doses due to the time needed for nurses to reach a physician and subsequent placement of a new order. In the postintervention phase, the STT protocol allowed nursing staff to give benzodiazepines without delay when needed. We believe this reduced the number of calls by nursing staff to physicians requesting additional medications, and that this improved teamwork when managing these patients.

As part of the intervention, a decision was made to use the MINDS scale rather than the CIWA-Ar scale to assess withdrawal severity. This was because the CIWA-Ar has only been validated in patients with uncomplicated alcohol withdrawal syndrome and has not been researched extensively in patients requiring ICU-level care.¹ MINDS assessment has proven to be reliable and reflects severity of withdrawal. Furthermore, MINDS requires less time to administer—3 to 5 minutes vs 5 to 15 minutes for the CIWA-Ar scale. CIWA-Ar, unlike MINDS, requires subjective input from the patient, which is less reliable for higher acuity patients. Our study is unique in that it focused on high-acuity patients and it showed both a significant reduction in quantity of benzodiazepines prescribed and length of stay. Previous studies on lower acuity patients in detoxification units have confirmed that STT is more effective than a FS approach.^3-5 In patients of higher acuity, STT has not proven to be superior.

A key lesson learned was the need for proper education of nursing staff. Concurrent nursing audits were necessary to ensure that scoring was performed in an accurate and timely manner. In addition, it was challenging to predict which patients might develop DTs versus those requiring a brief inpatient stay. While there was initial concern that an STT protocol could result in underdosing, we found that patients had fewer DT episodes and fewer ICU transfers.

This study had several limitations. These include a relatively small sample size and the data being less recent. As there has been no intervening change to the therapeutic paradigm of DT treatment, the findings remain pertinent to the present time. The study employed a simple pre/post design and was conducted in a single setting. We are not aware of any temporal or local trends likely to influence these results. Admissions and transfers to the SDU for severe alcohol withdrawal were based on physician discretion. However, patient characteristics in both groups were similar (Table 1). We note that the postintervention STT protocol allowed for more frequent benzodiazepine dosing, though benzodiazepine use did decrease. Different alcohol withdrawal scores (MINDS vs. CIWA-Ar) were used for postintervention and preintervention, although previous research has shown that MINDS and CIWA-Ar scores correlate well.⁷ Finally, some patients of higher acuity and complexity were excluded, potentially limiting the generalizability of our results.

Conclusion

Our STT protocol proved to be more effective and safer in treating severe alcohol withdrawal patients than usual care employing STT with FS. We believe the successful implementation of a STT protocol in high-acuity patients also requires frequent monitoring using the MINDS scale, integrated with benzodiazepine sliding-scale dosing to match symptom severity. This bundled approach resulted in a significant reduction of benzodiazepine usage and reduced length of stay. Timely treatment of these patients also reduced the percent of patients developing DTs, and reduced intubation rates and transfers to the ICU. Further studies may be warranted at other sites to confirm the effectiveness of this STT protocol.

Corresponding author: Paul W. Huang, MD, Stamford Hospital, One Hospital Plaza, PO Box 9317, Stamford, CT 06904; [email protected].

Financial disclosures: None.

From Stamford Hospital, Stamford, CT.

Objective: This single-center, quasi-experimental study of adult patients admitted or transferred to a medical step-down unit with alcohol withdrawal diagnoses sought to determine if symptom–triggered therapy (STT) is more effective than combined fixed-scheduled (FS) and STT in severe alcohol withdrawal.

Methods: In the preintervention group (72 episodes), patients were treated with FS and STT based on physician preference. In the postintervention group (69 episodes), providers were required to utilize only the STT protocol.

Results: Implementation of the intervention was associated with a significant reduction in average (per patient) cumulative benzodiazepine dose, from 250 mg to 96 mg (P < .001) and a decrease in average length of stay from 8.0 days to 5.1 days (P < .001). Secondary safety measures included a reduction in the proportion of patients who experienced delirium tremens from 47.5% to 22.5% (P < .001), and a reduction in intubation rates from 13.8% to 1.3% (P = .003).

Conclusion: The STT protocol proved to be more effective and safer in treating severe alcohol withdrawal patients than usual care employing STT with FS. We believe the successful implementation of a STT protocol in high-acuity patients requires frequent monitoring to assess withdrawal severity combined with appropriate and timely dosing of benzodiazepines.

Keywords: alcohol withdrawal delirium; alcohol withdrawal syndrome; treatment protocol; benzodiazepine; lorazepam.

Management of severe alcohol withdrawal and delirium tremens (DT) is challenging and requires significant resources, including close monitoring and intensive treatment, frequently in an intensive care unit (ICU).¹ Early diagnosis and therapeutic intervention are important to limit potential complications associated with DT.² Benzodiazepines are first-line therapeutic agents, but the definition of optimal use and dosing regimens has been limited, due to a lack of randomized controlled trials. In lower acuity patients admitted to a detoxification unit, systematic symptom–triggered benzodiazepine therapy (STT) has been established to be more effective than fixed-schedule (FS) dosing.^3-5 Patients treated using STT require lower total benzodiazepine dosing and achieve shorter treatment durations. However, in higher-acuity patients admitted to general medical services, analyses have not shown an advantage of STT over combined FS and STT.⁶

The purpose of this study was to determine whether implementation of STT is more effective than FS dosing combined with episodic STT in the management of hospitalized high-acuity alcohol withdrawal patients. We conducted a preintervention and postintervention quasi-experimental study in the step-down unit (SDU) of a 305-bed community teaching hospital. The study population consisted of adult inpatients 18 years or older admitted or transferred to the 12-bed SDU with alcohol withdrawal, as defined by primary or secondary International Classification of Diseases, Tenth Revision diagnoses. SDU admission criteria included patients with prior DT or those who had received multiple doses of benzodiazepines in the emergency department. In-hospital transfer to the SDU was at the physician’s discretion, if the patient required escalating doses of benzodiazepines or the use of increasing resources, such as those for behavioral emergencies. The majority of patients admitted or transferred to the SDU were assigned to medical house staff teams under hospitalist supervision, and, on occasion, under community physicians. The nurse-to-patient ratio in the SDU was 1:3.

Study groups

The preintervention group consisted of 80 successive treatment episodes involving patients admitted or transferred to the SDU from December 2, 2015, to July 1, 2017. Patients were treated based upon physician preference, consisting of a scheduled dosing regimen with additional doses as needed. The postintervention group included 80 successive treatment episodes involving patients admitted or transferred to the SDU from October 1, 2017, to March 23, 2019. The STT protocol was used in all patients in the postintervention group.

In the preintervention group, fixed, scheduled doses of lorazepam or chlordiazepoxide and as-needed lorazepam were prescribed and adjusted based upon physician judgment. Monitoring of symptom severity was scored using the revised Clinical Institute Withdrawal Assessment for Alcohol scale (CIWA-Ar). Benzodiazepine dosing occurred if the CIWA-Ar score had increased 2 or more points from the last score.

In the postintervention group, the STT protocol included the creation of a standardized physician order set for benzodiazepine “sliding scale” administration. The STT protocol allowed for escalating doses for higher withdrawal scores. Symptom severity was scored using MINDS (Minnesota Detoxification Scale) criteria.¹ Lorazepam as-needed dosing was based upon MINDS scores. A MINDS score less than 10 resulted in no medication, MINDS 10-12 required 2 mg, MINDS 13-16 required 4 mg, MINDS 17-19 required 6 mg, and MINDS 20 required 8 mg and a call to the physician. Transfer to the ICU was recommended if the MINDS score was ≥ 20 for 3 consecutive hours. Monitoring intervals occurred more frequently at 30 minutes unless the MINDS score was less than 10. After 7 days, the MINDS protocol was recommended to be discontinued, as the patient might have had iatrogenic delirium.

The STT protocol was introduced during a didactic session for the hospitalists and a separate session for internal medicine and family residents. Each registered nurse working in the SDU was trained in the use of the STT protocol and MINDS during nursing huddles.

Patients were excluded from evaluation if they were transferred to the SDU after 7 or more days in the hospital, if they had stayed in the hospital more than 30 days, were chronically on benzodiazepine therapy (to avoid confounding withdrawal symptoms), or if they left the hospital against medical advice (AMA). To avoid bias in the results, the patients with early discontinuation of treatment were included in analyses of secondary outcomes, thus resulting in all 80 episodes analyzed.

Measures and data

The primary outcome measure was benzodiazepine dose intensity, expressed in total lorazepam-equivalents. Secondary measures included average length of stay (including general medical, surgical, and ICU days), seizure incidence, DT incidence, sitter use, behavioral emergency responses, rates of leaving AMA, intubation, transfer to the ICU, and death.

Benzodiazepine dosing and length of stay were obtained from the data warehouse of the hospital’s electronic health record (EHR; Meditech). Benzodiazepine dosing was expressed in total lorazepam-equivalents, with conversion as follows: lorazepam orally and intravenously 1 mg = chlordiazepoxide 25 mg = diazepam 5 mg. All other measures were obtained from chart review of the patients’ EMR entries. The Stamford Hospital Institutional Review Board approved this study.

Analysis

Data analyses for this study were performed using SPSS version 25.0 (IBM). Categorical data were reported as frequency (count) and percent within category. Continuous data were reported as mean (SD). Categorical data were analyzed using χ² analysis; continuous data were analyzed using t-tests. A P value of .05 was considered significant for each analysis.

Results

During the preintervention period, 72 episodes (58 patients) met inclusion criteria, and 69 episodes (55 patients) met inclusion criteria during the postintervention period. Ten patients were represented in both groups. Eight preintervention episodes were excluded from the primary analysis because the patient left AMA. Eleven postintervention episodes were excluded: 9 due to patients leaving AMA, 1 due to chronic benzodiazepine usage, and 1 due to transfer to the SDU unit after 7 days. Baseline characteristics and medication use profiles of the preintervention and postintervention groups are summarized in Table 1.

Implementation of the intervention was associated with a significant reduction in average (per patient) cumulative benzodiazepine dose, from 250 mg to 96 mg (P < .001), as shown in Table 2. Average length of stay decreased from 8.0 days to 5.1 days (P < .001). Secondary safety measures were notable for a reduction in DT incidence, from 47.5% to 22.5% (P < .001), and lower rates of intubation, from 13.8% to 1.3% (P = .003). Seven-day readmission rates were 0% preintervention and 1.4% postintervention.

Discussion

We found that hospitalized patients with severe alcohol withdrawal treated with STT required fewer benzodiazepines and had a lower length of stay than patients treated with a conventional combined STT and FS regimen. Implementation of the change from the STT and FS approach to the STT approach in the SDU resulted in concerns that waiting for symptoms to appear could result in more severe withdrawal and prolonged treatment.³ To address this, the intervention included monitoring and dosing every 30 minutes, as compared to monitoring and dosing every 1 hour preintervention. In addition, a sliding-scale approach to match alcohol withdrawal score with dosage was employed in postintervention patients.

Employment of the STT protocol also resulted in decreased complications, including lower rates of DT and transfer to the ICU. This new intervention resulted in significantly decreased time required to control severe symptoms. In the preintervention phase, if a patient’s symptoms escalated despite administration of the as-needed dose of benzodiazepine, there was often a delay in administration of additional doses due to the time needed for nurses to reach a physician and subsequent placement of a new order. In the postintervention phase, the STT protocol allowed nursing staff to give benzodiazepines without delay when needed. We believe this reduced the number of calls by nursing staff to physicians requesting additional medications, and that this improved teamwork when managing these patients.

As part of the intervention, a decision was made to use the MINDS scale rather than the CIWA-Ar scale to assess withdrawal severity. This was because the CIWA-Ar has only been validated in patients with uncomplicated alcohol withdrawal syndrome and has not been researched extensively in patients requiring ICU-level care.¹ MINDS assessment has proven to be reliable and reflects severity of withdrawal. Furthermore, MINDS requires less time to administer—3 to 5 minutes vs 5 to 15 minutes for the CIWA-Ar scale. CIWA-Ar, unlike MINDS, requires subjective input from the patient, which is less reliable for higher acuity patients. Our study is unique in that it focused on high-acuity patients and it showed both a significant reduction in quantity of benzodiazepines prescribed and length of stay. Previous studies on lower acuity patients in detoxification units have confirmed that STT is more effective than a FS approach.^3-5 In patients of higher acuity, STT has not proven to be superior.

A key lesson learned was the need for proper education of nursing staff. Concurrent nursing audits were necessary to ensure that scoring was performed in an accurate and timely manner. In addition, it was challenging to predict which patients might develop DTs versus those requiring a brief inpatient stay. While there was initial concern that an STT protocol could result in underdosing, we found that patients had fewer DT episodes and fewer ICU transfers.

This study had several limitations. These include a relatively small sample size and the data being less recent. As there has been no intervening change to the therapeutic paradigm of DT treatment, the findings remain pertinent to the present time. The study employed a simple pre/post design and was conducted in a single setting. We are not aware of any temporal or local trends likely to influence these results. Admissions and transfers to the SDU for severe alcohol withdrawal were based on physician discretion. However, patient characteristics in both groups were similar (Table 1). We note that the postintervention STT protocol allowed for more frequent benzodiazepine dosing, though benzodiazepine use did decrease. Different alcohol withdrawal scores (MINDS vs. CIWA-Ar) were used for postintervention and preintervention, although previous research has shown that MINDS and CIWA-Ar scores correlate well.⁷ Finally, some patients of higher acuity and complexity were excluded, potentially limiting the generalizability of our results.

Conclusion

Our STT protocol proved to be more effective and safer in treating severe alcohol withdrawal patients than usual care employing STT with FS. We believe the successful implementation of a STT protocol in high-acuity patients also requires frequent monitoring using the MINDS scale, integrated with benzodiazepine sliding-scale dosing to match symptom severity. This bundled approach resulted in a significant reduction of benzodiazepine usage and reduced length of stay. Timely treatment of these patients also reduced the percent of patients developing DTs, and reduced intubation rates and transfers to the ICU. Further studies may be warranted at other sites to confirm the effectiveness of this STT protocol.

Corresponding author: Paul W. Huang, MD, Stamford Hospital, One Hospital Plaza, PO Box 9317, Stamford, CT 06904; [email protected].

Financial disclosures: None.

From Stamford Hospital, Stamford, CT.

Objective: This single-center, quasi-experimental study of adult patients admitted or transferred to a medical step-down unit with alcohol withdrawal diagnoses sought to determine if symptom–triggered therapy (STT) is more effective than combined fixed-scheduled (FS) and STT in severe alcohol withdrawal.

Methods: In the preintervention group (72 episodes), patients were treated with FS and STT based on physician preference. In the postintervention group (69 episodes), providers were required to utilize only the STT protocol.

Results: Implementation of the intervention was associated with a significant reduction in average (per patient) cumulative benzodiazepine dose, from 250 mg to 96 mg (P < .001) and a decrease in average length of stay from 8.0 days to 5.1 days (P < .001). Secondary safety measures included a reduction in the proportion of patients who experienced delirium tremens from 47.5% to 22.5% (P < .001), and a reduction in intubation rates from 13.8% to 1.3% (P = .003).

Conclusion: The STT protocol proved to be more effective and safer in treating severe alcohol withdrawal patients than usual care employing STT with FS. We believe the successful implementation of a STT protocol in high-acuity patients requires frequent monitoring to assess withdrawal severity combined with appropriate and timely dosing of benzodiazepines.

Keywords: alcohol withdrawal delirium; alcohol withdrawal syndrome; treatment protocol; benzodiazepine; lorazepam.

Management of severe alcohol withdrawal and delirium tremens (DT) is challenging and requires significant resources, including close monitoring and intensive treatment, frequently in an intensive care unit (ICU).¹ Early diagnosis and therapeutic intervention are important to limit potential complications associated with DT.² Benzodiazepines are first-line therapeutic agents, but the definition of optimal use and dosing regimens has been limited, due to a lack of randomized controlled trials. In lower acuity patients admitted to a detoxification unit, systematic symptom–triggered benzodiazepine therapy (STT) has been established to be more effective than fixed-schedule (FS) dosing.^3-5 Patients treated using STT require lower total benzodiazepine dosing and achieve shorter treatment durations. However, in higher-acuity patients admitted to general medical services, analyses have not shown an advantage of STT over combined FS and STT.⁶

The purpose of this study was to determine whether implementation of STT is more effective than FS dosing combined with episodic STT in the management of hospitalized high-acuity alcohol withdrawal patients. We conducted a preintervention and postintervention quasi-experimental study in the step-down unit (SDU) of a 305-bed community teaching hospital. The study population consisted of adult inpatients 18 years or older admitted or transferred to the 12-bed SDU with alcohol withdrawal, as defined by primary or secondary International Classification of Diseases, Tenth Revision diagnoses. SDU admission criteria included patients with prior DT or those who had received multiple doses of benzodiazepines in the emergency department. In-hospital transfer to the SDU was at the physician’s discretion, if the patient required escalating doses of benzodiazepines or the use of increasing resources, such as those for behavioral emergencies. The majority of patients admitted or transferred to the SDU were assigned to medical house staff teams under hospitalist supervision, and, on occasion, under community physicians. The nurse-to-patient ratio in the SDU was 1:3.

Study groups

The preintervention group consisted of 80 successive treatment episodes involving patients admitted or transferred to the SDU from December 2, 2015, to July 1, 2017. Patients were treated based upon physician preference, consisting of a scheduled dosing regimen with additional doses as needed. The postintervention group included 80 successive treatment episodes involving patients admitted or transferred to the SDU from October 1, 2017, to March 23, 2019. The STT protocol was used in all patients in the postintervention group.

In the preintervention group, fixed, scheduled doses of lorazepam or chlordiazepoxide and as-needed lorazepam were prescribed and adjusted based upon physician judgment. Monitoring of symptom severity was scored using the revised Clinical Institute Withdrawal Assessment for Alcohol scale (CIWA-Ar). Benzodiazepine dosing occurred if the CIWA-Ar score had increased 2 or more points from the last score.

In the postintervention group, the STT protocol included the creation of a standardized physician order set for benzodiazepine “sliding scale” administration. The STT protocol allowed for escalating doses for higher withdrawal scores. Symptom severity was scored using MINDS (Minnesota Detoxification Scale) criteria.¹ Lorazepam as-needed dosing was based upon MINDS scores. A MINDS score less than 10 resulted in no medication, MINDS 10-12 required 2 mg, MINDS 13-16 required 4 mg, MINDS 17-19 required 6 mg, and MINDS 20 required 8 mg and a call to the physician. Transfer to the ICU was recommended if the MINDS score was ≥ 20 for 3 consecutive hours. Monitoring intervals occurred more frequently at 30 minutes unless the MINDS score was less than 10. After 7 days, the MINDS protocol was recommended to be discontinued, as the patient might have had iatrogenic delirium.

The STT protocol was introduced during a didactic session for the hospitalists and a separate session for internal medicine and family residents. Each registered nurse working in the SDU was trained in the use of the STT protocol and MINDS during nursing huddles.

Patients were excluded from evaluation if they were transferred to the SDU after 7 or more days in the hospital, if they had stayed in the hospital more than 30 days, were chronically on benzodiazepine therapy (to avoid confounding withdrawal symptoms), or if they left the hospital against medical advice (AMA). To avoid bias in the results, the patients with early discontinuation of treatment were included in analyses of secondary outcomes, thus resulting in all 80 episodes analyzed.

Measures and data

The primary outcome measure was benzodiazepine dose intensity, expressed in total lorazepam-equivalents. Secondary measures included average length of stay (including general medical, surgical, and ICU days), seizure incidence, DT incidence, sitter use, behavioral emergency responses, rates of leaving AMA, intubation, transfer to the ICU, and death.

Benzodiazepine dosing and length of stay were obtained from the data warehouse of the hospital’s electronic health record (EHR; Meditech). Benzodiazepine dosing was expressed in total lorazepam-equivalents, with conversion as follows: lorazepam orally and intravenously 1 mg = chlordiazepoxide 25 mg = diazepam 5 mg. All other measures were obtained from chart review of the patients’ EMR entries. The Stamford Hospital Institutional Review Board approved this study.

Analysis

Data analyses for this study were performed using SPSS version 25.0 (IBM). Categorical data were reported as frequency (count) and percent within category. Continuous data were reported as mean (SD). Categorical data were analyzed using χ² analysis; continuous data were analyzed using t-tests. A P value of .05 was considered significant for each analysis.

Results

During the preintervention period, 72 episodes (58 patients) met inclusion criteria, and 69 episodes (55 patients) met inclusion criteria during the postintervention period. Ten patients were represented in both groups. Eight preintervention episodes were excluded from the primary analysis because the patient left AMA. Eleven postintervention episodes were excluded: 9 due to patients leaving AMA, 1 due to chronic benzodiazepine usage, and 1 due to transfer to the SDU unit after 7 days. Baseline characteristics and medication use profiles of the preintervention and postintervention groups are summarized in Table 1.

Implementation of the intervention was associated with a significant reduction in average (per patient) cumulative benzodiazepine dose, from 250 mg to 96 mg (P < .001), as shown in Table 2. Average length of stay decreased from 8.0 days to 5.1 days (P < .001). Secondary safety measures were notable for a reduction in DT incidence, from 47.5% to 22.5% (P < .001), and lower rates of intubation, from 13.8% to 1.3% (P = .003). Seven-day readmission rates were 0% preintervention and 1.4% postintervention.

Discussion

We found that hospitalized patients with severe alcohol withdrawal treated with STT required fewer benzodiazepines and had a lower length of stay than patients treated with a conventional combined STT and FS regimen. Implementation of the change from the STT and FS approach to the STT approach in the SDU resulted in concerns that waiting for symptoms to appear could result in more severe withdrawal and prolonged treatment.³ To address this, the intervention included monitoring and dosing every 30 minutes, as compared to monitoring and dosing every 1 hour preintervention. In addition, a sliding-scale approach to match alcohol withdrawal score with dosage was employed in postintervention patients.

Employment of the STT protocol also resulted in decreased complications, including lower rates of DT and transfer to the ICU. This new intervention resulted in significantly decreased time required to control severe symptoms. In the preintervention phase, if a patient’s symptoms escalated despite administration of the as-needed dose of benzodiazepine, there was often a delay in administration of additional doses due to the time needed for nurses to reach a physician and subsequent placement of a new order. In the postintervention phase, the STT protocol allowed nursing staff to give benzodiazepines without delay when needed. We believe this reduced the number of calls by nursing staff to physicians requesting additional medications, and that this improved teamwork when managing these patients.

As part of the intervention, a decision was made to use the MINDS scale rather than the CIWA-Ar scale to assess withdrawal severity. This was because the CIWA-Ar has only been validated in patients with uncomplicated alcohol withdrawal syndrome and has not been researched extensively in patients requiring ICU-level care.¹ MINDS assessment has proven to be reliable and reflects severity of withdrawal. Furthermore, MINDS requires less time to administer—3 to 5 minutes vs 5 to 15 minutes for the CIWA-Ar scale. CIWA-Ar, unlike MINDS, requires subjective input from the patient, which is less reliable for higher acuity patients. Our study is unique in that it focused on high-acuity patients and it showed both a significant reduction in quantity of benzodiazepines prescribed and length of stay. Previous studies on lower acuity patients in detoxification units have confirmed that STT is more effective than a FS approach.^3-5 In patients of higher acuity, STT has not proven to be superior.

A key lesson learned was the need for proper education of nursing staff. Concurrent nursing audits were necessary to ensure that scoring was performed in an accurate and timely manner. In addition, it was challenging to predict which patients might develop DTs versus those requiring a brief inpatient stay. While there was initial concern that an STT protocol could result in underdosing, we found that patients had fewer DT episodes and fewer ICU transfers.

This study had several limitations. These include a relatively small sample size and the data being less recent. As there has been no intervening change to the therapeutic paradigm of DT treatment, the findings remain pertinent to the present time. The study employed a simple pre/post design and was conducted in a single setting. We are not aware of any temporal or local trends likely to influence these results. Admissions and transfers to the SDU for severe alcohol withdrawal were based on physician discretion. However, patient characteristics in both groups were similar (Table 1). We note that the postintervention STT protocol allowed for more frequent benzodiazepine dosing, though benzodiazepine use did decrease. Different alcohol withdrawal scores (MINDS vs. CIWA-Ar) were used for postintervention and preintervention, although previous research has shown that MINDS and CIWA-Ar scores correlate well.⁷ Finally, some patients of higher acuity and complexity were excluded, potentially limiting the generalizability of our results.

Conclusion

Our STT protocol proved to be more effective and safer in treating severe alcohol withdrawal patients than usual care employing STT with FS. We believe the successful implementation of a STT protocol in high-acuity patients also requires frequent monitoring using the MINDS scale, integrated with benzodiazepine sliding-scale dosing to match symptom severity. This bundled approach resulted in a significant reduction of benzodiazepine usage and reduced length of stay. Timely treatment of these patients also reduced the percent of patients developing DTs, and reduced intubation rates and transfers to the ICU. Further studies may be warranted at other sites to confirm the effectiveness of this STT protocol.

Corresponding author: Paul W. Huang, MD, Stamford Hospital, One Hospital Plaza, PO Box 9317, Stamford, CT 06904; [email protected].

Financial disclosures: None.

From Western Sussex Hospitals NHS Foundation Trust, Physiotherapy Department, Chichester, UK (Richard J. Holmes), and Western Sussex Hospitals NHS Foundation Trust, Department of Occupational Therapy, Chichester, UK (Sophie Stratford).

Objective: Despite the benefits of early and frequent input from a neurologist, there is wide variation in the availability of this service, especially in district general hospitals, with many patients managed on clinically inappropriate wards. The purpose of this service evaluation was to explore the impact this had on patient care.

Methods: A retrospective service evaluation was undertaken at a National Health Service hospital by reviewing patient records over a 6-month period. Data related to demographics, processes within the patient’s care, and secondary complications were recorded. Findings were compared with those of stroke patients managed on a specialist stroke ward.

Results: A total of 63 patients were identified, with a mean age of 72 years. The mean length of stay was 25.9 days, with a readmission rate of 16.7%. Only 15.9% of patients were reviewed by a neurologist. There was a high rate of secondary complications, with a number of patients experiencing falls (11.1%), pressure ulcers (14.3%), and health care–acquired infections (33.3%) during their admission.

Conclusions: The lack of specialist input from a neurologist and the management of patients on clinically inappropriate wards may have negatively impacted length of stay, readmission rates, and the frequency of secondary complications.

Keywords: evaluation; clinical safety; neurology; patient-centered care; clinical outcomes; length of stay.

It is estimated that 10% of acute admissions to district general hospitals (DGHs) of the National Health Service (NHS) in the United Kingdom are due to a neurological problem other than stroke.¹ In 2011, a joint report from the Royal College of Physicians and the Association of British Neurologists (ABN) recommended that all of these patients should be admitted under the care of a neurologist and be regularly reviewed by a neurologist during their admission.² The rationale for this recommendation is clear. The involvement of a neurologist has been shown to improve accuracy of the diagnosis³ and significantly reduce length of stay.^4,5 Studies have also shown that the involvement of a neurologist has led to a change in the management plan in as high as 79%⁶ to 89%³ of cases, suggesting that a high proportion of neurological patients not seen by a neurologist are being managed suboptimally.

Despite this, a recent ABN survey of acute neurology services found ongoing wide variations in the availability of this specialist care, with a large proportion of DGHs having limited or no access to a neurologist and very few having dedicated neurology beds.⁷ While it is recognized that services have been structured in response to the reduced numbers of neurologists within the United Kingdom,⁸ it is prudent to assess the impact that such services have on patient care.

With this in mind, we planned to evaluate the current provision of care provided to neurological patients in a real-world setting. This was conducted in the context of a neurology liaison service at a DGH with no dedicated neurology beds.

Methods

A retrospective service evaluation was undertaken at a DGH in the southeast of England. The NHS hospital has neurologists on site who provide diagnostic and therapeutic consultations on the wards, but there are no dedicated beds for patients with neurological conditions. Patients requiring neurosurgical input are referred to a tertiary neurosciences center.

Patients were selected from the neurotherapy database if they were referred into the service between August 1, 2019, and January 31, 2020. The neurotherapy database was used as this was the only source that held thorough data on this patient group and allowed for the identification of patients who were not referred into the neurologist’s service. Patients were included if they had a new neurological condition as their primary diagnosis or if they had an exacerbation of an already established neurological condition. If a patient was admitted with more than 1 neurological diagnosis then the primary diagnosis for the admission was to be used in the analysis, though this did not occur during this evaluation. Patients with a primary diagnosis of a stroke were included if they were not managed on the acute stroke ward. Those managed on the stroke ward were excluded so that an analysis of patients managed on wards that were deemed clinically inappropriate could be undertaken. Patients were not included if they had a pre-existing neurological condition (ie, dementia, multiple sclerosis) but were admitted due to a non-neurological cause such as a fall or infection. All patients who met the criteria were included.

A team member independently reviewed each set of patient notes. Demographic data extracted from the medical notes included the patient’s age (on admission), gender, and diagnosis. Medical, nursing, and therapy notes were reviewed to identify secondary complications that arose during the patient’s admission. The secondary complications reviewed were falls (defined as the patient unexpectedly coming to the ground or other lower level), health care–acquired infections (HAIs) (defined as any infection acquired during the hospital admission), and pressure ulcers (defined as injuries to the skin or underlying tissue during the hospital admission). Other details, obtained from the patient administration system, included the length of stay (days), the number of ward moves the patient experienced, the speciality of the consultant responsible for the patient’s care, the discharge destination, and whether the patient was readmitted for any cause within 30 days. All data collected were stored on a password-protected computer and no patient-identifiable data were included.

The results were collated using descriptive statistics. The χ² test was used to compare categorical data between those patients who were and were not reviewed by a neurologist, and the Mann-Whitney U test was used to compare differences in the length of stay between these 2 groups.

No national data relating to this specific patient group were available within the literature. Therefore, to provide a comparator of neurological patients within the same hospital, data were collected on stroke patients managed on the stroke ward. This group was deemed most appropriate for comparison as they present with similar neurological symptoms but are cared for on a specialist ward. During the evaluation period, 284 stroke patients were admitted to the stroke ward. A sample of 75 patients was randomly selected using a random number generator, and the procedure for data collection was repeated. It was not appropriate to make direct comparative analysis on these 2 groups due to the inherent differences, but it was felt important to provide context with regards to what usual care was like on a specialist ward within the same hospital.

Ethical approval was not required as this was a service evaluation of routinely collected data within a single hospital site.

Results

In total, 63 patients were identified: 26 females and 37 males. The median age of patients was 74 years (range, 39-92 years). These demographic details and comparisons to stroke patients managed on a specialist ward can be seen in Table 1. To quantify the range of diagnoses, the condition groups defined by GIRFT Neurology Methodology⁹ were used. The most common diagnoses were tumors of the nervous system (25.4%) and traumatic brain and spine injury (23.8%). The other conditions included in the analysis can be seen in Table 2.

Despite having a neurological condition as their primary diagnosis, only 15.9% of patients were reviewed by a neurologist during their hospital admission. Patients were most commonly under the care of a geriatrician (60.3%), but they were also managed by orthopedics (12.6%), acute medicine (7.9%), respiratory (6.3%), cardiology (4.8%), gastroenterology (3.2%), and surgery (3.2%). One patient (1.6%) was managed by intensivists.

The average length of stay was 25.9 days (range, 2-78 days). This was more than double the average length of stay on the stroke ward (11.4 days) (Table 1) and the national average for patients with neurological conditions (9.78 days).¹⁰ During their stay, 33% had 2 or more ward moves, with 1 patient moving wards a total of 6 times. Just over half (52.4%) of the patients returned to their usual residence on discharge. The remainder were discharged to rehabilitation units (15.9%), nursing homes (14.3%), residential homes (6.3%), tertiary centers (4.8%), and hospice (1.6%). Unfortunately, 3 patients (4.8%) passed away. Of those still alive (n = 60), 16.7% were readmitted to the hospital within 30 days, compared to a readmission rate of 11% on the stroke ward. None of the patients who were readmitted were seen by a neurologist during their initial admission.

The frequency of secondary complications was reviewed as a measure of the multidisciplinary management of this patient group. It was noted that 11.1% had a fall on the ward, which was similar to a rate of 10.7% on the stroke ward. More striking was the fact that 14.3% of patients developed a pressure ulcer and 33.3% developed an HAI during their admission, compared with rates of 1.3% and 10.7%, respectively, on the stroke ward (Table 1).

There were no significant differences found in length of stay between those who were and were not reviewed by a neurologist (P = .73). This was also true for categorical data, whereby readmission rate (P = .13), frequency of falls (P = .22), frequency of pressure ulcers (P = .67), and HAIs (P = .81) all failed to show a significant difference between groups.

Discussion

The findings of this service evaluation show markedly poorer outcomes for neurological patients compared to stroke patients managed on a specialist stroke ward. It is suggested that these results are in part due to the lack of specialist input from a neurologist in the majority of cases and the fact that all were managed on clinically inappropriate wards. Only 15.9% of neurological patients were seen by a neurologist. This is a slight improvement compared to previous studies in DGHs that showed rates of 10%¹ and 11%,¹¹ but it is still a far cry from the goal of 100% set out in recommendations.² In addition, the increased readmission rate may be suggestive of suboptimal management, especially given that none of those readmitted had been reviewed by a neurologist. There are undoubtedly other factors that may influence readmissions, such as comorbidities, the severity/complexity of the condition, and the strength of community services. However, the impact of a lack of input from a specialist should not be underestimated, and further evaluation of this factor (with confounding factors controlled) would be beneficial.

The result of an extended length of stay was also a predictable outcome based on previous evidence.^4,5 With the potential for suboptimal management plans and inaccurate diagnoses, it is inevitable that the patient’s movement through the hospital system will be impeded. In our example, it is possible that the extended length of stay was influenced by the fact that patients included in the evaluation were managed on nonspecialist wards and a large proportion had multiple ward changes.

Given that the evidence clearly shows that stroke patients are most effectively managed by a multidisciplinary team (MDT) with specialist skills,¹² it is likely that other neurological patients, who have similar multifactorial needs, would also benefit. The patients in our evaluation were cared for by nursing staff who lacked specific skills and experience in neurology. The allied health professionals involved were specialists in neurotherapy but were not based on the ward and not directly linked to the ward MDT. A review by Epstein found that the benefits of having a MDT, in any speciality, working together on a ward included improved communication, reduced adverse events, and a reduced length of stay.¹³ This lack of an effective MDT approach may provide some explanation as to why the average length of stay and the rates of some secondary complications were at such elevated levels.

A systematic review exploring the impact of patients admitted to clinically inappropriate wards in a range of specialities found that these patients were associated with worse outcomes.¹⁴ This is supported by our findings, in which a higher rate of pressure ulcers and HAIs were observed when compared to rates in the specialist stroke ward. Again, a potential explanation for this is the impact of patients being managed by clinicians who lack the specialist knowledge of the patient group and the risks they face. Another explanation could be due to the high number of ward moves the patients experienced. Blay et al found that ward moves increased length of stay and carried an associated clinical risk, with the odds of falls and HAIs increasing with each move.¹⁵ A case example of this is apparent within our analysis in that the patient who experienced 6 ward moves not only had the longest length of stay (78 days), but also developed a pressure ulcer and 2 HAIs during their admission.

This service evaluation had a number of limitations that should be considered when interpreting the results. First, despite including all patients who met the criteria within the stipulated time frame, the sample size was relatively small, making it difficult to identify consistent patterns of behavior within the data.

Furthermore, caution should be applied when interpreting the comparators used, as the patient groups are not equivalent. The use of comparison against a standard is not a prerequisite in a service evaluation of this nature, but comparators were included to help frame the context for the reader. As such, they should only be used in this way rather than to make any firm conclusions.

Finally, as the evaluation was limited to the use of routinely collected data, there are several variables, other than those reported, which may have influenced the results. For example, it was not possible to ascertain certain demographic details, such as body mass index and socioeconomic factors, nor lifestyle factors such as smoking status, alcohol consumption, and exercise levels, all of which could impact negatively on the outcomes of interest. Furthermore, data were not collected on follow-up services after discharge to evaluate whether these had any impact on readmission rates.

Conclusion

This service evaluation highlights the potential impact of managing neurological patients on clinically inappropriate wards with limited input from a neurologist. There is the potential to ameliorate these impacts by cohorting these patients in neurologist-led beds with a specialist MDT. While there are limitations in the design of our study, including the lack of a controlled comparison, the small sample size, and the fact that this is an evaluation of a single service, the negative impacts to patients are concerning and warrant further investigation.

Corresponding author: Richard J. Holmes, MSc, Physiotherapy Department, St. Richard’s Hospital, Chichester, West Sussex, PO19 6SE; [email protected].

Financial disclosures: None.

From Western Sussex Hospitals NHS Foundation Trust, Physiotherapy Department, Chichester, UK (Richard J. Holmes), and Western Sussex Hospitals NHS Foundation Trust, Department of Occupational Therapy, Chichester, UK (Sophie Stratford).

Objective: Despite the benefits of early and frequent input from a neurologist, there is wide variation in the availability of this service, especially in district general hospitals, with many patients managed on clinically inappropriate wards. The purpose of this service evaluation was to explore the impact this had on patient care.

Methods: A retrospective service evaluation was undertaken at a National Health Service hospital by reviewing patient records over a 6-month period. Data related to demographics, processes within the patient’s care, and secondary complications were recorded. Findings were compared with those of stroke patients managed on a specialist stroke ward.

Results: A total of 63 patients were identified, with a mean age of 72 years. The mean length of stay was 25.9 days, with a readmission rate of 16.7%. Only 15.9% of patients were reviewed by a neurologist. There was a high rate of secondary complications, with a number of patients experiencing falls (11.1%), pressure ulcers (14.3%), and health care–acquired infections (33.3%) during their admission.

Conclusions: The lack of specialist input from a neurologist and the management of patients on clinically inappropriate wards may have negatively impacted length of stay, readmission rates, and the frequency of secondary complications.

Keywords: evaluation; clinical safety; neurology; patient-centered care; clinical outcomes; length of stay.

It is estimated that 10% of acute admissions to district general hospitals (DGHs) of the National Health Service (NHS) in the United Kingdom are due to a neurological problem other than stroke.¹ In 2011, a joint report from the Royal College of Physicians and the Association of British Neurologists (ABN) recommended that all of these patients should be admitted under the care of a neurologist and be regularly reviewed by a neurologist during their admission.² The rationale for this recommendation is clear. The involvement of a neurologist has been shown to improve accuracy of the diagnosis³ and significantly reduce length of stay.^4,5 Studies have also shown that the involvement of a neurologist has led to a change in the management plan in as high as 79%⁶ to 89%³ of cases, suggesting that a high proportion of neurological patients not seen by a neurologist are being managed suboptimally.

Despite this, a recent ABN survey of acute neurology services found ongoing wide variations in the availability of this specialist care, with a large proportion of DGHs having limited or no access to a neurologist and very few having dedicated neurology beds.⁷ While it is recognized that services have been structured in response to the reduced numbers of neurologists within the United Kingdom,⁸ it is prudent to assess the impact that such services have on patient care.

With this in mind, we planned to evaluate the current provision of care provided to neurological patients in a real-world setting. This was conducted in the context of a neurology liaison service at a DGH with no dedicated neurology beds.

Methods

A retrospective service evaluation was undertaken at a DGH in the southeast of England. The NHS hospital has neurologists on site who provide diagnostic and therapeutic consultations on the wards, but there are no dedicated beds for patients with neurological conditions. Patients requiring neurosurgical input are referred to a tertiary neurosciences center.

Patients were selected from the neurotherapy database if they were referred into the service between August 1, 2019, and January 31, 2020. The neurotherapy database was used as this was the only source that held thorough data on this patient group and allowed for the identification of patients who were not referred into the neurologist’s service. Patients were included if they had a new neurological condition as their primary diagnosis or if they had an exacerbation of an already established neurological condition. If a patient was admitted with more than 1 neurological diagnosis then the primary diagnosis for the admission was to be used in the analysis, though this did not occur during this evaluation. Patients with a primary diagnosis of a stroke were included if they were not managed on the acute stroke ward. Those managed on the stroke ward were excluded so that an analysis of patients managed on wards that were deemed clinically inappropriate could be undertaken. Patients were not included if they had a pre-existing neurological condition (ie, dementia, multiple sclerosis) but were admitted due to a non-neurological cause such as a fall or infection. All patients who met the criteria were included.

A team member independently reviewed each set of patient notes. Demographic data extracted from the medical notes included the patient’s age (on admission), gender, and diagnosis. Medical, nursing, and therapy notes were reviewed to identify secondary complications that arose during the patient’s admission. The secondary complications reviewed were falls (defined as the patient unexpectedly coming to the ground or other lower level), health care–acquired infections (HAIs) (defined as any infection acquired during the hospital admission), and pressure ulcers (defined as injuries to the skin or underlying tissue during the hospital admission). Other details, obtained from the patient administration system, included the length of stay (days), the number of ward moves the patient experienced, the speciality of the consultant responsible for the patient’s care, the discharge destination, and whether the patient was readmitted for any cause within 30 days. All data collected were stored on a password-protected computer and no patient-identifiable data were included.

The results were collated using descriptive statistics. The χ² test was used to compare categorical data between those patients who were and were not reviewed by a neurologist, and the Mann-Whitney U test was used to compare differences in the length of stay between these 2 groups.

No national data relating to this specific patient group were available within the literature. Therefore, to provide a comparator of neurological patients within the same hospital, data were collected on stroke patients managed on the stroke ward. This group was deemed most appropriate for comparison as they present with similar neurological symptoms but are cared for on a specialist ward. During the evaluation period, 284 stroke patients were admitted to the stroke ward. A sample of 75 patients was randomly selected using a random number generator, and the procedure for data collection was repeated. It was not appropriate to make direct comparative analysis on these 2 groups due to the inherent differences, but it was felt important to provide context with regards to what usual care was like on a specialist ward within the same hospital.

Ethical approval was not required as this was a service evaluation of routinely collected data within a single hospital site.

Results

In total, 63 patients were identified: 26 females and 37 males. The median age of patients was 74 years (range, 39-92 years). These demographic details and comparisons to stroke patients managed on a specialist ward can be seen in Table 1. To quantify the range of diagnoses, the condition groups defined by GIRFT Neurology Methodology⁹ were used. The most common diagnoses were tumors of the nervous system (25.4%) and traumatic brain and spine injury (23.8%). The other conditions included in the analysis can be seen in Table 2.

Despite having a neurological condition as their primary diagnosis, only 15.9% of patients were reviewed by a neurologist during their hospital admission. Patients were most commonly under the care of a geriatrician (60.3%), but they were also managed by orthopedics (12.6%), acute medicine (7.9%), respiratory (6.3%), cardiology (4.8%), gastroenterology (3.2%), and surgery (3.2%). One patient (1.6%) was managed by intensivists.

The average length of stay was 25.9 days (range, 2-78 days). This was more than double the average length of stay on the stroke ward (11.4 days) (Table 1) and the national average for patients with neurological conditions (9.78 days).¹⁰ During their stay, 33% had 2 or more ward moves, with 1 patient moving wards a total of 6 times. Just over half (52.4%) of the patients returned to their usual residence on discharge. The remainder were discharged to rehabilitation units (15.9%), nursing homes (14.3%), residential homes (6.3%), tertiary centers (4.8%), and hospice (1.6%). Unfortunately, 3 patients (4.8%) passed away. Of those still alive (n = 60), 16.7% were readmitted to the hospital within 30 days, compared to a readmission rate of 11% on the stroke ward. None of the patients who were readmitted were seen by a neurologist during their initial admission.

The frequency of secondary complications was reviewed as a measure of the multidisciplinary management of this patient group. It was noted that 11.1% had a fall on the ward, which was similar to a rate of 10.7% on the stroke ward. More striking was the fact that 14.3% of patients developed a pressure ulcer and 33.3% developed an HAI during their admission, compared with rates of 1.3% and 10.7%, respectively, on the stroke ward (Table 1).

There were no significant differences found in length of stay between those who were and were not reviewed by a neurologist (P = .73). This was also true for categorical data, whereby readmission rate (P = .13), frequency of falls (P = .22), frequency of pressure ulcers (P = .67), and HAIs (P = .81) all failed to show a significant difference between groups.

Discussion

The findings of this service evaluation show markedly poorer outcomes for neurological patients compared to stroke patients managed on a specialist stroke ward. It is suggested that these results are in part due to the lack of specialist input from a neurologist in the majority of cases and the fact that all were managed on clinically inappropriate wards. Only 15.9% of neurological patients were seen by a neurologist. This is a slight improvement compared to previous studies in DGHs that showed rates of 10%¹ and 11%,¹¹ but it is still a far cry from the goal of 100% set out in recommendations.² In addition, the increased readmission rate may be suggestive of suboptimal management, especially given that none of those readmitted had been reviewed by a neurologist. There are undoubtedly other factors that may influence readmissions, such as comorbidities, the severity/complexity of the condition, and the strength of community services. However, the impact of a lack of input from a specialist should not be underestimated, and further evaluation of this factor (with confounding factors controlled) would be beneficial.

The result of an extended length of stay was also a predictable outcome based on previous evidence.^4,5 With the potential for suboptimal management plans and inaccurate diagnoses, it is inevitable that the patient’s movement through the hospital system will be impeded. In our example, it is possible that the extended length of stay was influenced by the fact that patients included in the evaluation were managed on nonspecialist wards and a large proportion had multiple ward changes.

Given that the evidence clearly shows that stroke patients are most effectively managed by a multidisciplinary team (MDT) with specialist skills,¹² it is likely that other neurological patients, who have similar multifactorial needs, would also benefit. The patients in our evaluation were cared for by nursing staff who lacked specific skills and experience in neurology. The allied health professionals involved were specialists in neurotherapy but were not based on the ward and not directly linked to the ward MDT. A review by Epstein found that the benefits of having a MDT, in any speciality, working together on a ward included improved communication, reduced adverse events, and a reduced length of stay.¹³ This lack of an effective MDT approach may provide some explanation as to why the average length of stay and the rates of some secondary complications were at such elevated levels.

A systematic review exploring the impact of patients admitted to clinically inappropriate wards in a range of specialities found that these patients were associated with worse outcomes.¹⁴ This is supported by our findings, in which a higher rate of pressure ulcers and HAIs were observed when compared to rates in the specialist stroke ward. Again, a potential explanation for this is the impact of patients being managed by clinicians who lack the specialist knowledge of the patient group and the risks they face. Another explanation could be due to the high number of ward moves the patients experienced. Blay et al found that ward moves increased length of stay and carried an associated clinical risk, with the odds of falls and HAIs increasing with each move.¹⁵ A case example of this is apparent within our analysis in that the patient who experienced 6 ward moves not only had the longest length of stay (78 days), but also developed a pressure ulcer and 2 HAIs during their admission.

This service evaluation had a number of limitations that should be considered when interpreting the results. First, despite including all patients who met the criteria within the stipulated time frame, the sample size was relatively small, making it difficult to identify consistent patterns of behavior within the data.

Furthermore, caution should be applied when interpreting the comparators used, as the patient groups are not equivalent. The use of comparison against a standard is not a prerequisite in a service evaluation of this nature, but comparators were included to help frame the context for the reader. As such, they should only be used in this way rather than to make any firm conclusions.

Finally, as the evaluation was limited to the use of routinely collected data, there are several variables, other than those reported, which may have influenced the results. For example, it was not possible to ascertain certain demographic details, such as body mass index and socioeconomic factors, nor lifestyle factors such as smoking status, alcohol consumption, and exercise levels, all of which could impact negatively on the outcomes of interest. Furthermore, data were not collected on follow-up services after discharge to evaluate whether these had any impact on readmission rates.

Conclusion

This service evaluation highlights the potential impact of managing neurological patients on clinically inappropriate wards with limited input from a neurologist. There is the potential to ameliorate these impacts by cohorting these patients in neurologist-led beds with a specialist MDT. While there are limitations in the design of our study, including the lack of a controlled comparison, the small sample size, and the fact that this is an evaluation of a single service, the negative impacts to patients are concerning and warrant further investigation.

Corresponding author: Richard J. Holmes, MSc, Physiotherapy Department, St. Richard’s Hospital, Chichester, West Sussex, PO19 6SE; [email protected].

Financial disclosures: None.

From Western Sussex Hospitals NHS Foundation Trust, Physiotherapy Department, Chichester, UK (Richard J. Holmes), and Western Sussex Hospitals NHS Foundation Trust, Department of Occupational Therapy, Chichester, UK (Sophie Stratford).

Objective: Despite the benefits of early and frequent input from a neurologist, there is wide variation in the availability of this service, especially in district general hospitals, with many patients managed on clinically inappropriate wards. The purpose of this service evaluation was to explore the impact this had on patient care.

Methods: A retrospective service evaluation was undertaken at a National Health Service hospital by reviewing patient records over a 6-month period. Data related to demographics, processes within the patient’s care, and secondary complications were recorded. Findings were compared with those of stroke patients managed on a specialist stroke ward.

Results: A total of 63 patients were identified, with a mean age of 72 years. The mean length of stay was 25.9 days, with a readmission rate of 16.7%. Only 15.9% of patients were reviewed by a neurologist. There was a high rate of secondary complications, with a number of patients experiencing falls (11.1%), pressure ulcers (14.3%), and health care–acquired infections (33.3%) during their admission.

Conclusions: The lack of specialist input from a neurologist and the management of patients on clinically inappropriate wards may have negatively impacted length of stay, readmission rates, and the frequency of secondary complications.

Keywords: evaluation; clinical safety; neurology; patient-centered care; clinical outcomes; length of stay.

It is estimated that 10% of acute admissions to district general hospitals (DGHs) of the National Health Service (NHS) in the United Kingdom are due to a neurological problem other than stroke.¹ In 2011, a joint report from the Royal College of Physicians and the Association of British Neurologists (ABN) recommended that all of these patients should be admitted under the care of a neurologist and be regularly reviewed by a neurologist during their admission.² The rationale for this recommendation is clear. The involvement of a neurologist has been shown to improve accuracy of the diagnosis³ and significantly reduce length of stay.^4,5 Studies have also shown that the involvement of a neurologist has led to a change in the management plan in as high as 79%⁶ to 89%³ of cases, suggesting that a high proportion of neurological patients not seen by a neurologist are being managed suboptimally.

Despite this, a recent ABN survey of acute neurology services found ongoing wide variations in the availability of this specialist care, with a large proportion of DGHs having limited or no access to a neurologist and very few having dedicated neurology beds.⁷ While it is recognized that services have been structured in response to the reduced numbers of neurologists within the United Kingdom,⁸ it is prudent to assess the impact that such services have on patient care.

With this in mind, we planned to evaluate the current provision of care provided to neurological patients in a real-world setting. This was conducted in the context of a neurology liaison service at a DGH with no dedicated neurology beds.

Methods

A retrospective service evaluation was undertaken at a DGH in the southeast of England. The NHS hospital has neurologists on site who provide diagnostic and therapeutic consultations on the wards, but there are no dedicated beds for patients with neurological conditions. Patients requiring neurosurgical input are referred to a tertiary neurosciences center.

Patients were selected from the neurotherapy database if they were referred into the service between August 1, 2019, and January 31, 2020. The neurotherapy database was used as this was the only source that held thorough data on this patient group and allowed for the identification of patients who were not referred into the neurologist’s service. Patients were included if they had a new neurological condition as their primary diagnosis or if they had an exacerbation of an already established neurological condition. If a patient was admitted with more than 1 neurological diagnosis then the primary diagnosis for the admission was to be used in the analysis, though this did not occur during this evaluation. Patients with a primary diagnosis of a stroke were included if they were not managed on the acute stroke ward. Those managed on the stroke ward were excluded so that an analysis of patients managed on wards that were deemed clinically inappropriate could be undertaken. Patients were not included if they had a pre-existing neurological condition (ie, dementia, multiple sclerosis) but were admitted due to a non-neurological cause such as a fall or infection. All patients who met the criteria were included.

A team member independently reviewed each set of patient notes. Demographic data extracted from the medical notes included the patient’s age (on admission), gender, and diagnosis. Medical, nursing, and therapy notes were reviewed to identify secondary complications that arose during the patient’s admission. The secondary complications reviewed were falls (defined as the patient unexpectedly coming to the ground or other lower level), health care–acquired infections (HAIs) (defined as any infection acquired during the hospital admission), and pressure ulcers (defined as injuries to the skin or underlying tissue during the hospital admission). Other details, obtained from the patient administration system, included the length of stay (days), the number of ward moves the patient experienced, the speciality of the consultant responsible for the patient’s care, the discharge destination, and whether the patient was readmitted for any cause within 30 days. All data collected were stored on a password-protected computer and no patient-identifiable data were included.

The results were collated using descriptive statistics. The χ² test was used to compare categorical data between those patients who were and were not reviewed by a neurologist, and the Mann-Whitney U test was used to compare differences in the length of stay between these 2 groups.

No national data relating to this specific patient group were available within the literature. Therefore, to provide a comparator of neurological patients within the same hospital, data were collected on stroke patients managed on the stroke ward. This group was deemed most appropriate for comparison as they present with similar neurological symptoms but are cared for on a specialist ward. During the evaluation period, 284 stroke patients were admitted to the stroke ward. A sample of 75 patients was randomly selected using a random number generator, and the procedure for data collection was repeated. It was not appropriate to make direct comparative analysis on these 2 groups due to the inherent differences, but it was felt important to provide context with regards to what usual care was like on a specialist ward within the same hospital.

Ethical approval was not required as this was a service evaluation of routinely collected data within a single hospital site.

Results

In total, 63 patients were identified: 26 females and 37 males. The median age of patients was 74 years (range, 39-92 years). These demographic details and comparisons to stroke patients managed on a specialist ward can be seen in Table 1. To quantify the range of diagnoses, the condition groups defined by GIRFT Neurology Methodology⁹ were used. The most common diagnoses were tumors of the nervous system (25.4%) and traumatic brain and spine injury (23.8%). The other conditions included in the analysis can be seen in Table 2.

Despite having a neurological condition as their primary diagnosis, only 15.9% of patients were reviewed by a neurologist during their hospital admission. Patients were most commonly under the care of a geriatrician (60.3%), but they were also managed by orthopedics (12.6%), acute medicine (7.9%), respiratory (6.3%), cardiology (4.8%), gastroenterology (3.2%), and surgery (3.2%). One patient (1.6%) was managed by intensivists.

The average length of stay was 25.9 days (range, 2-78 days). This was more than double the average length of stay on the stroke ward (11.4 days) (Table 1) and the national average for patients with neurological conditions (9.78 days).¹⁰ During their stay, 33% had 2 or more ward moves, with 1 patient moving wards a total of 6 times. Just over half (52.4%) of the patients returned to their usual residence on discharge. The remainder were discharged to rehabilitation units (15.9%), nursing homes (14.3%), residential homes (6.3%), tertiary centers (4.8%), and hospice (1.6%). Unfortunately, 3 patients (4.8%) passed away. Of those still alive (n = 60), 16.7% were readmitted to the hospital within 30 days, compared to a readmission rate of 11% on the stroke ward. None of the patients who were readmitted were seen by a neurologist during their initial admission.

The frequency of secondary complications was reviewed as a measure of the multidisciplinary management of this patient group. It was noted that 11.1% had a fall on the ward, which was similar to a rate of 10.7% on the stroke ward. More striking was the fact that 14.3% of patients developed a pressure ulcer and 33.3% developed an HAI during their admission, compared with rates of 1.3% and 10.7%, respectively, on the stroke ward (Table 1).

There were no significant differences found in length of stay between those who were and were not reviewed by a neurologist (P = .73). This was also true for categorical data, whereby readmission rate (P = .13), frequency of falls (P = .22), frequency of pressure ulcers (P = .67), and HAIs (P = .81) all failed to show a significant difference between groups.

Discussion

The findings of this service evaluation show markedly poorer outcomes for neurological patients compared to stroke patients managed on a specialist stroke ward. It is suggested that these results are in part due to the lack of specialist input from a neurologist in the majority of cases and the fact that all were managed on clinically inappropriate wards. Only 15.9% of neurological patients were seen by a neurologist. This is a slight improvement compared to previous studies in DGHs that showed rates of 10%¹ and 11%,¹¹ but it is still a far cry from the goal of 100% set out in recommendations.² In addition, the increased readmission rate may be suggestive of suboptimal management, especially given that none of those readmitted had been reviewed by a neurologist. There are undoubtedly other factors that may influence readmissions, such as comorbidities, the severity/complexity of the condition, and the strength of community services. However, the impact of a lack of input from a specialist should not be underestimated, and further evaluation of this factor (with confounding factors controlled) would be beneficial.

The result of an extended length of stay was also a predictable outcome based on previous evidence.^4,5 With the potential for suboptimal management plans and inaccurate diagnoses, it is inevitable that the patient’s movement through the hospital system will be impeded. In our example, it is possible that the extended length of stay was influenced by the fact that patients included in the evaluation were managed on nonspecialist wards and a large proportion had multiple ward changes.

Given that the evidence clearly shows that stroke patients are most effectively managed by a multidisciplinary team (MDT) with specialist skills,¹² it is likely that other neurological patients, who have similar multifactorial needs, would also benefit. The patients in our evaluation were cared for by nursing staff who lacked specific skills and experience in neurology. The allied health professionals involved were specialists in neurotherapy but were not based on the ward and not directly linked to the ward MDT. A review by Epstein found that the benefits of having a MDT, in any speciality, working together on a ward included improved communication, reduced adverse events, and a reduced length of stay.¹³ This lack of an effective MDT approach may provide some explanation as to why the average length of stay and the rates of some secondary complications were at such elevated levels.

A systematic review exploring the impact of patients admitted to clinically inappropriate wards in a range of specialities found that these patients were associated with worse outcomes.¹⁴ This is supported by our findings, in which a higher rate of pressure ulcers and HAIs were observed when compared to rates in the specialist stroke ward. Again, a potential explanation for this is the impact of patients being managed by clinicians who lack the specialist knowledge of the patient group and the risks they face. Another explanation could be due to the high number of ward moves the patients experienced. Blay et al found that ward moves increased length of stay and carried an associated clinical risk, with the odds of falls and HAIs increasing with each move.¹⁵ A case example of this is apparent within our analysis in that the patient who experienced 6 ward moves not only had the longest length of stay (78 days), but also developed a pressure ulcer and 2 HAIs during their admission.

This service evaluation had a number of limitations that should be considered when interpreting the results. First, despite including all patients who met the criteria within the stipulated time frame, the sample size was relatively small, making it difficult to identify consistent patterns of behavior within the data.

Furthermore, caution should be applied when interpreting the comparators used, as the patient groups are not equivalent. The use of comparison against a standard is not a prerequisite in a service evaluation of this nature, but comparators were included to help frame the context for the reader. As such, they should only be used in this way rather than to make any firm conclusions.

Finally, as the evaluation was limited to the use of routinely collected data, there are several variables, other than those reported, which may have influenced the results. For example, it was not possible to ascertain certain demographic details, such as body mass index and socioeconomic factors, nor lifestyle factors such as smoking status, alcohol consumption, and exercise levels, all of which could impact negatively on the outcomes of interest. Furthermore, data were not collected on follow-up services after discharge to evaluate whether these had any impact on readmission rates.

Conclusion

This service evaluation highlights the potential impact of managing neurological patients on clinically inappropriate wards with limited input from a neurologist. There is the potential to ameliorate these impacts by cohorting these patients in neurologist-led beds with a specialist MDT. While there are limitations in the design of our study, including the lack of a controlled comparison, the small sample size, and the fact that this is an evaluation of a single service, the negative impacts to patients are concerning and warrant further investigation.

Corresponding author: Richard J. Holmes, MSc, Physiotherapy Department, St. Richard’s Hospital, Chichester, West Sussex, PO19 6SE; [email protected].

Financial disclosures: None.

Targeting “discharge before noon” (DBN) for hospitalized patients has been proposed as a way to improve hospital throughput and patient safety by reducing emergency department (ED) boarding and crowding. In this issue, Kirubarajan et al¹ report no association between morning discharge and length of stay (LOS) for either the ED or hospitalization.¹ This large (189,781 patients) 7-year study from seven quite different Canadian hospitals adds important data to a literature that remains divided about whether DBN helps or hurts hospital LOS and ED boarding.

Unlike trials reporting interventions to encourage DBN, this observational study was unique in that it took each day as the unit of observation. This method cleverly allowed the authors to examine whether days with more discharges before noon conferred a lower mean ED and inpatient LOS among patients admitted on those days. Their approach appropriately reframes the central issue as one of patient flow.

Kirubarajan et al’s most notable, and perhaps surprising, finding is the lack of association between morning discharge and ED LOS. Computer modeling supports the hypothesis that ED throughput will improve on days with earlier inpatient bed availability.² Several studies have also noted earlier ED departure times and decreased ED wait times after implementing interventions to promote DBN.³ Why might the authors’ findings contradict previous studies? Their outcomes may in part be due to high ED LOS (>14 hours), exceeding Canadian published targets and reports from the United States.^4,5 Problems relating to ED resources, practice, and hospital census may have overwhelmed DBN as factors in boarding. The interpretation of their findings is limited by the authors’ decision to report only ED LOS, rather than including the time between a decision to admit and ED departure (boarding time).

While early studies that focused on interventions to promote DBN noted decreased inpatient LOS after their implementation, later studies found no effect or even an increase in LOS for general internal medicine patients. Concerns have been raised about the confounding effect of concurrent initiatives aimed at improving LOS as well as misaligned incentives to delay discharge to the following morning. As the number of conflicting studies mounts, and with the current report in hand, it is tempting to conclude that for the DBN evidence base as a whole, we are observing random variation around no effect.

With growing doubt about benefits of morning discharge, perhaps we should turn our attention away from the question of how to increase DBN and consider instead why and at what cost. Hospitals are delicate organisms; a singular focus on one metric will undoubtedly impact others. Does the effort to discharge before noon consume valuable morning hours and detract from the care of other patients? Are patients held overnight unnecessarily to comply with DBN? Are there consequences in patient, nursing, or trainee satisfaction? Is bedside teaching affected?

And as concepts of patient-centered care are increasingly valued, we may ask whether DBN is such a concept, or is it rather an increasingly dubious strategy aimed at regularizing hospital operations? The need for a more holistic assessment of “discharge quality” is apparent. Instead of focusing on a particular hour, initiatives should determine the “best, earliest discharge time” for each patient and align multidisciplinary efforts toward this patient-centered goal. Such efforts are already underway in pediatric hospitals, where fixed discharge times are being replaced by discharge milestones embedded into the electronic medical record.⁶ An instrument to track “discharge readiness” such as this one, paired with ongoing analysis of the barriers to timely discharge, might better facilitate throughput by targeting the entire admission, rather than concentrating pressure on its final hours.

Targeting “discharge before noon” (DBN) for hospitalized patients has been proposed as a way to improve hospital throughput and patient safety by reducing emergency department (ED) boarding and crowding. In this issue, Kirubarajan et al¹ report no association between morning discharge and length of stay (LOS) for either the ED or hospitalization.¹ This large (189,781 patients) 7-year study from seven quite different Canadian hospitals adds important data to a literature that remains divided about whether DBN helps or hurts hospital LOS and ED boarding.

Unlike trials reporting interventions to encourage DBN, this observational study was unique in that it took each day as the unit of observation. This method cleverly allowed the authors to examine whether days with more discharges before noon conferred a lower mean ED and inpatient LOS among patients admitted on those days. Their approach appropriately reframes the central issue as one of patient flow.

Kirubarajan et al’s most notable, and perhaps surprising, finding is the lack of association between morning discharge and ED LOS. Computer modeling supports the hypothesis that ED throughput will improve on days with earlier inpatient bed availability.² Several studies have also noted earlier ED departure times and decreased ED wait times after implementing interventions to promote DBN.³ Why might the authors’ findings contradict previous studies? Their outcomes may in part be due to high ED LOS (>14 hours), exceeding Canadian published targets and reports from the United States.^4,5 Problems relating to ED resources, practice, and hospital census may have overwhelmed DBN as factors in boarding. The interpretation of their findings is limited by the authors’ decision to report only ED LOS, rather than including the time between a decision to admit and ED departure (boarding time).

While early studies that focused on interventions to promote DBN noted decreased inpatient LOS after their implementation, later studies found no effect or even an increase in LOS for general internal medicine patients. Concerns have been raised about the confounding effect of concurrent initiatives aimed at improving LOS as well as misaligned incentives to delay discharge to the following morning. As the number of conflicting studies mounts, and with the current report in hand, it is tempting to conclude that for the DBN evidence base as a whole, we are observing random variation around no effect.

With growing doubt about benefits of morning discharge, perhaps we should turn our attention away from the question of how to increase DBN and consider instead why and at what cost. Hospitals are delicate organisms; a singular focus on one metric will undoubtedly impact others. Does the effort to discharge before noon consume valuable morning hours and detract from the care of other patients? Are patients held overnight unnecessarily to comply with DBN? Are there consequences in patient, nursing, or trainee satisfaction? Is bedside teaching affected?

And as concepts of patient-centered care are increasingly valued, we may ask whether DBN is such a concept, or is it rather an increasingly dubious strategy aimed at regularizing hospital operations? The need for a more holistic assessment of “discharge quality” is apparent. Instead of focusing on a particular hour, initiatives should determine the “best, earliest discharge time” for each patient and align multidisciplinary efforts toward this patient-centered goal. Such efforts are already underway in pediatric hospitals, where fixed discharge times are being replaced by discharge milestones embedded into the electronic medical record.⁶ An instrument to track “discharge readiness” such as this one, paired with ongoing analysis of the barriers to timely discharge, might better facilitate throughput by targeting the entire admission, rather than concentrating pressure on its final hours.

Targeting “discharge before noon” (DBN) for hospitalized patients has been proposed as a way to improve hospital throughput and patient safety by reducing emergency department (ED) boarding and crowding. In this issue, Kirubarajan et al¹ report no association between morning discharge and length of stay (LOS) for either the ED or hospitalization.¹ This large (189,781 patients) 7-year study from seven quite different Canadian hospitals adds important data to a literature that remains divided about whether DBN helps or hurts hospital LOS and ED boarding.

Unlike trials reporting interventions to encourage DBN, this observational study was unique in that it took each day as the unit of observation. This method cleverly allowed the authors to examine whether days with more discharges before noon conferred a lower mean ED and inpatient LOS among patients admitted on those days. Their approach appropriately reframes the central issue as one of patient flow.

Kirubarajan et al’s most notable, and perhaps surprising, finding is the lack of association between morning discharge and ED LOS. Computer modeling supports the hypothesis that ED throughput will improve on days with earlier inpatient bed availability.² Several studies have also noted earlier ED departure times and decreased ED wait times after implementing interventions to promote DBN.³ Why might the authors’ findings contradict previous studies? Their outcomes may in part be due to high ED LOS (>14 hours), exceeding Canadian published targets and reports from the United States.^4,5 Problems relating to ED resources, practice, and hospital census may have overwhelmed DBN as factors in boarding. The interpretation of their findings is limited by the authors’ decision to report only ED LOS, rather than including the time between a decision to admit and ED departure (boarding time).

While early studies that focused on interventions to promote DBN noted decreased inpatient LOS after their implementation, later studies found no effect or even an increase in LOS for general internal medicine patients. Concerns have been raised about the confounding effect of concurrent initiatives aimed at improving LOS as well as misaligned incentives to delay discharge to the following morning. As the number of conflicting studies mounts, and with the current report in hand, it is tempting to conclude that for the DBN evidence base as a whole, we are observing random variation around no effect.

With growing doubt about benefits of morning discharge, perhaps we should turn our attention away from the question of how to increase DBN and consider instead why and at what cost. Hospitals are delicate organisms; a singular focus on one metric will undoubtedly impact others. Does the effort to discharge before noon consume valuable morning hours and detract from the care of other patients? Are patients held overnight unnecessarily to comply with DBN? Are there consequences in patient, nursing, or trainee satisfaction? Is bedside teaching affected?

And as concepts of patient-centered care are increasingly valued, we may ask whether DBN is such a concept, or is it rather an increasingly dubious strategy aimed at regularizing hospital operations? The need for a more holistic assessment of “discharge quality” is apparent. Instead of focusing on a particular hour, initiatives should determine the “best, earliest discharge time” for each patient and align multidisciplinary efforts toward this patient-centered goal. Such efforts are already underway in pediatric hospitals, where fixed discharge times are being replaced by discharge milestones embedded into the electronic medical record.⁶ An instrument to track “discharge readiness” such as this one, paired with ongoing analysis of the barriers to timely discharge, might better facilitate throughput by targeting the entire admission, rather than concentrating pressure on its final hours.

Hospital discharge is a complex, multi-stakeholder event, and evidence suggests that the quality of that transition directly relates to mortality, readmissions, and postdischarge quality of life and functional status.¹ The Centers for Medicare & Medicaid Services call for team-based and patient-centered discharge planning,² yet the process for achieving this is poorly defined.

In this issue of the Journal of Hospital Medicine, Manges et al³ use shared mental models (SMM) as a conceptual framework to describe differences in how care team members and patients perceive hospital discharge readiness. While our understanding of factors associated with safe and patient-centered hospital discharges is still growing, the authors focus on one critical component: lack of agreement between patients and interprofessional teams regarding discharge readiness.

Manges et al³ measured whether interprofessional team members agree, or converge, on their assessment of a patient’s discharge readiness (team-SMM convergence) and whether that assessment converges with the patient’s self-assessment (team-patient SMM convergence). They found good team-SMM convergence regarding the patient’s discharge readiness, yet teams overestimated readiness compared with the patient’s self-assessment nearly half (48.4%) of the time. A clinical trial found that clinician assessments of discharge readiness were poorly predictive of readmissions unless they were combined with a patient’s self-assessment.⁴ Manges et al’s study findings, while of limited generalizability, enhance our understanding of a potential gap in achieving patient-centered care as outlined in the Institute of Medicine’s Crossing the Quality Chasm,⁵ which urges clinicians to see patients and families as partners in improving care.

The authors also found that higher team-patient convergence was associated with teams that reported high-quality teamwork and those having more baccalaureate degree−educated nurses (BSN). While Manges et al³ did not elucidate the mechanism by which this occurs, their findings align with existing literature showing that patients receiving care from a higher proportion of BSN-prepared nurses experience an 18.7% reduction in odds of readmission.⁶ Further research investigating the link between team communication, registered nurse education, and discharge outcomes may reveal additional opportunities for interventions to improve discharge quality.

The lack of patient outcomes and the limited diversity of the patient population are substantial limitations of the study. The authors did not assess the relationship between SMMs and important outcomes like readmission or adverse events. Furthermore, most of the patients were White and English-speaking, precluding assessment of factors that disproportionately impact patient populations that already experience disparities in a multitude of health outcomes.

In summary, Manges et al³ highlight challenges and opportunities in optimizing clinician communication and ensuring that the team’s and the patient’s self-assessments align and inform discharge planning. Their findings suggest the theoretical framework of SMM holds promise in identifying and evaluating some of the complex determinants involved in high-quality, patient-centered hospital discharges.

Hospital discharge is a complex, multi-stakeholder event, and evidence suggests that the quality of that transition directly relates to mortality, readmissions, and postdischarge quality of life and functional status.¹ The Centers for Medicare & Medicaid Services call for team-based and patient-centered discharge planning,² yet the process for achieving this is poorly defined.

In this issue of the Journal of Hospital Medicine, Manges et al³ use shared mental models (SMM) as a conceptual framework to describe differences in how care team members and patients perceive hospital discharge readiness. While our understanding of factors associated with safe and patient-centered hospital discharges is still growing, the authors focus on one critical component: lack of agreement between patients and interprofessional teams regarding discharge readiness.

Manges et al³ measured whether interprofessional team members agree, or converge, on their assessment of a patient’s discharge readiness (team-SMM convergence) and whether that assessment converges with the patient’s self-assessment (team-patient SMM convergence). They found good team-SMM convergence regarding the patient’s discharge readiness, yet teams overestimated readiness compared with the patient’s self-assessment nearly half (48.4%) of the time. A clinical trial found that clinician assessments of discharge readiness were poorly predictive of readmissions unless they were combined with a patient’s self-assessment.⁴ Manges et al’s study findings, while of limited generalizability, enhance our understanding of a potential gap in achieving patient-centered care as outlined in the Institute of Medicine’s Crossing the Quality Chasm,⁵ which urges clinicians to see patients and families as partners in improving care.

The authors also found that higher team-patient convergence was associated with teams that reported high-quality teamwork and those having more baccalaureate degree−educated nurses (BSN). While Manges et al³ did not elucidate the mechanism by which this occurs, their findings align with existing literature showing that patients receiving care from a higher proportion of BSN-prepared nurses experience an 18.7% reduction in odds of readmission.⁶ Further research investigating the link between team communication, registered nurse education, and discharge outcomes may reveal additional opportunities for interventions to improve discharge quality.

The lack of patient outcomes and the limited diversity of the patient population are substantial limitations of the study. The authors did not assess the relationship between SMMs and important outcomes like readmission or adverse events. Furthermore, most of the patients were White and English-speaking, precluding assessment of factors that disproportionately impact patient populations that already experience disparities in a multitude of health outcomes.

In summary, Manges et al³ highlight challenges and opportunities in optimizing clinician communication and ensuring that the team’s and the patient’s self-assessments align and inform discharge planning. Their findings suggest the theoretical framework of SMM holds promise in identifying and evaluating some of the complex determinants involved in high-quality, patient-centered hospital discharges.

Hospital discharge is a complex, multi-stakeholder event, and evidence suggests that the quality of that transition directly relates to mortality, readmissions, and postdischarge quality of life and functional status.¹ The Centers for Medicare & Medicaid Services call for team-based and patient-centered discharge planning,² yet the process for achieving this is poorly defined.

In this issue of the Journal of Hospital Medicine, Manges et al³ use shared mental models (SMM) as a conceptual framework to describe differences in how care team members and patients perceive hospital discharge readiness. While our understanding of factors associated with safe and patient-centered hospital discharges is still growing, the authors focus on one critical component: lack of agreement between patients and interprofessional teams regarding discharge readiness.

Manges et al³ measured whether interprofessional team members agree, or converge, on their assessment of a patient’s discharge readiness (team-SMM convergence) and whether that assessment converges with the patient’s self-assessment (team-patient SMM convergence). They found good team-SMM convergence regarding the patient’s discharge readiness, yet teams overestimated readiness compared with the patient’s self-assessment nearly half (48.4%) of the time. A clinical trial found that clinician assessments of discharge readiness were poorly predictive of readmissions unless they were combined with a patient’s self-assessment.⁴ Manges et al’s study findings, while of limited generalizability, enhance our understanding of a potential gap in achieving patient-centered care as outlined in the Institute of Medicine’s Crossing the Quality Chasm,⁵ which urges clinicians to see patients and families as partners in improving care.

The authors also found that higher team-patient convergence was associated with teams that reported high-quality teamwork and those having more baccalaureate degree−educated nurses (BSN). While Manges et al³ did not elucidate the mechanism by which this occurs, their findings align with existing literature showing that patients receiving care from a higher proportion of BSN-prepared nurses experience an 18.7% reduction in odds of readmission.⁶ Further research investigating the link between team communication, registered nurse education, and discharge outcomes may reveal additional opportunities for interventions to improve discharge quality.

The lack of patient outcomes and the limited diversity of the patient population are substantial limitations of the study. The authors did not assess the relationship between SMMs and important outcomes like readmission or adverse events. Furthermore, most of the patients were White and English-speaking, precluding assessment of factors that disproportionately impact patient populations that already experience disparities in a multitude of health outcomes.

In summary, Manges et al³ highlight challenges and opportunities in optimizing clinician communication and ensuring that the team’s and the patient’s self-assessments align and inform discharge planning. Their findings suggest the theoretical framework of SMM holds promise in identifying and evaluating some of the complex determinants involved in high-quality, patient-centered hospital discharges.

A functioning healthcare system requires trust on many levels. In its simplest form, this is the trust between an individual patient and their physician that allows for candor, autonomy, informed decisions, and compassionate care. Trust is a central component of medical education, as trainees gradually earn the trust of their supervisors to achieve autonomy. And, on a much larger scale, societal trust in science, the facts, and the medical system influences individual and group decisions that can have far-reaching consequences.

Defining trust is challenging. Trust is relational, an often subconscious decision “by one individual to depend on another,” but it can also be as broad as trust in an institution or a national system.¹ Trust also requires vulnerability—trusting another person or system means ceding some level of personal control and accepting risk. Thus, to ask patients and society to trust in physicians, the healthcare system, or public health institutions, though essential, is no small request.

Physicians and the medical system at large have not always behaved in ways that warrant trust. Medical research on vulnerable populations (historically marginalized communities, prisoners, residents of institutions) has occurred within living memory. Systemic racism within medicine has led to marked disparities in access and outcomes between White and minoritized communities.² These disparities have been accentuated by the pandemic. Black and Brown patients have higher infection rates and higher mortality rates but less access to healthcare.³ Vaccine distribution, which has been complicated by historic earned distrust from Black and Brown communities, revealed systemic racism. For example, many early mass vaccination sites, such as Dodger Stadium in Los Angeles, could only be easily reached by car. Online appointment scheduling platforms were opaque and required access to technology.⁴

Public trust in institutions has been eroding over the past several decades, but healthcare has unfortunately seen the largest decline.⁵ Individual healthcare decisions have also been increasingly politicized; the net result is the creation of laws, such as those limiting discussions of firearm safety or banning gender-affirming treatments for transgender children, that influence patient-physician interactions. This combination of erosion of trust and politicization of medical decisions has been harshly highlighted by the global pandemic, complicating public health policy and doctor-patient discussions. Public health measures such as masking and vaccination have become polarized.⁶ Further, there is diminishing trust in medical recommendations, brought about by the current media landscape and by frequent modifications to public health recommendations. Science and medicine are constantly changing, and knowledge in these fields is ultimately provisional. Unfortunately, when new data are published that contradict prior information or report new or dramatic findings, it can appear that the medical system was somehow obscuring the truth in the past, rather than simply advancing its knowledge in the present.

How do we build trust? How do we function in a healthcare system where trust has been eroded? Trust is ultimately a fragile thing. The process of earning it is not swift or straightforward, but it can be lost in a moment.

In partnership with the ABIM Foundation, the Journal of Hospital Medicine will explore the concept of trust in all facets of healthcare and medical education, including understanding the drivers of trust in a multitude of settings and in different relationships (patient-clinician, clinician-trainee, clinician- or trainee-organization, health system-community), interventions to build trust, and the enablers of those interventions. To this end, we are seeking articles that explore or evaluate trust. These include original research, brief reports, perspectives, and Leadership & Professional Development articles. Articles focusing on trust should be submitted by December 31, 2021.

A functioning healthcare system requires trust on many levels. In its simplest form, this is the trust between an individual patient and their physician that allows for candor, autonomy, informed decisions, and compassionate care. Trust is a central component of medical education, as trainees gradually earn the trust of their supervisors to achieve autonomy. And, on a much larger scale, societal trust in science, the facts, and the medical system influences individual and group decisions that can have far-reaching consequences.

Defining trust is challenging. Trust is relational, an often subconscious decision “by one individual to depend on another,” but it can also be as broad as trust in an institution or a national system.¹ Trust also requires vulnerability—trusting another person or system means ceding some level of personal control and accepting risk. Thus, to ask patients and society to trust in physicians, the healthcare system, or public health institutions, though essential, is no small request.

Physicians and the medical system at large have not always behaved in ways that warrant trust. Medical research on vulnerable populations (historically marginalized communities, prisoners, residents of institutions) has occurred within living memory. Systemic racism within medicine has led to marked disparities in access and outcomes between White and minoritized communities.² These disparities have been accentuated by the pandemic. Black and Brown patients have higher infection rates and higher mortality rates but less access to healthcare.³ Vaccine distribution, which has been complicated by historic earned distrust from Black and Brown communities, revealed systemic racism. For example, many early mass vaccination sites, such as Dodger Stadium in Los Angeles, could only be easily reached by car. Online appointment scheduling platforms were opaque and required access to technology.⁴

Public trust in institutions has been eroding over the past several decades, but healthcare has unfortunately seen the largest decline.⁵ Individual healthcare decisions have also been increasingly politicized; the net result is the creation of laws, such as those limiting discussions of firearm safety or banning gender-affirming treatments for transgender children, that influence patient-physician interactions. This combination of erosion of trust and politicization of medical decisions has been harshly highlighted by the global pandemic, complicating public health policy and doctor-patient discussions. Public health measures such as masking and vaccination have become polarized.⁶ Further, there is diminishing trust in medical recommendations, brought about by the current media landscape and by frequent modifications to public health recommendations. Science and medicine are constantly changing, and knowledge in these fields is ultimately provisional. Unfortunately, when new data are published that contradict prior information or report new or dramatic findings, it can appear that the medical system was somehow obscuring the truth in the past, rather than simply advancing its knowledge in the present.

How do we build trust? How do we function in a healthcare system where trust has been eroded? Trust is ultimately a fragile thing. The process of earning it is not swift or straightforward, but it can be lost in a moment.

In partnership with the ABIM Foundation, the Journal of Hospital Medicine will explore the concept of trust in all facets of healthcare and medical education, including understanding the drivers of trust in a multitude of settings and in different relationships (patient-clinician, clinician-trainee, clinician- or trainee-organization, health system-community), interventions to build trust, and the enablers of those interventions. To this end, we are seeking articles that explore or evaluate trust. These include original research, brief reports, perspectives, and Leadership & Professional Development articles. Articles focusing on trust should be submitted by December 31, 2021.

A functioning healthcare system requires trust on many levels. In its simplest form, this is the trust between an individual patient and their physician that allows for candor, autonomy, informed decisions, and compassionate care. Trust is a central component of medical education, as trainees gradually earn the trust of their supervisors to achieve autonomy. And, on a much larger scale, societal trust in science, the facts, and the medical system influences individual and group decisions that can have far-reaching consequences.

Defining trust is challenging. Trust is relational, an often subconscious decision “by one individual to depend on another,” but it can also be as broad as trust in an institution or a national system.¹ Trust also requires vulnerability—trusting another person or system means ceding some level of personal control and accepting risk. Thus, to ask patients and society to trust in physicians, the healthcare system, or public health institutions, though essential, is no small request.

Physicians and the medical system at large have not always behaved in ways that warrant trust. Medical research on vulnerable populations (historically marginalized communities, prisoners, residents of institutions) has occurred within living memory. Systemic racism within medicine has led to marked disparities in access and outcomes between White and minoritized communities.² These disparities have been accentuated by the pandemic. Black and Brown patients have higher infection rates and higher mortality rates but less access to healthcare.³ Vaccine distribution, which has been complicated by historic earned distrust from Black and Brown communities, revealed systemic racism. For example, many early mass vaccination sites, such as Dodger Stadium in Los Angeles, could only be easily reached by car. Online appointment scheduling platforms were opaque and required access to technology.⁴

Public trust in institutions has been eroding over the past several decades, but healthcare has unfortunately seen the largest decline.⁵ Individual healthcare decisions have also been increasingly politicized; the net result is the creation of laws, such as those limiting discussions of firearm safety or banning gender-affirming treatments for transgender children, that influence patient-physician interactions. This combination of erosion of trust and politicization of medical decisions has been harshly highlighted by the global pandemic, complicating public health policy and doctor-patient discussions. Public health measures such as masking and vaccination have become polarized.⁶ Further, there is diminishing trust in medical recommendations, brought about by the current media landscape and by frequent modifications to public health recommendations. Science and medicine are constantly changing, and knowledge in these fields is ultimately provisional. Unfortunately, when new data are published that contradict prior information or report new or dramatic findings, it can appear that the medical system was somehow obscuring the truth in the past, rather than simply advancing its knowledge in the present.

How do we build trust? How do we function in a healthcare system where trust has been eroded? Trust is ultimately a fragile thing. The process of earning it is not swift or straightforward, but it can be lost in a moment.

In partnership with the ABIM Foundation, the Journal of Hospital Medicine will explore the concept of trust in all facets of healthcare and medical education, including understanding the drivers of trust in a multitude of settings and in different relationships (patient-clinician, clinician-trainee, clinician- or trainee-organization, health system-community), interventions to build trust, and the enablers of those interventions. To this end, we are seeking articles that explore or evaluate trust. These include original research, brief reports, perspectives, and Leadership & Professional Development articles. Articles focusing on trust should be submitted by December 31, 2021.

We recently published an article in our Leadership & Professional Development series titled “Tribalism: The Good, the Bad, and the Future.” Despite pre- and post-acceptance manuscript review and discussion by a diverse and thoughtful team of editors, we did not appreciate how particular language in this article would be hurtful to some communities. We also promoted the article using the hashtag “tribalism” in a journal tweet. Shortly after we posted the tweet, several readers on social media reached out with constructive feedback on the prejudicial nature of this terminology. Within hours of receiving this feedback, our editorial team met to better understand our error, and we made the decision to immediately retract the manuscript. We also deleted the tweet and issued an apology referencing a screenshot of the original tweet.^1,2 We have republished the original article with appropriate language.³ Tweets promoting the new article will incorporate this new language.

From this experience, we learned that the words “tribe” and “tribalism” have no consistent meaning, are associated with negative historical and cultural assumptions, and can promote misleading stereotypes.⁴ The term “tribe” became popular as a colonial construct to describe forms of social organization considered ”uncivilized” or ”primitive.“⁵ In using the term “tribe” to describe members of medical communities, we ignored the complex and dynamic identities of Native American, African, and other Indigenous Peoples and the history of their oppression.

The intent of the original article was to highlight how being part of a distinct medical discipline, such as hospital medicine or emergency medicine, conferred benefits, such as shared identity and social support structure, and caution how this group identity could also lead to nonconstructive partisan behaviors that might not best serve our patients. We recognize that other words more accurately convey our intent and do not cause harm. We used “tribe” when we meant “group,” “discipline,” or “specialty.” We used “tribalism” when we meant “siloed” or “factional.”

This misstep underscores how, even with the best intentions and diverse teams, microaggressions can happen. We accept responsibility for this mistake, and we will continue to do the work of respecting and advocating for all members of our community. To minimize the likelihood of future errors, we are developing a systematic process to identify language within manuscripts accepted for publication that may be racist, sexist, ableist, homophobic, or otherwise harmful. As we embrace a growth mindset, we vow to remain transparent, responsive, and welcoming of feedback. We are grateful to our readers for helping us learn.

We recently published an article in our Leadership & Professional Development series titled “Tribalism: The Good, the Bad, and the Future.” Despite pre- and post-acceptance manuscript review and discussion by a diverse and thoughtful team of editors, we did not appreciate how particular language in this article would be hurtful to some communities. We also promoted the article using the hashtag “tribalism” in a journal tweet. Shortly after we posted the tweet, several readers on social media reached out with constructive feedback on the prejudicial nature of this terminology. Within hours of receiving this feedback, our editorial team met to better understand our error, and we made the decision to immediately retract the manuscript. We also deleted the tweet and issued an apology referencing a screenshot of the original tweet.^1,2 We have republished the original article with appropriate language.³ Tweets promoting the new article will incorporate this new language.

From this experience, we learned that the words “tribe” and “tribalism” have no consistent meaning, are associated with negative historical and cultural assumptions, and can promote misleading stereotypes.⁴ The term “tribe” became popular as a colonial construct to describe forms of social organization considered ”uncivilized” or ”primitive.“⁵ In using the term “tribe” to describe members of medical communities, we ignored the complex and dynamic identities of Native American, African, and other Indigenous Peoples and the history of their oppression.

The intent of the original article was to highlight how being part of a distinct medical discipline, such as hospital medicine or emergency medicine, conferred benefits, such as shared identity and social support structure, and caution how this group identity could also lead to nonconstructive partisan behaviors that might not best serve our patients. We recognize that other words more accurately convey our intent and do not cause harm. We used “tribe” when we meant “group,” “discipline,” or “specialty.” We used “tribalism” when we meant “siloed” or “factional.”

This misstep underscores how, even with the best intentions and diverse teams, microaggressions can happen. We accept responsibility for this mistake, and we will continue to do the work of respecting and advocating for all members of our community. To minimize the likelihood of future errors, we are developing a systematic process to identify language within manuscripts accepted for publication that may be racist, sexist, ableist, homophobic, or otherwise harmful. As we embrace a growth mindset, we vow to remain transparent, responsive, and welcoming of feedback. We are grateful to our readers for helping us learn.

We recently published an article in our Leadership & Professional Development series titled “Tribalism: The Good, the Bad, and the Future.” Despite pre- and post-acceptance manuscript review and discussion by a diverse and thoughtful team of editors, we did not appreciate how particular language in this article would be hurtful to some communities. We also promoted the article using the hashtag “tribalism” in a journal tweet. Shortly after we posted the tweet, several readers on social media reached out with constructive feedback on the prejudicial nature of this terminology. Within hours of receiving this feedback, our editorial team met to better understand our error, and we made the decision to immediately retract the manuscript. We also deleted the tweet and issued an apology referencing a screenshot of the original tweet.^1,2 We have republished the original article with appropriate language.³ Tweets promoting the new article will incorporate this new language.

From this experience, we learned that the words “tribe” and “tribalism” have no consistent meaning, are associated with negative historical and cultural assumptions, and can promote misleading stereotypes.⁴ The term “tribe” became popular as a colonial construct to describe forms of social organization considered ”uncivilized” or ”primitive.“⁵ In using the term “tribe” to describe members of medical communities, we ignored the complex and dynamic identities of Native American, African, and other Indigenous Peoples and the history of their oppression.

The intent of the original article was to highlight how being part of a distinct medical discipline, such as hospital medicine or emergency medicine, conferred benefits, such as shared identity and social support structure, and caution how this group identity could also lead to nonconstructive partisan behaviors that might not best serve our patients. We recognize that other words more accurately convey our intent and do not cause harm. We used “tribe” when we meant “group,” “discipline,” or “specialty.” We used “tribalism” when we meant “siloed” or “factional.”

This misstep underscores how, even with the best intentions and diverse teams, microaggressions can happen. We accept responsibility for this mistake, and we will continue to do the work of respecting and advocating for all members of our community. To minimize the likelihood of future errors, we are developing a systematic process to identify language within manuscripts accepted for publication that may be racist, sexist, ableist, homophobic, or otherwise harmful. As we embrace a growth mindset, we vow to remain transparent, responsive, and welcoming of feedback. We are grateful to our readers for helping us learn.

Siloed, adj.:

Kept in isolation in a way that hinders communication and cooperation . . .

—Merriam-Webster’s Dictionary

Humans naturally separate into groups, and the medical field is no exception. Being a member of a likeminded group, such as one’s specialty, can improve self-esteem and provide social organization: it feels good to identify with people we admire. Through culture, these specialty-based groups implicitly and explicitly guide and encourage positive attributes or behaviors like a hospitalist’s thoroughness or an emergency medicine physician’s steady management of unstable patients. Our specialties also provide support and understanding in challenging times. 

Despite these positive aspects, such divisions can negatively affect interprofessional relationships when our specialties become siloed. A potential side-effect of building up ourselves and our own groups is that we can implicitly put others down. For example, a hospitalist who spends extra time on the phone regularly updating each patient’s family will appropriately take pride in their practice, but over time this can also lead to an unreasonable assumption that physicians in other departments with different routines are not as committed to outstanding communication.

These rigid separations facilitate the fundamental attribution error, the tendency to ascribe a problem or disagreement to a colleague’s substandard character or ability. Imagine that the aforementioned hospitalist’s phone call delays a response to an admission page from the emergency room. The emergency medicine physician, who is waiting to sign out the admission while simultaneously managing many sick and complex patients, could assume the hospitalist is being disrespectful, rather than also working hard to provide the best care. Our siloed specialty identities can lead us to imagine the worst in each other and exacerbate intergroup conflict.¹

Silos in medicine also adversely affect patients. Poor communication and lack of information-sharing across disciplines can lead to medical error² and stifle dissemination of safer practices.³ Further, the unintentional disparaging of other medical specialties undermines the confidence our patients have in all of us; a patient within earshot of the hospitalist expressing annoyance at the “impatient” emergency medicine physician who “won’t stop paging,” or the emergency medicine physician complaining about the hospitalist who “refuses to call back,” will lose trust in each of their providers. 

We suggest three steps to reduce the negative impact of specialty silos in medicine: 

1. Get to know each other personally. Friendly conversation during work hours and social interaction outside the hospital can inoculate against interspecialty conflict by putting a human face on our colleagues. The resultant relationships make it easier to work together and see things from another’s perspective. 
2. Emphasize our shared affiliations.⁴ The greater the salience of a mutual identity as “healthcare providers,” the more likely we are to recognize each other’s unique contributions and question the stereotypes we imagine about one another. 
3. Consider projects across specialties. Interdepartmental data-sharing and joint meetings, including educational conferences, can facilitate situational awareness, synergy, and efficient problem-solving. 

Our medical specialties will continue to group together. While these groups can be a source of strength and meaning, silos can interfere with professional alliances and effective patient care. Mitigating the harmful effects of silos can benefit all of us and our patients.

Authors’ note: This article was previously published using the term “tribalism,” which we have since learned is derogatory to Indigenous Americans and others. We apologize for any harm. We have retracted and republished the article without this language. We appreciate readers teaching us how to choose better words so all people feel respected and valued.

Siloed, adj.:

Kept in isolation in a way that hinders communication and cooperation . . .

—Merriam-Webster’s Dictionary

Humans naturally separate into groups, and the medical field is no exception. Being a member of a likeminded group, such as one’s specialty, can improve self-esteem and provide social organization: it feels good to identify with people we admire. Through culture, these specialty-based groups implicitly and explicitly guide and encourage positive attributes or behaviors like a hospitalist’s thoroughness or an emergency medicine physician’s steady management of unstable patients. Our specialties also provide support and understanding in challenging times. 

Despite these positive aspects, such divisions can negatively affect interprofessional relationships when our specialties become siloed. A potential side-effect of building up ourselves and our own groups is that we can implicitly put others down. For example, a hospitalist who spends extra time on the phone regularly updating each patient’s family will appropriately take pride in their practice, but over time this can also lead to an unreasonable assumption that physicians in other departments with different routines are not as committed to outstanding communication.

These rigid separations facilitate the fundamental attribution error, the tendency to ascribe a problem or disagreement to a colleague’s substandard character or ability. Imagine that the aforementioned hospitalist’s phone call delays a response to an admission page from the emergency room. The emergency medicine physician, who is waiting to sign out the admission while simultaneously managing many sick and complex patients, could assume the hospitalist is being disrespectful, rather than also working hard to provide the best care. Our siloed specialty identities can lead us to imagine the worst in each other and exacerbate intergroup conflict.¹

Silos in medicine also adversely affect patients. Poor communication and lack of information-sharing across disciplines can lead to medical error² and stifle dissemination of safer practices.³ Further, the unintentional disparaging of other medical specialties undermines the confidence our patients have in all of us; a patient within earshot of the hospitalist expressing annoyance at the “impatient” emergency medicine physician who “won’t stop paging,” or the emergency medicine physician complaining about the hospitalist who “refuses to call back,” will lose trust in each of their providers. 

We suggest three steps to reduce the negative impact of specialty silos in medicine: 

1. Get to know each other personally. Friendly conversation during work hours and social interaction outside the hospital can inoculate against interspecialty conflict by putting a human face on our colleagues. The resultant relationships make it easier to work together and see things from another’s perspective. 
2. Emphasize our shared affiliations.⁴ The greater the salience of a mutual identity as “healthcare providers,” the more likely we are to recognize each other’s unique contributions and question the stereotypes we imagine about one another. 
3. Consider projects across specialties. Interdepartmental data-sharing and joint meetings, including educational conferences, can facilitate situational awareness, synergy, and efficient problem-solving. 

Our medical specialties will continue to group together. While these groups can be a source of strength and meaning, silos can interfere with professional alliances and effective patient care. Mitigating the harmful effects of silos can benefit all of us and our patients.

Authors’ note: This article was previously published using the term “tribalism,” which we have since learned is derogatory to Indigenous Americans and others. We apologize for any harm. We have retracted and republished the article without this language. We appreciate readers teaching us how to choose better words so all people feel respected and valued.

Siloed, adj.:

Kept in isolation in a way that hinders communication and cooperation . . .

—Merriam-Webster’s Dictionary

Humans naturally separate into groups, and the medical field is no exception. Being a member of a likeminded group, such as one’s specialty, can improve self-esteem and provide social organization: it feels good to identify with people we admire. Through culture, these specialty-based groups implicitly and explicitly guide and encourage positive attributes or behaviors like a hospitalist’s thoroughness or an emergency medicine physician’s steady management of unstable patients. Our specialties also provide support and understanding in challenging times. 

Despite these positive aspects, such divisions can negatively affect interprofessional relationships when our specialties become siloed. A potential side-effect of building up ourselves and our own groups is that we can implicitly put others down. For example, a hospitalist who spends extra time on the phone regularly updating each patient’s family will appropriately take pride in their practice, but over time this can also lead to an unreasonable assumption that physicians in other departments with different routines are not as committed to outstanding communication.

These rigid separations facilitate the fundamental attribution error, the tendency to ascribe a problem or disagreement to a colleague’s substandard character or ability. Imagine that the aforementioned hospitalist’s phone call delays a response to an admission page from the emergency room. The emergency medicine physician, who is waiting to sign out the admission while simultaneously managing many sick and complex patients, could assume the hospitalist is being disrespectful, rather than also working hard to provide the best care. Our siloed specialty identities can lead us to imagine the worst in each other and exacerbate intergroup conflict.¹

Silos in medicine also adversely affect patients. Poor communication and lack of information-sharing across disciplines can lead to medical error² and stifle dissemination of safer practices.³ Further, the unintentional disparaging of other medical specialties undermines the confidence our patients have in all of us; a patient within earshot of the hospitalist expressing annoyance at the “impatient” emergency medicine physician who “won’t stop paging,” or the emergency medicine physician complaining about the hospitalist who “refuses to call back,” will lose trust in each of their providers. 

We suggest three steps to reduce the negative impact of specialty silos in medicine: 

1. Get to know each other personally. Friendly conversation during work hours and social interaction outside the hospital can inoculate against interspecialty conflict by putting a human face on our colleagues. The resultant relationships make it easier to work together and see things from another’s perspective. 
2. Emphasize our shared affiliations.⁴ The greater the salience of a mutual identity as “healthcare providers,” the more likely we are to recognize each other’s unique contributions and question the stereotypes we imagine about one another. 
3. Consider projects across specialties. Interdepartmental data-sharing and joint meetings, including educational conferences, can facilitate situational awareness, synergy, and efficient problem-solving. 

Our medical specialties will continue to group together. While these groups can be a source of strength and meaning, silos can interfere with professional alliances and effective patient care. Mitigating the harmful effects of silos can benefit all of us and our patients.

Authors’ note: This article was previously published using the term “tribalism,” which we have since learned is derogatory to Indigenous Americans and others. We apologize for any harm. We have retracted and republished the article without this language. We appreciate readers teaching us how to choose better words so all people feel respected and valued.

There is substantial interest in improving patient flow and reducing hospital length of stay (LOS).^1-4 Impaired hospital flow may negatively impact both patient satisfaction and safety through, for example, emergency department (ED) overcrowding.^5,6 Impaired hospital flow is associated with downstream effects on patient care, hospital costs, and availability of beds.^7-9

A number of quality-improvement interventions aim to improve patient flow, including efforts to increase the number of discharges that occur before noon.^10,11 Morning discharges have been hypothesized to free hospital beds earlier, thus reducing ED wait times for incoming patients and increasing beds for elective surgeries.¹¹ Morning discharges may also be more predictable for staff and patients. However, it is unclear whether efforts to increase the number of morning discharges have a negative impact on inpatient LOS by incentivizing physicians to keep patients in the hospital for an extra night to facilitate discharge in the early morning rather than the late afternoon. Morning discharges have been associated with both increased¹² and decreased LOS.^10,11,13-15

The purpose of this study was to examine the associations between morning discharges and ED LOS and hospital LOS in general internal medicine (GIM) at seven hospitals. GIM patients represent nearly 40% of ED admissions to a hospital,¹⁶ and thus are an important determinant of patient flow through the ED and hospital. We hypothesized that patients who were admitted to GIM on days with more morning discharges would have shorter ED LOS and hospital LOS.

Design, Setting, and Participants

This was a retrospective cohort study conducted using the General Medicine Inpatient Initiative (GEMINI) clinical dataset.¹⁶ The dataset includes all GIM admissions at seven large hospital sites in Toronto and Mississauga, Ontario, Canada. These include five academic hospitals and two community-based teaching hospitals. Each hospital is publicly funded and provides tertiary and/or quaternary care to diverse multiethnic populations. Research ethics board approval was obtained from all participating sites.

GIM care is delivered by several interdisciplinary clinical teams functioning in parallel. Attending physicians are predominantly internists who practice as hospitalists in discrete service blocks, typically lasting 2 weeks at a time. Although GIM patients are preferentially admitted to GIM wards, participating hospitals did not have strict policies regarding cohorting GIM patients to specific wards (ie, holding patients in ED until a specific bed becomes available) that would confound the association between morning discharge and ED wait times. Approximately 75% of GIM patients are cared for on dedicated GIM wards at participating hospitals, with the remainder cared for on other medical or surgical wards.

We included all hospitalized patients who were admitted to hospital and discharged from GIM between April 1, 2010, and October 31, 2017, from the seven GEMINI hospitals. We included only patients admitted through the ED. As such, we did not include elective admissions or interfacility transfers who would not experience ED wait times. We excluded patients who were discharged without a provincial health insurance number (N = 2,169; 1.1% of total sample) because they could not be linked across visits to measure readmissions.

Data Source

The GEMINI dataset has been rigorously validated and previously described in detail.¹⁶ GEMINI collects both administrative health data reported to the Canadian Institute for Health Information (including data about patient demographics, comorbidities, and discharge destination) as well as electronic clinical data extracted from hospital computer systems (including attending physicians, in-hospital patient room transfers, and laboratory test results). Data are collected for each individual hospital encounter, and the provincial health insurance number is used to link patients across encounters.

Exposures and Outcomes

The primary exposure was the number of GIM patients discharged alive between 8:00 am and 12:00 pm (ie, morning GIM discharges) on the day of admission for each hospital encounter. This time window to define morning discharges was selected based on previous literature.^10-13 To report admission characteristics and unadjusted outcomes, hospital days were categorized into quartiles within each hospital based on the number of morning GIM discharges. Hospital days with the lowest number of morning discharges were classified into Q1 and the highest number of morning discharges into Q4, and quartiles were pooled across hospitals.

The two primary outcomes were ED LOS and hospital LOS. ED LOS was calculated as the difference between the time from triage by nursing staff to a patient’s exit from the ED, measured in hours. We also examined 30-day readmission to GIM at any participating hospital as a balancing measure against premature discharges and inpatient mortality because it could modify hospital LOS.

Patient Characteristics

Baseline patient characteristics were measured, including age, sex, Charlson Comorbidity Index score,¹⁷ day of admission (categorized as weekend/holiday or weekday), time of admission to hospital (categorized as daytime, 8:00 am to 4:59 pm, or nighttime, 5:00 pm to 07:59 am), study month, hospital site, and whether patients had been admitted to GIM in the prior 30 days. We used laboratory data to calculate the baseline Laboratory-based Acute Physiology Score (LAPS), which is a validated predictor of inpatient mortality when combined with age and Charlson Comorbidity Index score.^18,19 GIM census on day of admission was calculated in order to include overall patient volumes as an important adjustment variable that could confound the association between morning discharges and patient flow.

Statistical Analysis

The study population and physician characteristics were summarized with descriptive statistics. The balance of baseline patient characteristics across morning discharge quartiles was assessed using standardized differences. A standardized difference of less than 0.1 reflects good balance.²⁰

Unadjusted estimates of patient outcomes were reported across morning discharge quartiles. To model the overall association between morning discharge and outcomes, the number of morning GIM discharges on the day of admission was subtracted from the mean number of morning discharges at each hospital and considered as a continuous exposure. We used generalized linear mixed models to estimate the effect of morning discharges on patient outcomes. We fit negative binomial regression models with log link to examine the association between the number of morning discharges (centered by subtracting the hospital mean) and the two main outcomes, ED LOS and hospital LOS. Given the overdispersion of the study population due to the unequal mean and variance, a negative binomial model was preferred over a Poisson regression, as the mean and variance were not equal.²¹ For our secondary outcomes of binary measures (30-day readmission and morality), we fit logistic regression models. Adjustment for multiple comparisons was not performed.

Multivariable analysis was conducted to adjust for the baseline characteristics described above as well as the total number of GIM discharges on the day of admission and GIM census on the day of admission. Hospital and study month (to account for secular time trends) were included as fixed effects, and patients and admitting physicians were included as crossed random effects to account for the nested structure of admissions within patients and admissions within physicians within hospitals.

A sensitivity analysis was performed to assess for nonlinear associations between morning discharges and the four outcomes (hospital LOS, ED LOS, in-hospital mortality, and readmission) by inputting the term as a restricted cubic spline, with up to five knots, in multivariable regression models. We compared the Akaike information criteria (AIC), computed using the log-likelihood, to determine the goodness-of-fit from the negative binomial models.²² We replicated models for each individual hospital to examine whether any hospital-specific associations existed. Two additional sensitivity analyses were performed to examine heterogeneity in hospital-specific effects. Regression models were fit, including interaction terms between morning discharge and hospital, as well as interaction terms between hospital and the total number of GIM discharges and GIM census. The findings of these models (data not presented) were qualitatively similar to the overall results.

Study Population and Patient Characteristics

The study population consisted of 189,781 hospitalizations involving 115,630 unique patients. The median patient age was 73 years (interquartile range [IQR], 57-84), 50.3% were female, 43.8% had a high Charlson Comorbidity Index score, and 11.1% were admitted to GIM in the prior 30 days (Table 1). The median ED LOS was 14.5 hours (IQR, 10.0-23.1), and the mean was 18.1 hours (SD, 12.2). The median hospital LOS was 4.6 days (IQR, 2.4-9.0), and the mean was 8.6 days (SD, 18.7).

In total, 36,043 (19.0%) discharges occurred between 8:00 am and 12:00 pm. The average number of total daily discharges per hospital was 8.4 (SD, 4.6), and the average number of morning discharges was 1.7 (SD, 1.4). Morning discharges varied across hospitals, ranging from 0.9 per day (SD, 1.1) to 2.1 (SD, 1.6) (Appendix Table 1). The average number of morning discharges in the lowest quartile (Q1) was 0.3 (SD, 0.5) and was 3.3 (SD, 1.2) in the highest quartile (Q4). Baseline patient characteristics were well balanced across the quartiles, with standardized differences less than 0.1 (Table 1), except day of admission and GIM census. Days with a greater number of morning discharges were more likely to be weekdays rather than weekends/holidays (89.2% weekday admissions in Q4 compared with 53.9% in Q1). The median GIM census was 93 patients (IQR, 79-109).

Outcomes

Unadjusted clinical outcomes by number of morning discharges are presented in Table 2. The median unadjusted ED LOS was 14.4 (SD, 14.1), 14.3 (SD, 13.2), 14.5 (SD, 13.0), and 14.8 (SD, 13.0) hours for the first to fourth quartiles (fewest to largest number of morning discharges), respectively. The median unadjusted hospital LOS was 4.6 (SD, 6.5), 4.6 (SD, 6.9), 4.7 (SD, 6.4), and 4.6 (SD, 6.4) days for the first to fourth quartiles, respectively.

Unadjusted inpatient mortality was 6.1%, 5.5%, 5.5%, and 5.2% across the first to fourth quartiles, respectively. Unadjusted 30-day readmission to GIM was 12.2%, 12.6%, 12.6%, and 12.5% across the first to fourth quartiles, respectively.

After multivariable adjustment, there was no significant association between morning discharge and hospital LOS (aRR, 1.000; 95% CI, 0.996-1.000; P = .997), ED LOS (aRR, 0.999; 95% CI, 0.997-1.000; P = .307), in-hospital mortality (aRR, 0.967; 95% CI, 0.920-1.020; P =.183), or 30-day readmission (aRR, 1.010; 95% CI, 0.991-1.020; P = .471) (Table 3, Appendix Table 2, Appendix Table 3, Appendix Table 4, Appendix Table 5). When examining each hospital separately, we found that morning discharge was significantly associated with hospital LOS at only one hospital (Hospital D; aRR, 0.981; 95% CI, 0.966-0.996; P = .013). Morning discharge was statistically significantly associated with ED LOS at three hospitals (A, B, and C), but the aRR was at least 0.99 in all three cases (Table 4).

In sensitivity analyses, we found no improvements in model fit when adding spline terms to the model, suggesting no significant nonlinear associations between morning discharges and the outcomes of interest.

DISCUSSION

This large multicenter cohort study found no significant overall association between the number of morning discharges and ED or hospital LOS in GIM. At one hospital, there was a 1.9% reduction in adjusted ED LOS for every additional morning discharge, but no difference in hospital LOS. We also did not observe differences in readmission or inpatient mortality associated with the number of morning discharges. Our observational findings suggest that there is unlikely to be a strong association between morning discharge and patient throughput in GIM. Given that there may be other downstream benefits of morning discharge, such as freeing beds for daytime surgeries,²³ further research is needed to determine the effectiveness of specific interventions.

Several studies have posited morning discharge as a method of improving both patient care and hospital flow metrics.^{10,11,13-15,23} Quality improvement initiatives targeting morning discharges have included stakeholder meetings, incentives programs, discharge-centered breakfast programs, and creating deadlines for discharge orders.^24-29 Although these initiatives have gained support, critics have suggested that their supporting evidence is not robust. Werthemier et al¹⁰ found a 9.0% reduction of observed to expected LOS associated with increasing the number of early discharges. However, a response article suggested that their findings were confounded by other hospital initiatives, such as allocation of medical and social services to weekends.³⁰ Other observational studies have concluded that hospital LOS is not affected by the number of morning discharges, but this research has been limited by single-center analysis and relatively smaller sample sizes.¹² Our study further calls into question the association between morning discharge and patient throughput.

An additional reason for the controversy is that physicians may actively work to discharge patients late in the day to avoid an additional night in hospital. A qualitative study by Minichiello et al³¹ evaluated staff perceptions regarding afternoon discharges. Physicians and medical students believed that afternoon discharges were a result of waiting for test results and procedures, with staff aiming to discharge patients immediately after obtaining results or finishing necessary procedures. As such, there are concerns that incentivizing morning discharge may lead physicians in the opposite direction, to consciously or unconsciously keep patients overnight in order to facilitate an early morning discharge.³⁰

Our study’s greatest strength was the large sample size over 7 years at seven hospitals in two cities, including both academic and community hospitals with different models of care. To our knowledge, this is the first cohort study that has analyzed the association between early discharge and LOS using multiple centers. To avoid the confounding and reverse causality that may exist when examining the relationship between LOS and morning discharge at the patient level (eg, patients who stay in hospital longer may have more “planned” discharges and leave in the morning), we examined the association based on variation across different days within the GIM service of each hospital. Further, we included robust risk adjustment using clinical and laboratory data. Finally, since our study included a diverse patient population served by participating centers in a system with universal insurance for hospital care, our findings are likely generalizable to other urban and suburban hospitals.

There are several important limitations of our analysis. First, we could only include GIM patients, who represent nearly 40% of ED admissions to hospital at participating centers. A more holistic analysis across all hospital services could be justified; however, given that many quality improvement initiatives occur at the level of a single hospital service, we felt our approach would be informative for future research and improvement efforts. Approximately 75% of GIM patients at participating hospitals were cared for on a GIM ward, with 25% cared for on off-service units. We were unable to include the total hospital census in our models, and this could affect LOS and waiting times for GIM patients, particularly those admitted to off-service units. GIM census is likely highly correlated with hospital census, and we were able to adjust for this. Nevertheless, this remains an important potential source of unmeasured confounding. Second, we did not model the effects of morning discharges from GIM on patient-flow measures for non-GIM patients. Given the lack of effects for GIM patients, who would be more likely to be directly affected, it is unlikely that large effects would be seen for other hospital patients, but we did not measure effects on surgical delays or cancellations, for example.²³ Third, we report 30-day readmission to GIM at participating hospitals only, rather than all readmissions. However, prior research in our region demonstrated that 82% of hospital readmissions occur to the same site.³² Thus, our measure, which includes admission to any participating hospital, likely captures more than 80% of all readmissions, and this was a secondary outcome in our analysis. Finally, qualitative metrics, such as patient or provider satisfaction, were not measured in our study. Earlier discharge may impact patient care in other ways by being more predictable for staff, improving bed allocation for daytime procedures, making medication pick-ups easier to arrange, or making consultations with allied health services more convenient.^11,28,33 Conversely, if pressured to discharge before noon, providers may feel rushed to complete tasks and may face disruptions to typical workflow.²⁴ As such, future research is needed to provide a more complete understanding of the impact of early-morning discharge beyond hospital flow. Given the lack of a strong association observed between morning discharge and patient throughput in our study, further research should also consider the opportunity costs of interventions designed to improve morning discharge.

CONCLUSION

The number of morning discharges was not significantly associated with shorter ED LOS or hospital LOS for GIM patients. Our observational findings suggest that increasing morning discharges alone may not substantially improve patient flow in GIM. Further research is needed to evaluate specific morning discharge interventions and assess hospital-wide effects.

There is substantial interest in improving patient flow and reducing hospital length of stay (LOS).^1-4 Impaired hospital flow may negatively impact both patient satisfaction and safety through, for example, emergency department (ED) overcrowding.^5,6 Impaired hospital flow is associated with downstream effects on patient care, hospital costs, and availability of beds.^7-9

A number of quality-improvement interventions aim to improve patient flow, including efforts to increase the number of discharges that occur before noon.^10,11 Morning discharges have been hypothesized to free hospital beds earlier, thus reducing ED wait times for incoming patients and increasing beds for elective surgeries.¹¹ Morning discharges may also be more predictable for staff and patients. However, it is unclear whether efforts to increase the number of morning discharges have a negative impact on inpatient LOS by incentivizing physicians to keep patients in the hospital for an extra night to facilitate discharge in the early morning rather than the late afternoon. Morning discharges have been associated with both increased¹² and decreased LOS.^10,11,13-15

The purpose of this study was to examine the associations between morning discharges and ED LOS and hospital LOS in general internal medicine (GIM) at seven hospitals. GIM patients represent nearly 40% of ED admissions to a hospital,¹⁶ and thus are an important determinant of patient flow through the ED and hospital. We hypothesized that patients who were admitted to GIM on days with more morning discharges would have shorter ED LOS and hospital LOS.

Design, Setting, and Participants

This was a retrospective cohort study conducted using the General Medicine Inpatient Initiative (GEMINI) clinical dataset.¹⁶ The dataset includes all GIM admissions at seven large hospital sites in Toronto and Mississauga, Ontario, Canada. These include five academic hospitals and two community-based teaching hospitals. Each hospital is publicly funded and provides tertiary and/or quaternary care to diverse multiethnic populations. Research ethics board approval was obtained from all participating sites.

GIM care is delivered by several interdisciplinary clinical teams functioning in parallel. Attending physicians are predominantly internists who practice as hospitalists in discrete service blocks, typically lasting 2 weeks at a time. Although GIM patients are preferentially admitted to GIM wards, participating hospitals did not have strict policies regarding cohorting GIM patients to specific wards (ie, holding patients in ED until a specific bed becomes available) that would confound the association between morning discharge and ED wait times. Approximately 75% of GIM patients are cared for on dedicated GIM wards at participating hospitals, with the remainder cared for on other medical or surgical wards.

We included all hospitalized patients who were admitted to hospital and discharged from GIM between April 1, 2010, and October 31, 2017, from the seven GEMINI hospitals. We included only patients admitted through the ED. As such, we did not include elective admissions or interfacility transfers who would not experience ED wait times. We excluded patients who were discharged without a provincial health insurance number (N = 2,169; 1.1% of total sample) because they could not be linked across visits to measure readmissions.

Data Source

The GEMINI dataset has been rigorously validated and previously described in detail.¹⁶ GEMINI collects both administrative health data reported to the Canadian Institute for Health Information (including data about patient demographics, comorbidities, and discharge destination) as well as electronic clinical data extracted from hospital computer systems (including attending physicians, in-hospital patient room transfers, and laboratory test results). Data are collected for each individual hospital encounter, and the provincial health insurance number is used to link patients across encounters.

Exposures and Outcomes

The primary exposure was the number of GIM patients discharged alive between 8:00 am and 12:00 pm (ie, morning GIM discharges) on the day of admission for each hospital encounter. This time window to define morning discharges was selected based on previous literature.^10-13 To report admission characteristics and unadjusted outcomes, hospital days were categorized into quartiles within each hospital based on the number of morning GIM discharges. Hospital days with the lowest number of morning discharges were classified into Q1 and the highest number of morning discharges into Q4, and quartiles were pooled across hospitals.

The two primary outcomes were ED LOS and hospital LOS. ED LOS was calculated as the difference between the time from triage by nursing staff to a patient’s exit from the ED, measured in hours. We also examined 30-day readmission to GIM at any participating hospital as a balancing measure against premature discharges and inpatient mortality because it could modify hospital LOS.

Patient Characteristics

Baseline patient characteristics were measured, including age, sex, Charlson Comorbidity Index score,¹⁷ day of admission (categorized as weekend/holiday or weekday), time of admission to hospital (categorized as daytime, 8:00 am to 4:59 pm, or nighttime, 5:00 pm to 07:59 am), study month, hospital site, and whether patients had been admitted to GIM in the prior 30 days. We used laboratory data to calculate the baseline Laboratory-based Acute Physiology Score (LAPS), which is a validated predictor of inpatient mortality when combined with age and Charlson Comorbidity Index score.^18,19 GIM census on day of admission was calculated in order to include overall patient volumes as an important adjustment variable that could confound the association between morning discharges and patient flow.

Statistical Analysis

The study population and physician characteristics were summarized with descriptive statistics. The balance of baseline patient characteristics across morning discharge quartiles was assessed using standardized differences. A standardized difference of less than 0.1 reflects good balance.²⁰

Unadjusted estimates of patient outcomes were reported across morning discharge quartiles. To model the overall association between morning discharge and outcomes, the number of morning GIM discharges on the day of admission was subtracted from the mean number of morning discharges at each hospital and considered as a continuous exposure. We used generalized linear mixed models to estimate the effect of morning discharges on patient outcomes. We fit negative binomial regression models with log link to examine the association between the number of morning discharges (centered by subtracting the hospital mean) and the two main outcomes, ED LOS and hospital LOS. Given the overdispersion of the study population due to the unequal mean and variance, a negative binomial model was preferred over a Poisson regression, as the mean and variance were not equal.²¹ For our secondary outcomes of binary measures (30-day readmission and morality), we fit logistic regression models. Adjustment for multiple comparisons was not performed.

Multivariable analysis was conducted to adjust for the baseline characteristics described above as well as the total number of GIM discharges on the day of admission and GIM census on the day of admission. Hospital and study month (to account for secular time trends) were included as fixed effects, and patients and admitting physicians were included as crossed random effects to account for the nested structure of admissions within patients and admissions within physicians within hospitals.

A sensitivity analysis was performed to assess for nonlinear associations between morning discharges and the four outcomes (hospital LOS, ED LOS, in-hospital mortality, and readmission) by inputting the term as a restricted cubic spline, with up to five knots, in multivariable regression models. We compared the Akaike information criteria (AIC), computed using the log-likelihood, to determine the goodness-of-fit from the negative binomial models.²² We replicated models for each individual hospital to examine whether any hospital-specific associations existed. Two additional sensitivity analyses were performed to examine heterogeneity in hospital-specific effects. Regression models were fit, including interaction terms between morning discharge and hospital, as well as interaction terms between hospital and the total number of GIM discharges and GIM census. The findings of these models (data not presented) were qualitatively similar to the overall results.

Study Population and Patient Characteristics

The study population consisted of 189,781 hospitalizations involving 115,630 unique patients. The median patient age was 73 years (interquartile range [IQR], 57-84), 50.3% were female, 43.8% had a high Charlson Comorbidity Index score, and 11.1% were admitted to GIM in the prior 30 days (Table 1). The median ED LOS was 14.5 hours (IQR, 10.0-23.1), and the mean was 18.1 hours (SD, 12.2). The median hospital LOS was 4.6 days (IQR, 2.4-9.0), and the mean was 8.6 days (SD, 18.7).

In total, 36,043 (19.0%) discharges occurred between 8:00 am and 12:00 pm. The average number of total daily discharges per hospital was 8.4 (SD, 4.6), and the average number of morning discharges was 1.7 (SD, 1.4). Morning discharges varied across hospitals, ranging from 0.9 per day (SD, 1.1) to 2.1 (SD, 1.6) (Appendix Table 1). The average number of morning discharges in the lowest quartile (Q1) was 0.3 (SD, 0.5) and was 3.3 (SD, 1.2) in the highest quartile (Q4). Baseline patient characteristics were well balanced across the quartiles, with standardized differences less than 0.1 (Table 1), except day of admission and GIM census. Days with a greater number of morning discharges were more likely to be weekdays rather than weekends/holidays (89.2% weekday admissions in Q4 compared with 53.9% in Q1). The median GIM census was 93 patients (IQR, 79-109).

Outcomes

Unadjusted clinical outcomes by number of morning discharges are presented in Table 2. The median unadjusted ED LOS was 14.4 (SD, 14.1), 14.3 (SD, 13.2), 14.5 (SD, 13.0), and 14.8 (SD, 13.0) hours for the first to fourth quartiles (fewest to largest number of morning discharges), respectively. The median unadjusted hospital LOS was 4.6 (SD, 6.5), 4.6 (SD, 6.9), 4.7 (SD, 6.4), and 4.6 (SD, 6.4) days for the first to fourth quartiles, respectively.

Unadjusted inpatient mortality was 6.1%, 5.5%, 5.5%, and 5.2% across the first to fourth quartiles, respectively. Unadjusted 30-day readmission to GIM was 12.2%, 12.6%, 12.6%, and 12.5% across the first to fourth quartiles, respectively.

After multivariable adjustment, there was no significant association between morning discharge and hospital LOS (aRR, 1.000; 95% CI, 0.996-1.000; P = .997), ED LOS (aRR, 0.999; 95% CI, 0.997-1.000; P = .307), in-hospital mortality (aRR, 0.967; 95% CI, 0.920-1.020; P =.183), or 30-day readmission (aRR, 1.010; 95% CI, 0.991-1.020; P = .471) (Table 3, Appendix Table 2, Appendix Table 3, Appendix Table 4, Appendix Table 5). When examining each hospital separately, we found that morning discharge was significantly associated with hospital LOS at only one hospital (Hospital D; aRR, 0.981; 95% CI, 0.966-0.996; P = .013). Morning discharge was statistically significantly associated with ED LOS at three hospitals (A, B, and C), but the aRR was at least 0.99 in all three cases (Table 4).

In sensitivity analyses, we found no improvements in model fit when adding spline terms to the model, suggesting no significant nonlinear associations between morning discharges and the outcomes of interest.

DISCUSSION

This large multicenter cohort study found no significant overall association between the number of morning discharges and ED or hospital LOS in GIM. At one hospital, there was a 1.9% reduction in adjusted ED LOS for every additional morning discharge, but no difference in hospital LOS. We also did not observe differences in readmission or inpatient mortality associated with the number of morning discharges. Our observational findings suggest that there is unlikely to be a strong association between morning discharge and patient throughput in GIM. Given that there may be other downstream benefits of morning discharge, such as freeing beds for daytime surgeries,²³ further research is needed to determine the effectiveness of specific interventions.

Several studies have posited morning discharge as a method of improving both patient care and hospital flow metrics.^{10,11,13-15,23} Quality improvement initiatives targeting morning discharges have included stakeholder meetings, incentives programs, discharge-centered breakfast programs, and creating deadlines for discharge orders.^24-29 Although these initiatives have gained support, critics have suggested that their supporting evidence is not robust. Werthemier et al¹⁰ found a 9.0% reduction of observed to expected LOS associated with increasing the number of early discharges. However, a response article suggested that their findings were confounded by other hospital initiatives, such as allocation of medical and social services to weekends.³⁰ Other observational studies have concluded that hospital LOS is not affected by the number of morning discharges, but this research has been limited by single-center analysis and relatively smaller sample sizes.¹² Our study further calls into question the association between morning discharge and patient throughput.

An additional reason for the controversy is that physicians may actively work to discharge patients late in the day to avoid an additional night in hospital. A qualitative study by Minichiello et al³¹ evaluated staff perceptions regarding afternoon discharges. Physicians and medical students believed that afternoon discharges were a result of waiting for test results and procedures, with staff aiming to discharge patients immediately after obtaining results or finishing necessary procedures. As such, there are concerns that incentivizing morning discharge may lead physicians in the opposite direction, to consciously or unconsciously keep patients overnight in order to facilitate an early morning discharge.³⁰

Our study’s greatest strength was the large sample size over 7 years at seven hospitals in two cities, including both academic and community hospitals with different models of care. To our knowledge, this is the first cohort study that has analyzed the association between early discharge and LOS using multiple centers. To avoid the confounding and reverse causality that may exist when examining the relationship between LOS and morning discharge at the patient level (eg, patients who stay in hospital longer may have more “planned” discharges and leave in the morning), we examined the association based on variation across different days within the GIM service of each hospital. Further, we included robust risk adjustment using clinical and laboratory data. Finally, since our study included a diverse patient population served by participating centers in a system with universal insurance for hospital care, our findings are likely generalizable to other urban and suburban hospitals.

There are several important limitations of our analysis. First, we could only include GIM patients, who represent nearly 40% of ED admissions to hospital at participating centers. A more holistic analysis across all hospital services could be justified; however, given that many quality improvement initiatives occur at the level of a single hospital service, we felt our approach would be informative for future research and improvement efforts. Approximately 75% of GIM patients at participating hospitals were cared for on a GIM ward, with 25% cared for on off-service units. We were unable to include the total hospital census in our models, and this could affect LOS and waiting times for GIM patients, particularly those admitted to off-service units. GIM census is likely highly correlated with hospital census, and we were able to adjust for this. Nevertheless, this remains an important potential source of unmeasured confounding. Second, we did not model the effects of morning discharges from GIM on patient-flow measures for non-GIM patients. Given the lack of effects for GIM patients, who would be more likely to be directly affected, it is unlikely that large effects would be seen for other hospital patients, but we did not measure effects on surgical delays or cancellations, for example.²³ Third, we report 30-day readmission to GIM at participating hospitals only, rather than all readmissions. However, prior research in our region demonstrated that 82% of hospital readmissions occur to the same site.³² Thus, our measure, which includes admission to any participating hospital, likely captures more than 80% of all readmissions, and this was a secondary outcome in our analysis. Finally, qualitative metrics, such as patient or provider satisfaction, were not measured in our study. Earlier discharge may impact patient care in other ways by being more predictable for staff, improving bed allocation for daytime procedures, making medication pick-ups easier to arrange, or making consultations with allied health services more convenient.^11,28,33 Conversely, if pressured to discharge before noon, providers may feel rushed to complete tasks and may face disruptions to typical workflow.²⁴ As such, future research is needed to provide a more complete understanding of the impact of early-morning discharge beyond hospital flow. Given the lack of a strong association observed between morning discharge and patient throughput in our study, further research should also consider the opportunity costs of interventions designed to improve morning discharge.

CONCLUSION

The number of morning discharges was not significantly associated with shorter ED LOS or hospital LOS for GIM patients. Our observational findings suggest that increasing morning discharges alone may not substantially improve patient flow in GIM. Further research is needed to evaluate specific morning discharge interventions and assess hospital-wide effects.

There is substantial interest in improving patient flow and reducing hospital length of stay (LOS).^1-4 Impaired hospital flow may negatively impact both patient satisfaction and safety through, for example, emergency department (ED) overcrowding.^5,6 Impaired hospital flow is associated with downstream effects on patient care, hospital costs, and availability of beds.^7-9

A number of quality-improvement interventions aim to improve patient flow, including efforts to increase the number of discharges that occur before noon.^10,11 Morning discharges have been hypothesized to free hospital beds earlier, thus reducing ED wait times for incoming patients and increasing beds for elective surgeries.¹¹ Morning discharges may also be more predictable for staff and patients. However, it is unclear whether efforts to increase the number of morning discharges have a negative impact on inpatient LOS by incentivizing physicians to keep patients in the hospital for an extra night to facilitate discharge in the early morning rather than the late afternoon. Morning discharges have been associated with both increased¹² and decreased LOS.^10,11,13-15

The purpose of this study was to examine the associations between morning discharges and ED LOS and hospital LOS in general internal medicine (GIM) at seven hospitals. GIM patients represent nearly 40% of ED admissions to a hospital,¹⁶ and thus are an important determinant of patient flow through the ED and hospital. We hypothesized that patients who were admitted to GIM on days with more morning discharges would have shorter ED LOS and hospital LOS.

Design, Setting, and Participants

This was a retrospective cohort study conducted using the General Medicine Inpatient Initiative (GEMINI) clinical dataset.¹⁶ The dataset includes all GIM admissions at seven large hospital sites in Toronto and Mississauga, Ontario, Canada. These include five academic hospitals and two community-based teaching hospitals. Each hospital is publicly funded and provides tertiary and/or quaternary care to diverse multiethnic populations. Research ethics board approval was obtained from all participating sites.

GIM care is delivered by several interdisciplinary clinical teams functioning in parallel. Attending physicians are predominantly internists who practice as hospitalists in discrete service blocks, typically lasting 2 weeks at a time. Although GIM patients are preferentially admitted to GIM wards, participating hospitals did not have strict policies regarding cohorting GIM patients to specific wards (ie, holding patients in ED until a specific bed becomes available) that would confound the association between morning discharge and ED wait times. Approximately 75% of GIM patients are cared for on dedicated GIM wards at participating hospitals, with the remainder cared for on other medical or surgical wards.

We included all hospitalized patients who were admitted to hospital and discharged from GIM between April 1, 2010, and October 31, 2017, from the seven GEMINI hospitals. We included only patients admitted through the ED. As such, we did not include elective admissions or interfacility transfers who would not experience ED wait times. We excluded patients who were discharged without a provincial health insurance number (N = 2,169; 1.1% of total sample) because they could not be linked across visits to measure readmissions.

Data Source

The GEMINI dataset has been rigorously validated and previously described in detail.¹⁶ GEMINI collects both administrative health data reported to the Canadian Institute for Health Information (including data about patient demographics, comorbidities, and discharge destination) as well as electronic clinical data extracted from hospital computer systems (including attending physicians, in-hospital patient room transfers, and laboratory test results). Data are collected for each individual hospital encounter, and the provincial health insurance number is used to link patients across encounters.

Exposures and Outcomes

The primary exposure was the number of GIM patients discharged alive between 8:00 am and 12:00 pm (ie, morning GIM discharges) on the day of admission for each hospital encounter. This time window to define morning discharges was selected based on previous literature.^10-13 To report admission characteristics and unadjusted outcomes, hospital days were categorized into quartiles within each hospital based on the number of morning GIM discharges. Hospital days with the lowest number of morning discharges were classified into Q1 and the highest number of morning discharges into Q4, and quartiles were pooled across hospitals.

The two primary outcomes were ED LOS and hospital LOS. ED LOS was calculated as the difference between the time from triage by nursing staff to a patient’s exit from the ED, measured in hours. We also examined 30-day readmission to GIM at any participating hospital as a balancing measure against premature discharges and inpatient mortality because it could modify hospital LOS.

Patient Characteristics

Baseline patient characteristics were measured, including age, sex, Charlson Comorbidity Index score,¹⁷ day of admission (categorized as weekend/holiday or weekday), time of admission to hospital (categorized as daytime, 8:00 am to 4:59 pm, or nighttime, 5:00 pm to 07:59 am), study month, hospital site, and whether patients had been admitted to GIM in the prior 30 days. We used laboratory data to calculate the baseline Laboratory-based Acute Physiology Score (LAPS), which is a validated predictor of inpatient mortality when combined with age and Charlson Comorbidity Index score.^18,19 GIM census on day of admission was calculated in order to include overall patient volumes as an important adjustment variable that could confound the association between morning discharges and patient flow.

Statistical Analysis

The study population and physician characteristics were summarized with descriptive statistics. The balance of baseline patient characteristics across morning discharge quartiles was assessed using standardized differences. A standardized difference of less than 0.1 reflects good balance.²⁰

Unadjusted estimates of patient outcomes were reported across morning discharge quartiles. To model the overall association between morning discharge and outcomes, the number of morning GIM discharges on the day of admission was subtracted from the mean number of morning discharges at each hospital and considered as a continuous exposure. We used generalized linear mixed models to estimate the effect of morning discharges on patient outcomes. We fit negative binomial regression models with log link to examine the association between the number of morning discharges (centered by subtracting the hospital mean) and the two main outcomes, ED LOS and hospital LOS. Given the overdispersion of the study population due to the unequal mean and variance, a negative binomial model was preferred over a Poisson regression, as the mean and variance were not equal.²¹ For our secondary outcomes of binary measures (30-day readmission and morality), we fit logistic regression models. Adjustment for multiple comparisons was not performed.

Multivariable analysis was conducted to adjust for the baseline characteristics described above as well as the total number of GIM discharges on the day of admission and GIM census on the day of admission. Hospital and study month (to account for secular time trends) were included as fixed effects, and patients and admitting physicians were included as crossed random effects to account for the nested structure of admissions within patients and admissions within physicians within hospitals.

A sensitivity analysis was performed to assess for nonlinear associations between morning discharges and the four outcomes (hospital LOS, ED LOS, in-hospital mortality, and readmission) by inputting the term as a restricted cubic spline, with up to five knots, in multivariable regression models. We compared the Akaike information criteria (AIC), computed using the log-likelihood, to determine the goodness-of-fit from the negative binomial models.²² We replicated models for each individual hospital to examine whether any hospital-specific associations existed. Two additional sensitivity analyses were performed to examine heterogeneity in hospital-specific effects. Regression models were fit, including interaction terms between morning discharge and hospital, as well as interaction terms between hospital and the total number of GIM discharges and GIM census. The findings of these models (data not presented) were qualitatively similar to the overall results.

Study Population and Patient Characteristics

The study population consisted of 189,781 hospitalizations involving 115,630 unique patients. The median patient age was 73 years (interquartile range [IQR], 57-84), 50.3% were female, 43.8% had a high Charlson Comorbidity Index score, and 11.1% were admitted to GIM in the prior 30 days (Table 1). The median ED LOS was 14.5 hours (IQR, 10.0-23.1), and the mean was 18.1 hours (SD, 12.2). The median hospital LOS was 4.6 days (IQR, 2.4-9.0), and the mean was 8.6 days (SD, 18.7).

In total, 36,043 (19.0%) discharges occurred between 8:00 am and 12:00 pm. The average number of total daily discharges per hospital was 8.4 (SD, 4.6), and the average number of morning discharges was 1.7 (SD, 1.4). Morning discharges varied across hospitals, ranging from 0.9 per day (SD, 1.1) to 2.1 (SD, 1.6) (Appendix Table 1). The average number of morning discharges in the lowest quartile (Q1) was 0.3 (SD, 0.5) and was 3.3 (SD, 1.2) in the highest quartile (Q4). Baseline patient characteristics were well balanced across the quartiles, with standardized differences less than 0.1 (Table 1), except day of admission and GIM census. Days with a greater number of morning discharges were more likely to be weekdays rather than weekends/holidays (89.2% weekday admissions in Q4 compared with 53.9% in Q1). The median GIM census was 93 patients (IQR, 79-109).

Outcomes

Unadjusted clinical outcomes by number of morning discharges are presented in Table 2. The median unadjusted ED LOS was 14.4 (SD, 14.1), 14.3 (SD, 13.2), 14.5 (SD, 13.0), and 14.8 (SD, 13.0) hours for the first to fourth quartiles (fewest to largest number of morning discharges), respectively. The median unadjusted hospital LOS was 4.6 (SD, 6.5), 4.6 (SD, 6.9), 4.7 (SD, 6.4), and 4.6 (SD, 6.4) days for the first to fourth quartiles, respectively.

Unadjusted inpatient mortality was 6.1%, 5.5%, 5.5%, and 5.2% across the first to fourth quartiles, respectively. Unadjusted 30-day readmission to GIM was 12.2%, 12.6%, 12.6%, and 12.5% across the first to fourth quartiles, respectively.

After multivariable adjustment, there was no significant association between morning discharge and hospital LOS (aRR, 1.000; 95% CI, 0.996-1.000; P = .997), ED LOS (aRR, 0.999; 95% CI, 0.997-1.000; P = .307), in-hospital mortality (aRR, 0.967; 95% CI, 0.920-1.020; P =.183), or 30-day readmission (aRR, 1.010; 95% CI, 0.991-1.020; P = .471) (Table 3, Appendix Table 2, Appendix Table 3, Appendix Table 4, Appendix Table 5). When examining each hospital separately, we found that morning discharge was significantly associated with hospital LOS at only one hospital (Hospital D; aRR, 0.981; 95% CI, 0.966-0.996; P = .013). Morning discharge was statistically significantly associated with ED LOS at three hospitals (A, B, and C), but the aRR was at least 0.99 in all three cases (Table 4).

In sensitivity analyses, we found no improvements in model fit when adding spline terms to the model, suggesting no significant nonlinear associations between morning discharges and the outcomes of interest.

DISCUSSION

This large multicenter cohort study found no significant overall association between the number of morning discharges and ED or hospital LOS in GIM. At one hospital, there was a 1.9% reduction in adjusted ED LOS for every additional morning discharge, but no difference in hospital LOS. We also did not observe differences in readmission or inpatient mortality associated with the number of morning discharges. Our observational findings suggest that there is unlikely to be a strong association between morning discharge and patient throughput in GIM. Given that there may be other downstream benefits of morning discharge, such as freeing beds for daytime surgeries,²³ further research is needed to determine the effectiveness of specific interventions.

Several studies have posited morning discharge as a method of improving both patient care and hospital flow metrics.^{10,11,13-15,23} Quality improvement initiatives targeting morning discharges have included stakeholder meetings, incentives programs, discharge-centered breakfast programs, and creating deadlines for discharge orders.^24-29 Although these initiatives have gained support, critics have suggested that their supporting evidence is not robust. Werthemier et al¹⁰ found a 9.0% reduction of observed to expected LOS associated with increasing the number of early discharges. However, a response article suggested that their findings were confounded by other hospital initiatives, such as allocation of medical and social services to weekends.³⁰ Other observational studies have concluded that hospital LOS is not affected by the number of morning discharges, but this research has been limited by single-center analysis and relatively smaller sample sizes.¹² Our study further calls into question the association between morning discharge and patient throughput.

An additional reason for the controversy is that physicians may actively work to discharge patients late in the day to avoid an additional night in hospital. A qualitative study by Minichiello et al³¹ evaluated staff perceptions regarding afternoon discharges. Physicians and medical students believed that afternoon discharges were a result of waiting for test results and procedures, with staff aiming to discharge patients immediately after obtaining results or finishing necessary procedures. As such, there are concerns that incentivizing morning discharge may lead physicians in the opposite direction, to consciously or unconsciously keep patients overnight in order to facilitate an early morning discharge.³⁰

Our study’s greatest strength was the large sample size over 7 years at seven hospitals in two cities, including both academic and community hospitals with different models of care. To our knowledge, this is the first cohort study that has analyzed the association between early discharge and LOS using multiple centers. To avoid the confounding and reverse causality that may exist when examining the relationship between LOS and morning discharge at the patient level (eg, patients who stay in hospital longer may have more “planned” discharges and leave in the morning), we examined the association based on variation across different days within the GIM service of each hospital. Further, we included robust risk adjustment using clinical and laboratory data. Finally, since our study included a diverse patient population served by participating centers in a system with universal insurance for hospital care, our findings are likely generalizable to other urban and suburban hospitals.

There are several important limitations of our analysis. First, we could only include GIM patients, who represent nearly 40% of ED admissions to hospital at participating centers. A more holistic analysis across all hospital services could be justified; however, given that many quality improvement initiatives occur at the level of a single hospital service, we felt our approach would be informative for future research and improvement efforts. Approximately 75% of GIM patients at participating hospitals were cared for on a GIM ward, with 25% cared for on off-service units. We were unable to include the total hospital census in our models, and this could affect LOS and waiting times for GIM patients, particularly those admitted to off-service units. GIM census is likely highly correlated with hospital census, and we were able to adjust for this. Nevertheless, this remains an important potential source of unmeasured confounding. Second, we did not model the effects of morning discharges from GIM on patient-flow measures for non-GIM patients. Given the lack of effects for GIM patients, who would be more likely to be directly affected, it is unlikely that large effects would be seen for other hospital patients, but we did not measure effects on surgical delays or cancellations, for example.²³ Third, we report 30-day readmission to GIM at participating hospitals only, rather than all readmissions. However, prior research in our region demonstrated that 82% of hospital readmissions occur to the same site.³² Thus, our measure, which includes admission to any participating hospital, likely captures more than 80% of all readmissions, and this was a secondary outcome in our analysis. Finally, qualitative metrics, such as patient or provider satisfaction, were not measured in our study. Earlier discharge may impact patient care in other ways by being more predictable for staff, improving bed allocation for daytime procedures, making medication pick-ups easier to arrange, or making consultations with allied health services more convenient.^11,28,33 Conversely, if pressured to discharge before noon, providers may feel rushed to complete tasks and may face disruptions to typical workflow.²⁴ As such, future research is needed to provide a more complete understanding of the impact of early-morning discharge beyond hospital flow. Given the lack of a strong association observed between morning discharge and patient throughput in our study, further research should also consider the opportunity costs of interventions designed to improve morning discharge.

CONCLUSION

The number of morning discharges was not significantly associated with shorter ED LOS or hospital LOS for GIM patients. Our observational findings suggest that increasing morning discharges alone may not substantially improve patient flow in GIM. Further research is needed to evaluate specific morning discharge interventions and assess hospital-wide effects.

Nearly 48,000 Americans died from overdoses involving opioids in 2018, continuing a national crisis that has led to 446,000 deaths since 1999.¹ Annually, opioids are responsible for more than 500,000 admissions, approximately 1% of all hospitalizations, costing the United States nearly $15 billion.^2,3 Among hospitalized patients, chronic opioid use is associated with increased mortality, severe infectious complications, and higher rates of readmission.⁴ Opioid use disorder (OUD) is a chronic, relapsing medical condition with biopsychosocial origins and significant morbidity and mortality.⁵ Opioid agonist therapy (OAT) with buprenorphine or methadone maintenance, the evidence-based standard of treatment, reduces the mortality rate by half, decreases overdoses and hospital readmissions, and improves retention in care.^6-10

OAT maintenance refers to using buprenorphine or methadone for long-term treatment of OUD rather than for acute treatment of opioid withdrawal. Despite evidence supporting OAT maintenance, clinicians start medications for only 11% to 15% of hospitalized patients with OUD, depending on practice contexts.^11,12 Three significant barriers—stigma, insufficient clinician education, and restrictive regulations—prevent clinicians from starting OAT.¹³ Clinicians who do not have the Drug Enforcement Administration (DEA)–issued DATA-2000 waiver (X-waiver) for outpatient prescribing can order buprenorphine for admitted patients but cannot prescribe it at discharge.¹⁴ In hospitals where they exist, addiction medicine consult services offer primary teams guidance on pharmacotherapy, leading to reduced hospital readmissions and increased engagement in outpatient addiction treatment.^15-17 However, in most hospitals around the country, such specialty services do not exist.¹⁸ In some hospitals without addiction medicine consult services, hospitalists with expertise in OUD have started assisting primary teams in starting OAT, but to our knowledge, no prior studies have described the impact of these interventions on patients or clinician experience with OAT.¹⁹

This quality improvement project aimed to increase the rate at which internal medicine resident teams at Johns Hopkins Hospital (JHH) in Baltimore, Maryland, started hospitalized patients with OUD on buprenorphine maintenance. We hypothesized that resident education and measures to increase the availability of X-waivered physicians would increase the rate of initiating buprenorphine maintenance. We additionally hypothesized that these interventions would increase knowledge about and comfort with buprenorphine across the residency. This represents the first study to examine the effects of clinician education and a team of X-waivered residents and hospitalists who assist in starting buprenorphine maintenance in a hospital without an addiction medicine consult service.

Setting

This study took place from July 2018 to June 2019 at JHH, a large, academic, urban hospital in Baltimore. Prior to the intervention, internal medicine residents at JHH commonly used short courses of buprenorphine to treat withdrawal, but they did not have access to hospital-specific resources to assist with starting maintenance OAT. During the study period, JHH had a Substance Use Disorders team staffed by peer recovery specialists that could be consulted by hospitalists and residents to provide psychosocial support and link admitted patients to treatment after discharge. There were no providers on the team to guide pharmacotherapy or to write discharge buprenorphine prescriptions. The Osler Medical Residency Training Program at JHH has 140 internal medicine residents and 16 combined medicine-pediatrics residents. All residents receive 1 hour of formal education about opioid use disorder annually. In addition, 28 of those 156 residents, those in the Urban Health Primary Care track, spend 1 month on an Addiction Medicine rotation in which they complete the 8-hour training required to receive the X-waiver. Those residents are encouraged to apply for the X-waiver once they obtain a medical license subsidized by a Health Resources & Services Administration (HRSA) grant. Four internal medicine attending physicians on teaching services and one resident had X-waivers prior to the intervention.

Intervention

In November 2018, we administered a survey to residents to identify barriers to starting buprenorphine maintenance and to measure knowledge and confidence with using buprenorphine for OUD (Appendix Figure 1 and Figure 2). We focused on buprenorphine because providers at JHH were familiar with this medication and because Baltimore has widespread access to buprenorphine, with more than 490 local buprenorphine providers.²⁰ Five residents piloted the survey and provided feedback. We then administered the survey to all internal medicine and medicine-pediatrics residents. Based on the results, we developed a targeted educational conference and also created the Buprenorphine Bridge Team (BBT).

In January 2019, we presented the educational conference for residents devoted to the use of buprenorphine for OUD and introduced the BBT. The conference started with a patient testimonial and included peer recovery specialists, pharmacists, nurses, and social workers. We summarized the evidence for buprenorphine and offered a practical guide to start treatment in a one-page protocol. This protocol included guidance on selecting patients, shared decision-making around OUD treatment, avoiding precipitated withdrawal, dosing buprenorphine, and establishing follow-up (Appendix Figure 3). We asked for input on this protocol from nursing leadership, social work teams, and peer recovery specialists. Dosing was adapted from the Guidelines from the American Society of Addiction Medicine, with expert input from physicians from the Addiction Medicine Consult service at Johns Hopkins Bayview Medical Center, also in Baltimore.⁵ We instructed residents to obtain discharge buprenorphine prescriptions from an X-waivered physician on their team or from the newly established BBT. We asked resident teams to set up a postdischarge appointment for patients with an X-waivered provider, either in a community practice or at the JHH After Care Clinic, a transitional care clinic for discharged patients.²¹

The BBT is a resident-led group of X-waivered JHH residents and hospitalists who volunteer to write discharge buprenorphine prescriptions for patients. The BBT serves to ensure primary teams have access to an X-waivered prescriber. It is not a consult service. We asked primary teams to contact the BBT after initiating buprenorphine and after securing a follow-up appointment. In response to each request, a member of the BBT reviews the patient chart, confirms the follow-up plan, writes a prescription for buprenorphine along with intranasal naloxone, and leaves a brief note. During the 6-month postintervention period, the team consisted of three residents and three hospitalist attendings. Each week, two members (residents or attendings) staffed the team Monday to Friday, 8 am to 5 pm. Most weeks were staffed by two residents. One resident provided services after hours and during weekends. Resident team members ensured that the buprenorphine plan was discussed with the primary team’s attending. For dosing questions beyond the BBT’s scope of knowledge, a member of the BBT relayed questions to physicians from the Addiction Medicine Consult team at Johns Hopkins Bayview Medical Center.

In May 2019, 5 months after the education session and implementation of the BBT, we administered a follow-up survey.

Outcomes

The primary outcome was the percent of inpatients eligible to start OAT who were discharged on buprenorphine maintenance. We obtained data from the electronic medical record. The denominator consisted of patients with OUD not on buprenorphine or methadone maintenance on admission. We identified patients with OUD by an opioid-related International Classification of Diseases, Tenth Revision (ICD-10) diagnosis code or by a standing or as-needed order for buprenorphine or methadone during hospitalization.²² We reviewed admission and discharge documentation to identify patients with OUD who were not in active treatment with buprenorphine or methadone maintenance.

As a secondary outcome, we measured engagement in OUD treatment after discharge by calculating the proportion of patients started on buprenorphine who filled a buprenorphine prescription within 30 days after discharge. We chose 30 days based on the National Committee for Quality Assurance’s Healthcare Effectiveness Data and Information Set (HEDIS) measure for engagement of treatment for alcohol and other drugs.²³ We obtained the data from the Chesapeake Regional Information System for our Patients (CRISP) Prescription Drug Monitoring Program, which monitors all prescriptions for controlled substances dispensed in Maryland and five neighboring states. As a balancing measure, we counted patients newly started on methadone maintenance for OUD before and after the intervention. Additional secondary process outcomes included frequency of BBT requests, the volume of buprenorphine prescriptions written by the team, and time required to complete a BBT request.

Clinician-level outcomes, measured with electronically administered pre- and postintervention surveys to residents, included knowledge about and comfort with buprenorphine. Of the 16 questions in the pre- and postimplementation surveys, we analyzed the 6 questions concerning knowledge and comfort that remained identical in the pre- and postintervention surveys and used 5-point Likert scale responses. As an incentive, we randomly distributed three $50 gift cards to survey completers.

Analysis

We used an interrupted time series analysis to evaluate the association between the intervention bundle and a change in the rate that medical teams started patients with OUD on buprenorphine maintenance. This approach allowed us to control for preintervention trends. To evaluate the impact of our interventions, our pre- and postintervention periods include the same residents during the 2018-2019 academic year. Both periods consisted of twelve 2-week intervals (preintervention: July 26, 2019, to January 9, 2019; postintervention: January 10, 2019, to June 26, 2019).

To evaluate for changes in engagement in OUD treatment after discharge, we used two-sample t tests. To evaluate for changes in resident-reported comfort and knowledge with initiating buprenorphine maintenance, we used Wilcoxon rank sum tests for survey data and Wilcoxon signed rank tests for paired data. All analyses employed two-sided P values with statistical significance evaluated at the .05 alpha level. We analyzed data using R version 3.6.3 (Foundation for Statistical Computing). The Institutional Review Board at JHH reviewed and approved the study protocol as a quality improvement project (IRB00193365).

During the 24-week preintervention period, internal medicine resident teams started 30 out of 305 eligible patients (10%) on buprenorphine maintenance vs 64 out of 270 eligible patients (24%) during the 24-week postintervention period. Our interrupted time series analysis showed a significant increase in the percent of eligible patients started on buprenorphine maintenance (expected number of patients started postintervention, 27; actual, 64; absolute increase in percent, 14.4%; 95% CI, 3.6%-25.3%; P = .017) (Figure). There was no significant trend during the preintervention period and no significant trend during the postintervention period.

Before the intervention, 13 of the 30 patients (40%) newly started on buprenorphine maintenance during their admission filled a follow-up buprenorphine prescription within 30 days of discharge. After the intervention, 31 of 64 patients (46%) filled a buprenorphine prescription within 30 days (P = .612). Two patients were started on methadone maintenance, one prior to and one after the intervention.

During the 6-month postintervention period, the BBT received 75 requests and wrote 70 prescriptions for buprenorphine. The median time required to complete a BBT request was 15 minutes (minimum, 5 minutes; maximum, 60 minutes).

Of 156 internal medicine and medicine-pediatrics residents, 89 residents (57%) completed the baseline survey and 66 residents (42%) completed the follow-up survey. Forty residents completed both surveys. After the intervention, residents were significantly more likely to feel comfortable dosing buprenorphine (P < .0001) and counseling patients about its use (P = .0237) and were more likely to report ease of establishing follow-up (P < .0001). Self-reported knowledge about preventing precipitated withdrawal increased significantly (P = .0191), as did knowledge about the effectiveness of buprenorphine (P = .0003) independent of formal drug counseling (P = .0066) (Table). Paired survey data also found statistically significant results for all questions except those about preventing precipitated withdrawal and efficacy. For the latter, respondents who completed both surveys were more knowledgeable before the intervention than the overall group that completed the baseline survey (Appendix Table).

This study shows how a resident-led quality improvement project comprising clinician education and implementation of a novel BBT was associated with an increased rate of starting buprenorphine maintenance in hospitalized patients with OUD and improved resident knowledge about and comfort with buprenorphine. To our knowledge, this is the first study demonstrating how education and a team of X-waivered generalists can help primary teams initiate and discharge patients on buprenorphine maintenance in a hospital without an addiction medicine consult service.

Prior to the intervention, resident internal medicine teams at JHH started 10% of hospitalized patients with OUD on buprenorphine maintenance, consistent with prior studies showing rates of 11% to 15% for initiating OAT for hospitalized patients.^11,12 After the intervention, the rate of initiating buprenorphine maintenance more than doubled, rising to 24% of eligible patients. Resident internal medicine teams at JHH started buprenorphine maintenance for 37 more patients over the 24-week postintervention period than would have been predicted prior to the intervention, or an additional three patients every 2 weeks.

Between 40% and 46% of hospitalized patients newly started on buprenorphine maintenance filled an outpatient buprenorphine prescription within 30 days of discharge. We are not aware of comparative data for 30-day follow-up for hospitalized patients newly started on buprenorphine maintenance. Data from other contexts show 5% to 10% of veterans were engaged in addiction treatment 30 days after initiation from inpatient or outpatient encounters. An analysis of an academic medical center in Oregon found engagement with an addiction medicine consult service increased after hospital engagement for patients with any substance use disorder from 23% to 39% using the 34-day HEDIS measure for engagement.^17,24,25

The BBT required approximately 15 minutes per request and wrote an average of three prescriptions per week, demonstrating the feasibility of this approach and the high demand for this service. One strength of our approach is that residents gained experience starting buprenorphine independently using the aforementioned protocol instead of deferring to a full consult service. It is likely that this resident engagement in initiating longitudinal OUD care contributed to the success of this initiative, as did existing resident familiarity with using buprenorphine for opioid withdrawal.

This approach to resident education—promoting direct, first-person experience with medications in a clinical context—aligns with recommendations from a recent review about substance use disorder education for health professionals.²⁶ Our interventions increased resident knowledge and comfort with buprenorphine, consistent with prior studies showing increased resident confidence in management of substance use disorders after curricular innovations.^24,25

A few contextual features were essential for this project’s viability. Maryland allows American medical graduates to obtain a medical license after 1 year of postgraduate training. This allowed three residents to obtain X-waivers. These residents had access to HRSA funding to subsidize the expenses of applying for state licensure and DEA registration. BBT members volunteered their time while working on other services. Last, we were able to take advantage of buprenorphine-providing clinics in Baltimore, including the JHH After Care Clinic, to accept patients for follow-up appointments after discharge.

Limitations

The BBT required motivated clinicians willing to volunteer for additional clinical responsibilities during inpatient rotations and supportive faculty and residency leadership. Attending physicians, nurse practitioners, or physician assistants could staff a similar BBT in hospitals without residents or in hospitals where residents cannot obtain DEA registration. Crucially, other hospitals may not have access to practices with X-waivered physicians for outpatient follow-up. A recent study found X-waivered primary care physicians were less likely to be affiliated with hospital health systems. Other studies have shown limitations in access to buprenorphine at the county level based on geography and racial/ethnic segregation.^27-29

Most patients hospitalized with OUD did not have ICD-10 codes associated with OUD. We addressed this by assuming patients had OUD if buprenorphine or methadone was ordered during their hospitalization, even if the medication was never administered. This may have overcounted patients prescribed these medications for indications other than OUD, and it may have undercounted patients with OUD for whom buprenorphine or methadone were never considered. The opioid withdrawal order set at JHH automatically offers an option to use buprenorphine to treat withdrawal. Patients with OUD for whom buprenorphine or methadone were never ordered likely did not experience withdrawal or were in withdrawal so mild that it escaped the attention of the team, which limits the generalizability of our intervention.

We identified several limitations to the internal validity of our study. First, we used a before-and-after study design without a control group. We could not ethically withhold access to evidence-based, mortality-reducing medications from patients. Without a control group, we cannot rule out the possibility that underlying temporal trends made residents more likely to start buprenorphine maintenance independent of our intervention. We attempted to control for unmeasured confounders by using an interrupted time series analysis to control for preintervention trends, comparing the same group of residents before and after our interventions, and selecting an intervention period during which residents were given only educational sessions and materials provided by our team. Our results may be biased by clustered data because certain residents may have been more likely to initiate buprenorphine, but these effects are likely marginal because resident schedules are balanced between outpatient and inpatient rotations during each 6-month period.

Finally, this project focused on buprenorphine, not on other medications for OUD, including methadone or naltrexone, or nonpharmacologic treatments for OUD.

Since the start of the BBT in January 2019, five additional PGY-2 residents obtained their medical licenses and X-waivers. These residents, with the support of two attending hospitalists, led the BBT and coordinated education sessions that were incorporated into the curriculum during the 2019-2020 academic year. These educational sessions will continue indefinitely. In 2020, JHH started an Addiction Medicine Consult Service staffed by physicians, NPs, and a pharmacist. The BBT continues to operate in conjunction with this service.

We found substantial variability in the rate of buprenorphine maintenance initiation despite our interventions. This is an area for future improvement. In a free-response prompt in our follow-up survey, residents requested additional education sessions and an order set to assist with initiation of buprenorphine. To address these gaps, three educational sessions were added, one of which included education on starting methadone maintenance therapy. We also added a new order set for starting buprenorphine maintenance. We hypothesize that these interventions will improve consistency.

In order for a similar program to be disseminated to other institutions, educational initiatives and a team of dedicated X-waivered prescribers are key. Materials to assist with this process are available in the Appendix.

This study shows how a resident-led intervention comprising clinician education and a team of X-waivered generalists was associated with improved treatment of OUD for hospitalized patients. We encourage residents and all clinicians at other hospitals without addiction medicine consult services to design, implement, and study similar interventions that directly increase the use of buprenorphine or methadone maintenance to treat OUD.

Preliminary results from this project were presented at the AMERSA National Conference on November 7, 2019.

Nearly 48,000 Americans died from overdoses involving opioids in 2018, continuing a national crisis that has led to 446,000 deaths since 1999.¹ Annually, opioids are responsible for more than 500,000 admissions, approximately 1% of all hospitalizations, costing the United States nearly $15 billion.^2,3 Among hospitalized patients, chronic opioid use is associated with increased mortality, severe infectious complications, and higher rates of readmission.⁴ Opioid use disorder (OUD) is a chronic, relapsing medical condition with biopsychosocial origins and significant morbidity and mortality.⁵ Opioid agonist therapy (OAT) with buprenorphine or methadone maintenance, the evidence-based standard of treatment, reduces the mortality rate by half, decreases overdoses and hospital readmissions, and improves retention in care.^6-10

OAT maintenance refers to using buprenorphine or methadone for long-term treatment of OUD rather than for acute treatment of opioid withdrawal. Despite evidence supporting OAT maintenance, clinicians start medications for only 11% to 15% of hospitalized patients with OUD, depending on practice contexts.^11,12 Three significant barriers—stigma, insufficient clinician education, and restrictive regulations—prevent clinicians from starting OAT.¹³ Clinicians who do not have the Drug Enforcement Administration (DEA)–issued DATA-2000 waiver (X-waiver) for outpatient prescribing can order buprenorphine for admitted patients but cannot prescribe it at discharge.¹⁴ In hospitals where they exist, addiction medicine consult services offer primary teams guidance on pharmacotherapy, leading to reduced hospital readmissions and increased engagement in outpatient addiction treatment.^15-17 However, in most hospitals around the country, such specialty services do not exist.¹⁸ In some hospitals without addiction medicine consult services, hospitalists with expertise in OUD have started assisting primary teams in starting OAT, but to our knowledge, no prior studies have described the impact of these interventions on patients or clinician experience with OAT.¹⁹

This quality improvement project aimed to increase the rate at which internal medicine resident teams at Johns Hopkins Hospital (JHH) in Baltimore, Maryland, started hospitalized patients with OUD on buprenorphine maintenance. We hypothesized that resident education and measures to increase the availability of X-waivered physicians would increase the rate of initiating buprenorphine maintenance. We additionally hypothesized that these interventions would increase knowledge about and comfort with buprenorphine across the residency. This represents the first study to examine the effects of clinician education and a team of X-waivered residents and hospitalists who assist in starting buprenorphine maintenance in a hospital without an addiction medicine consult service.

Setting

This study took place from July 2018 to June 2019 at JHH, a large, academic, urban hospital in Baltimore. Prior to the intervention, internal medicine residents at JHH commonly used short courses of buprenorphine to treat withdrawal, but they did not have access to hospital-specific resources to assist with starting maintenance OAT. During the study period, JHH had a Substance Use Disorders team staffed by peer recovery specialists that could be consulted by hospitalists and residents to provide psychosocial support and link admitted patients to treatment after discharge. There were no providers on the team to guide pharmacotherapy or to write discharge buprenorphine prescriptions. The Osler Medical Residency Training Program at JHH has 140 internal medicine residents and 16 combined medicine-pediatrics residents. All residents receive 1 hour of formal education about opioid use disorder annually. In addition, 28 of those 156 residents, those in the Urban Health Primary Care track, spend 1 month on an Addiction Medicine rotation in which they complete the 8-hour training required to receive the X-waiver. Those residents are encouraged to apply for the X-waiver once they obtain a medical license subsidized by a Health Resources & Services Administration (HRSA) grant. Four internal medicine attending physicians on teaching services and one resident had X-waivers prior to the intervention.

Intervention

In November 2018, we administered a survey to residents to identify barriers to starting buprenorphine maintenance and to measure knowledge and confidence with using buprenorphine for OUD (Appendix Figure 1 and Figure 2). We focused on buprenorphine because providers at JHH were familiar with this medication and because Baltimore has widespread access to buprenorphine, with more than 490 local buprenorphine providers.²⁰ Five residents piloted the survey and provided feedback. We then administered the survey to all internal medicine and medicine-pediatrics residents. Based on the results, we developed a targeted educational conference and also created the Buprenorphine Bridge Team (BBT).

In January 2019, we presented the educational conference for residents devoted to the use of buprenorphine for OUD and introduced the BBT. The conference started with a patient testimonial and included peer recovery specialists, pharmacists, nurses, and social workers. We summarized the evidence for buprenorphine and offered a practical guide to start treatment in a one-page protocol. This protocol included guidance on selecting patients, shared decision-making around OUD treatment, avoiding precipitated withdrawal, dosing buprenorphine, and establishing follow-up (Appendix Figure 3). We asked for input on this protocol from nursing leadership, social work teams, and peer recovery specialists. Dosing was adapted from the Guidelines from the American Society of Addiction Medicine, with expert input from physicians from the Addiction Medicine Consult service at Johns Hopkins Bayview Medical Center, also in Baltimore.⁵ We instructed residents to obtain discharge buprenorphine prescriptions from an X-waivered physician on their team or from the newly established BBT. We asked resident teams to set up a postdischarge appointment for patients with an X-waivered provider, either in a community practice or at the JHH After Care Clinic, a transitional care clinic for discharged patients.²¹

The BBT is a resident-led group of X-waivered JHH residents and hospitalists who volunteer to write discharge buprenorphine prescriptions for patients. The BBT serves to ensure primary teams have access to an X-waivered prescriber. It is not a consult service. We asked primary teams to contact the BBT after initiating buprenorphine and after securing a follow-up appointment. In response to each request, a member of the BBT reviews the patient chart, confirms the follow-up plan, writes a prescription for buprenorphine along with intranasal naloxone, and leaves a brief note. During the 6-month postintervention period, the team consisted of three residents and three hospitalist attendings. Each week, two members (residents or attendings) staffed the team Monday to Friday, 8 am to 5 pm. Most weeks were staffed by two residents. One resident provided services after hours and during weekends. Resident team members ensured that the buprenorphine plan was discussed with the primary team’s attending. For dosing questions beyond the BBT’s scope of knowledge, a member of the BBT relayed questions to physicians from the Addiction Medicine Consult team at Johns Hopkins Bayview Medical Center.

In May 2019, 5 months after the education session and implementation of the BBT, we administered a follow-up survey.

Outcomes

The primary outcome was the percent of inpatients eligible to start OAT who were discharged on buprenorphine maintenance. We obtained data from the electronic medical record. The denominator consisted of patients with OUD not on buprenorphine or methadone maintenance on admission. We identified patients with OUD by an opioid-related International Classification of Diseases, Tenth Revision (ICD-10) diagnosis code or by a standing or as-needed order for buprenorphine or methadone during hospitalization.²² We reviewed admission and discharge documentation to identify patients with OUD who were not in active treatment with buprenorphine or methadone maintenance.

As a secondary outcome, we measured engagement in OUD treatment after discharge by calculating the proportion of patients started on buprenorphine who filled a buprenorphine prescription within 30 days after discharge. We chose 30 days based on the National Committee for Quality Assurance’s Healthcare Effectiveness Data and Information Set (HEDIS) measure for engagement of treatment for alcohol and other drugs.²³ We obtained the data from the Chesapeake Regional Information System for our Patients (CRISP) Prescription Drug Monitoring Program, which monitors all prescriptions for controlled substances dispensed in Maryland and five neighboring states. As a balancing measure, we counted patients newly started on methadone maintenance for OUD before and after the intervention. Additional secondary process outcomes included frequency of BBT requests, the volume of buprenorphine prescriptions written by the team, and time required to complete a BBT request.

Clinician-level outcomes, measured with electronically administered pre- and postintervention surveys to residents, included knowledge about and comfort with buprenorphine. Of the 16 questions in the pre- and postimplementation surveys, we analyzed the 6 questions concerning knowledge and comfort that remained identical in the pre- and postintervention surveys and used 5-point Likert scale responses. As an incentive, we randomly distributed three $50 gift cards to survey completers.

Analysis

We used an interrupted time series analysis to evaluate the association between the intervention bundle and a change in the rate that medical teams started patients with OUD on buprenorphine maintenance. This approach allowed us to control for preintervention trends. To evaluate the impact of our interventions, our pre- and postintervention periods include the same residents during the 2018-2019 academic year. Both periods consisted of twelve 2-week intervals (preintervention: July 26, 2019, to January 9, 2019; postintervention: January 10, 2019, to June 26, 2019).

To evaluate for changes in engagement in OUD treatment after discharge, we used two-sample t tests. To evaluate for changes in resident-reported comfort and knowledge with initiating buprenorphine maintenance, we used Wilcoxon rank sum tests for survey data and Wilcoxon signed rank tests for paired data. All analyses employed two-sided P values with statistical significance evaluated at the .05 alpha level. We analyzed data using R version 3.6.3 (Foundation for Statistical Computing). The Institutional Review Board at JHH reviewed and approved the study protocol as a quality improvement project (IRB00193365).

During the 24-week preintervention period, internal medicine resident teams started 30 out of 305 eligible patients (10%) on buprenorphine maintenance vs 64 out of 270 eligible patients (24%) during the 24-week postintervention period. Our interrupted time series analysis showed a significant increase in the percent of eligible patients started on buprenorphine maintenance (expected number of patients started postintervention, 27; actual, 64; absolute increase in percent, 14.4%; 95% CI, 3.6%-25.3%; P = .017) (Figure). There was no significant trend during the preintervention period and no significant trend during the postintervention period.

Before the intervention, 13 of the 30 patients (40%) newly started on buprenorphine maintenance during their admission filled a follow-up buprenorphine prescription within 30 days of discharge. After the intervention, 31 of 64 patients (46%) filled a buprenorphine prescription within 30 days (P = .612). Two patients were started on methadone maintenance, one prior to and one after the intervention.

During the 6-month postintervention period, the BBT received 75 requests and wrote 70 prescriptions for buprenorphine. The median time required to complete a BBT request was 15 minutes (minimum, 5 minutes; maximum, 60 minutes).

Of 156 internal medicine and medicine-pediatrics residents, 89 residents (57%) completed the baseline survey and 66 residents (42%) completed the follow-up survey. Forty residents completed both surveys. After the intervention, residents were significantly more likely to feel comfortable dosing buprenorphine (P < .0001) and counseling patients about its use (P = .0237) and were more likely to report ease of establishing follow-up (P < .0001). Self-reported knowledge about preventing precipitated withdrawal increased significantly (P = .0191), as did knowledge about the effectiveness of buprenorphine (P = .0003) independent of formal drug counseling (P = .0066) (Table). Paired survey data also found statistically significant results for all questions except those about preventing precipitated withdrawal and efficacy. For the latter, respondents who completed both surveys were more knowledgeable before the intervention than the overall group that completed the baseline survey (Appendix Table).

This study shows how a resident-led quality improvement project comprising clinician education and implementation of a novel BBT was associated with an increased rate of starting buprenorphine maintenance in hospitalized patients with OUD and improved resident knowledge about and comfort with buprenorphine. To our knowledge, this is the first study demonstrating how education and a team of X-waivered generalists can help primary teams initiate and discharge patients on buprenorphine maintenance in a hospital without an addiction medicine consult service.

Prior to the intervention, resident internal medicine teams at JHH started 10% of hospitalized patients with OUD on buprenorphine maintenance, consistent with prior studies showing rates of 11% to 15% for initiating OAT for hospitalized patients.^11,12 After the intervention, the rate of initiating buprenorphine maintenance more than doubled, rising to 24% of eligible patients. Resident internal medicine teams at JHH started buprenorphine maintenance for 37 more patients over the 24-week postintervention period than would have been predicted prior to the intervention, or an additional three patients every 2 weeks.

Between 40% and 46% of hospitalized patients newly started on buprenorphine maintenance filled an outpatient buprenorphine prescription within 30 days of discharge. We are not aware of comparative data for 30-day follow-up for hospitalized patients newly started on buprenorphine maintenance. Data from other contexts show 5% to 10% of veterans were engaged in addiction treatment 30 days after initiation from inpatient or outpatient encounters. An analysis of an academic medical center in Oregon found engagement with an addiction medicine consult service increased after hospital engagement for patients with any substance use disorder from 23% to 39% using the 34-day HEDIS measure for engagement.^17,24,25

The BBT required approximately 15 minutes per request and wrote an average of three prescriptions per week, demonstrating the feasibility of this approach and the high demand for this service. One strength of our approach is that residents gained experience starting buprenorphine independently using the aforementioned protocol instead of deferring to a full consult service. It is likely that this resident engagement in initiating longitudinal OUD care contributed to the success of this initiative, as did existing resident familiarity with using buprenorphine for opioid withdrawal.

This approach to resident education—promoting direct, first-person experience with medications in a clinical context—aligns with recommendations from a recent review about substance use disorder education for health professionals.²⁶ Our interventions increased resident knowledge and comfort with buprenorphine, consistent with prior studies showing increased resident confidence in management of substance use disorders after curricular innovations.^24,25

A few contextual features were essential for this project’s viability. Maryland allows American medical graduates to obtain a medical license after 1 year of postgraduate training. This allowed three residents to obtain X-waivers. These residents had access to HRSA funding to subsidize the expenses of applying for state licensure and DEA registration. BBT members volunteered their time while working on other services. Last, we were able to take advantage of buprenorphine-providing clinics in Baltimore, including the JHH After Care Clinic, to accept patients for follow-up appointments after discharge.

Limitations

The BBT required motivated clinicians willing to volunteer for additional clinical responsibilities during inpatient rotations and supportive faculty and residency leadership. Attending physicians, nurse practitioners, or physician assistants could staff a similar BBT in hospitals without residents or in hospitals where residents cannot obtain DEA registration. Crucially, other hospitals may not have access to practices with X-waivered physicians for outpatient follow-up. A recent study found X-waivered primary care physicians were less likely to be affiliated with hospital health systems. Other studies have shown limitations in access to buprenorphine at the county level based on geography and racial/ethnic segregation.^27-29

Most patients hospitalized with OUD did not have ICD-10 codes associated with OUD. We addressed this by assuming patients had OUD if buprenorphine or methadone was ordered during their hospitalization, even if the medication was never administered. This may have overcounted patients prescribed these medications for indications other than OUD, and it may have undercounted patients with OUD for whom buprenorphine or methadone were never considered. The opioid withdrawal order set at JHH automatically offers an option to use buprenorphine to treat withdrawal. Patients with OUD for whom buprenorphine or methadone were never ordered likely did not experience withdrawal or were in withdrawal so mild that it escaped the attention of the team, which limits the generalizability of our intervention.

We identified several limitations to the internal validity of our study. First, we used a before-and-after study design without a control group. We could not ethically withhold access to evidence-based, mortality-reducing medications from patients. Without a control group, we cannot rule out the possibility that underlying temporal trends made residents more likely to start buprenorphine maintenance independent of our intervention. We attempted to control for unmeasured confounders by using an interrupted time series analysis to control for preintervention trends, comparing the same group of residents before and after our interventions, and selecting an intervention period during which residents were given only educational sessions and materials provided by our team. Our results may be biased by clustered data because certain residents may have been more likely to initiate buprenorphine, but these effects are likely marginal because resident schedules are balanced between outpatient and inpatient rotations during each 6-month period.

Finally, this project focused on buprenorphine, not on other medications for OUD, including methadone or naltrexone, or nonpharmacologic treatments for OUD.

Since the start of the BBT in January 2019, five additional PGY-2 residents obtained their medical licenses and X-waivers. These residents, with the support of two attending hospitalists, led the BBT and coordinated education sessions that were incorporated into the curriculum during the 2019-2020 academic year. These educational sessions will continue indefinitely. In 2020, JHH started an Addiction Medicine Consult Service staffed by physicians, NPs, and a pharmacist. The BBT continues to operate in conjunction with this service.

We found substantial variability in the rate of buprenorphine maintenance initiation despite our interventions. This is an area for future improvement. In a free-response prompt in our follow-up survey, residents requested additional education sessions and an order set to assist with initiation of buprenorphine. To address these gaps, three educational sessions were added, one of which included education on starting methadone maintenance therapy. We also added a new order set for starting buprenorphine maintenance. We hypothesize that these interventions will improve consistency.

In order for a similar program to be disseminated to other institutions, educational initiatives and a team of dedicated X-waivered prescribers are key. Materials to assist with this process are available in the Appendix.

This study shows how a resident-led intervention comprising clinician education and a team of X-waivered generalists was associated with improved treatment of OUD for hospitalized patients. We encourage residents and all clinicians at other hospitals without addiction medicine consult services to design, implement, and study similar interventions that directly increase the use of buprenorphine or methadone maintenance to treat OUD.

Preliminary results from this project were presented at the AMERSA National Conference on November 7, 2019.

Nearly 48,000 Americans died from overdoses involving opioids in 2018, continuing a national crisis that has led to 446,000 deaths since 1999.¹ Annually, opioids are responsible for more than 500,000 admissions, approximately 1% of all hospitalizations, costing the United States nearly $15 billion.^2,3 Among hospitalized patients, chronic opioid use is associated with increased mortality, severe infectious complications, and higher rates of readmission.⁴ Opioid use disorder (OUD) is a chronic, relapsing medical condition with biopsychosocial origins and significant morbidity and mortality.⁵ Opioid agonist therapy (OAT) with buprenorphine or methadone maintenance, the evidence-based standard of treatment, reduces the mortality rate by half, decreases overdoses and hospital readmissions, and improves retention in care.^6-10

OAT maintenance refers to using buprenorphine or methadone for long-term treatment of OUD rather than for acute treatment of opioid withdrawal. Despite evidence supporting OAT maintenance, clinicians start medications for only 11% to 15% of hospitalized patients with OUD, depending on practice contexts.^11,12 Three significant barriers—stigma, insufficient clinician education, and restrictive regulations—prevent clinicians from starting OAT.¹³ Clinicians who do not have the Drug Enforcement Administration (DEA)–issued DATA-2000 waiver (X-waiver) for outpatient prescribing can order buprenorphine for admitted patients but cannot prescribe it at discharge.¹⁴ In hospitals where they exist, addiction medicine consult services offer primary teams guidance on pharmacotherapy, leading to reduced hospital readmissions and increased engagement in outpatient addiction treatment.^15-17 However, in most hospitals around the country, such specialty services do not exist.¹⁸ In some hospitals without addiction medicine consult services, hospitalists with expertise in OUD have started assisting primary teams in starting OAT, but to our knowledge, no prior studies have described the impact of these interventions on patients or clinician experience with OAT.¹⁹

This quality improvement project aimed to increase the rate at which internal medicine resident teams at Johns Hopkins Hospital (JHH) in Baltimore, Maryland, started hospitalized patients with OUD on buprenorphine maintenance. We hypothesized that resident education and measures to increase the availability of X-waivered physicians would increase the rate of initiating buprenorphine maintenance. We additionally hypothesized that these interventions would increase knowledge about and comfort with buprenorphine across the residency. This represents the first study to examine the effects of clinician education and a team of X-waivered residents and hospitalists who assist in starting buprenorphine maintenance in a hospital without an addiction medicine consult service.

Setting

This study took place from July 2018 to June 2019 at JHH, a large, academic, urban hospital in Baltimore. Prior to the intervention, internal medicine residents at JHH commonly used short courses of buprenorphine to treat withdrawal, but they did not have access to hospital-specific resources to assist with starting maintenance OAT. During the study period, JHH had a Substance Use Disorders team staffed by peer recovery specialists that could be consulted by hospitalists and residents to provide psychosocial support and link admitted patients to treatment after discharge. There were no providers on the team to guide pharmacotherapy or to write discharge buprenorphine prescriptions. The Osler Medical Residency Training Program at JHH has 140 internal medicine residents and 16 combined medicine-pediatrics residents. All residents receive 1 hour of formal education about opioid use disorder annually. In addition, 28 of those 156 residents, those in the Urban Health Primary Care track, spend 1 month on an Addiction Medicine rotation in which they complete the 8-hour training required to receive the X-waiver. Those residents are encouraged to apply for the X-waiver once they obtain a medical license subsidized by a Health Resources & Services Administration (HRSA) grant. Four internal medicine attending physicians on teaching services and one resident had X-waivers prior to the intervention.

Intervention

In November 2018, we administered a survey to residents to identify barriers to starting buprenorphine maintenance and to measure knowledge and confidence with using buprenorphine for OUD (Appendix Figure 1 and Figure 2). We focused on buprenorphine because providers at JHH were familiar with this medication and because Baltimore has widespread access to buprenorphine, with more than 490 local buprenorphine providers.²⁰ Five residents piloted the survey and provided feedback. We then administered the survey to all internal medicine and medicine-pediatrics residents. Based on the results, we developed a targeted educational conference and also created the Buprenorphine Bridge Team (BBT).

In January 2019, we presented the educational conference for residents devoted to the use of buprenorphine for OUD and introduced the BBT. The conference started with a patient testimonial and included peer recovery specialists, pharmacists, nurses, and social workers. We summarized the evidence for buprenorphine and offered a practical guide to start treatment in a one-page protocol. This protocol included guidance on selecting patients, shared decision-making around OUD treatment, avoiding precipitated withdrawal, dosing buprenorphine, and establishing follow-up (Appendix Figure 3). We asked for input on this protocol from nursing leadership, social work teams, and peer recovery specialists. Dosing was adapted from the Guidelines from the American Society of Addiction Medicine, with expert input from physicians from the Addiction Medicine Consult service at Johns Hopkins Bayview Medical Center, also in Baltimore.⁵ We instructed residents to obtain discharge buprenorphine prescriptions from an X-waivered physician on their team or from the newly established BBT. We asked resident teams to set up a postdischarge appointment for patients with an X-waivered provider, either in a community practice or at the JHH After Care Clinic, a transitional care clinic for discharged patients.²¹

The BBT is a resident-led group of X-waivered JHH residents and hospitalists who volunteer to write discharge buprenorphine prescriptions for patients. The BBT serves to ensure primary teams have access to an X-waivered prescriber. It is not a consult service. We asked primary teams to contact the BBT after initiating buprenorphine and after securing a follow-up appointment. In response to each request, a member of the BBT reviews the patient chart, confirms the follow-up plan, writes a prescription for buprenorphine along with intranasal naloxone, and leaves a brief note. During the 6-month postintervention period, the team consisted of three residents and three hospitalist attendings. Each week, two members (residents or attendings) staffed the team Monday to Friday, 8 am to 5 pm. Most weeks were staffed by two residents. One resident provided services after hours and during weekends. Resident team members ensured that the buprenorphine plan was discussed with the primary team’s attending. For dosing questions beyond the BBT’s scope of knowledge, a member of the BBT relayed questions to physicians from the Addiction Medicine Consult team at Johns Hopkins Bayview Medical Center.

In May 2019, 5 months after the education session and implementation of the BBT, we administered a follow-up survey.

Outcomes

The primary outcome was the percent of inpatients eligible to start OAT who were discharged on buprenorphine maintenance. We obtained data from the electronic medical record. The denominator consisted of patients with OUD not on buprenorphine or methadone maintenance on admission. We identified patients with OUD by an opioid-related International Classification of Diseases, Tenth Revision (ICD-10) diagnosis code or by a standing or as-needed order for buprenorphine or methadone during hospitalization.²² We reviewed admission and discharge documentation to identify patients with OUD who were not in active treatment with buprenorphine or methadone maintenance.

As a secondary outcome, we measured engagement in OUD treatment after discharge by calculating the proportion of patients started on buprenorphine who filled a buprenorphine prescription within 30 days after discharge. We chose 30 days based on the National Committee for Quality Assurance’s Healthcare Effectiveness Data and Information Set (HEDIS) measure for engagement of treatment for alcohol and other drugs.²³ We obtained the data from the Chesapeake Regional Information System for our Patients (CRISP) Prescription Drug Monitoring Program, which monitors all prescriptions for controlled substances dispensed in Maryland and five neighboring states. As a balancing measure, we counted patients newly started on methadone maintenance for OUD before and after the intervention. Additional secondary process outcomes included frequency of BBT requests, the volume of buprenorphine prescriptions written by the team, and time required to complete a BBT request.

Clinician-level outcomes, measured with electronically administered pre- and postintervention surveys to residents, included knowledge about and comfort with buprenorphine. Of the 16 questions in the pre- and postimplementation surveys, we analyzed the 6 questions concerning knowledge and comfort that remained identical in the pre- and postintervention surveys and used 5-point Likert scale responses. As an incentive, we randomly distributed three $50 gift cards to survey completers.

Analysis

We used an interrupted time series analysis to evaluate the association between the intervention bundle and a change in the rate that medical teams started patients with OUD on buprenorphine maintenance. This approach allowed us to control for preintervention trends. To evaluate the impact of our interventions, our pre- and postintervention periods include the same residents during the 2018-2019 academic year. Both periods consisted of twelve 2-week intervals (preintervention: July 26, 2019, to January 9, 2019; postintervention: January 10, 2019, to June 26, 2019).

To evaluate for changes in engagement in OUD treatment after discharge, we used two-sample t tests. To evaluate for changes in resident-reported comfort and knowledge with initiating buprenorphine maintenance, we used Wilcoxon rank sum tests for survey data and Wilcoxon signed rank tests for paired data. All analyses employed two-sided P values with statistical significance evaluated at the .05 alpha level. We analyzed data using R version 3.6.3 (Foundation for Statistical Computing). The Institutional Review Board at JHH reviewed and approved the study protocol as a quality improvement project (IRB00193365).

During the 24-week preintervention period, internal medicine resident teams started 30 out of 305 eligible patients (10%) on buprenorphine maintenance vs 64 out of 270 eligible patients (24%) during the 24-week postintervention period. Our interrupted time series analysis showed a significant increase in the percent of eligible patients started on buprenorphine maintenance (expected number of patients started postintervention, 27; actual, 64; absolute increase in percent, 14.4%; 95% CI, 3.6%-25.3%; P = .017) (Figure). There was no significant trend during the preintervention period and no significant trend during the postintervention period.

Before the intervention, 13 of the 30 patients (40%) newly started on buprenorphine maintenance during their admission filled a follow-up buprenorphine prescription within 30 days of discharge. After the intervention, 31 of 64 patients (46%) filled a buprenorphine prescription within 30 days (P = .612). Two patients were started on methadone maintenance, one prior to and one after the intervention.

During the 6-month postintervention period, the BBT received 75 requests and wrote 70 prescriptions for buprenorphine. The median time required to complete a BBT request was 15 minutes (minimum, 5 minutes; maximum, 60 minutes).

Of 156 internal medicine and medicine-pediatrics residents, 89 residents (57%) completed the baseline survey and 66 residents (42%) completed the follow-up survey. Forty residents completed both surveys. After the intervention, residents were significantly more likely to feel comfortable dosing buprenorphine (P < .0001) and counseling patients about its use (P = .0237) and were more likely to report ease of establishing follow-up (P < .0001). Self-reported knowledge about preventing precipitated withdrawal increased significantly (P = .0191), as did knowledge about the effectiveness of buprenorphine (P = .0003) independent of formal drug counseling (P = .0066) (Table). Paired survey data also found statistically significant results for all questions except those about preventing precipitated withdrawal and efficacy. For the latter, respondents who completed both surveys were more knowledgeable before the intervention than the overall group that completed the baseline survey (Appendix Table).

This study shows how a resident-led quality improvement project comprising clinician education and implementation of a novel BBT was associated with an increased rate of starting buprenorphine maintenance in hospitalized patients with OUD and improved resident knowledge about and comfort with buprenorphine. To our knowledge, this is the first study demonstrating how education and a team of X-waivered generalists can help primary teams initiate and discharge patients on buprenorphine maintenance in a hospital without an addiction medicine consult service.

Prior to the intervention, resident internal medicine teams at JHH started 10% of hospitalized patients with OUD on buprenorphine maintenance, consistent with prior studies showing rates of 11% to 15% for initiating OAT for hospitalized patients.^11,12 After the intervention, the rate of initiating buprenorphine maintenance more than doubled, rising to 24% of eligible patients. Resident internal medicine teams at JHH started buprenorphine maintenance for 37 more patients over the 24-week postintervention period than would have been predicted prior to the intervention, or an additional three patients every 2 weeks.

Between 40% and 46% of hospitalized patients newly started on buprenorphine maintenance filled an outpatient buprenorphine prescription within 30 days of discharge. We are not aware of comparative data for 30-day follow-up for hospitalized patients newly started on buprenorphine maintenance. Data from other contexts show 5% to 10% of veterans were engaged in addiction treatment 30 days after initiation from inpatient or outpatient encounters. An analysis of an academic medical center in Oregon found engagement with an addiction medicine consult service increased after hospital engagement for patients with any substance use disorder from 23% to 39% using the 34-day HEDIS measure for engagement.^17,24,25

The BBT required approximately 15 minutes per request and wrote an average of three prescriptions per week, demonstrating the feasibility of this approach and the high demand for this service. One strength of our approach is that residents gained experience starting buprenorphine independently using the aforementioned protocol instead of deferring to a full consult service. It is likely that this resident engagement in initiating longitudinal OUD care contributed to the success of this initiative, as did existing resident familiarity with using buprenorphine for opioid withdrawal.

This approach to resident education—promoting direct, first-person experience with medications in a clinical context—aligns with recommendations from a recent review about substance use disorder education for health professionals.²⁶ Our interventions increased resident knowledge and comfort with buprenorphine, consistent with prior studies showing increased resident confidence in management of substance use disorders after curricular innovations.^24,25

A few contextual features were essential for this project’s viability. Maryland allows American medical graduates to obtain a medical license after 1 year of postgraduate training. This allowed three residents to obtain X-waivers. These residents had access to HRSA funding to subsidize the expenses of applying for state licensure and DEA registration. BBT members volunteered their time while working on other services. Last, we were able to take advantage of buprenorphine-providing clinics in Baltimore, including the JHH After Care Clinic, to accept patients for follow-up appointments after discharge.

Limitations

The BBT required motivated clinicians willing to volunteer for additional clinical responsibilities during inpatient rotations and supportive faculty and residency leadership. Attending physicians, nurse practitioners, or physician assistants could staff a similar BBT in hospitals without residents or in hospitals where residents cannot obtain DEA registration. Crucially, other hospitals may not have access to practices with X-waivered physicians for outpatient follow-up. A recent study found X-waivered primary care physicians were less likely to be affiliated with hospital health systems. Other studies have shown limitations in access to buprenorphine at the county level based on geography and racial/ethnic segregation.^27-29

Most patients hospitalized with OUD did not have ICD-10 codes associated with OUD. We addressed this by assuming patients had OUD if buprenorphine or methadone was ordered during their hospitalization, even if the medication was never administered. This may have overcounted patients prescribed these medications for indications other than OUD, and it may have undercounted patients with OUD for whom buprenorphine or methadone were never considered. The opioid withdrawal order set at JHH automatically offers an option to use buprenorphine to treat withdrawal. Patients with OUD for whom buprenorphine or methadone were never ordered likely did not experience withdrawal or were in withdrawal so mild that it escaped the attention of the team, which limits the generalizability of our intervention.

We identified several limitations to the internal validity of our study. First, we used a before-and-after study design without a control group. We could not ethically withhold access to evidence-based, mortality-reducing medications from patients. Without a control group, we cannot rule out the possibility that underlying temporal trends made residents more likely to start buprenorphine maintenance independent of our intervention. We attempted to control for unmeasured confounders by using an interrupted time series analysis to control for preintervention trends, comparing the same group of residents before and after our interventions, and selecting an intervention period during which residents were given only educational sessions and materials provided by our team. Our results may be biased by clustered data because certain residents may have been more likely to initiate buprenorphine, but these effects are likely marginal because resident schedules are balanced between outpatient and inpatient rotations during each 6-month period.

Finally, this project focused on buprenorphine, not on other medications for OUD, including methadone or naltrexone, or nonpharmacologic treatments for OUD.

Since the start of the BBT in January 2019, five additional PGY-2 residents obtained their medical licenses and X-waivers. These residents, with the support of two attending hospitalists, led the BBT and coordinated education sessions that were incorporated into the curriculum during the 2019-2020 academic year. These educational sessions will continue indefinitely. In 2020, JHH started an Addiction Medicine Consult Service staffed by physicians, NPs, and a pharmacist. The BBT continues to operate in conjunction with this service.

We found substantial variability in the rate of buprenorphine maintenance initiation despite our interventions. This is an area for future improvement. In a free-response prompt in our follow-up survey, residents requested additional education sessions and an order set to assist with initiation of buprenorphine. To address these gaps, three educational sessions were added, one of which included education on starting methadone maintenance therapy. We also added a new order set for starting buprenorphine maintenance. We hypothesize that these interventions will improve consistency.

In order for a similar program to be disseminated to other institutions, educational initiatives and a team of dedicated X-waivered prescribers are key. Materials to assist with this process are available in the Appendix.

This study shows how a resident-led intervention comprising clinician education and a team of X-waivered generalists was associated with improved treatment of OUD for hospitalized patients. We encourage residents and all clinicians at other hospitals without addiction medicine consult services to design, implement, and study similar interventions that directly increase the use of buprenorphine or methadone maintenance to treat OUD.

Preliminary results from this project were presented at the AMERSA National Conference on November 7, 2019.

Hospitalizations related to opioid use disorder (OUD) have increased and now account for up to 6% of hospital admissions in certain areas of the United States.¹ Patients with OUD who are started on buprenorphine during hospitalization are more likely to enter outpatient treatment, stay in treatment longer, and have more drug-free days compared with patients who only receive a referral for outpatient treatment.^2,3 Therefore, a crucial comprehensive strategy for OUD care should include hospital-based programs that support initiation of treatment in the inpatient setting and strong bridges to outpatient care. One of the common barriers to initiating treatment in the inpatient setting, however, is a lack of access to addiction medicine specialists.^4-6

In 2017, we created a hospitalist-led interprofessional team called the B-Team (Buprenorphine Team) to help primary care teams identify patients with OUD, initiate and maintain buprenorphine therapy during hospitalization, provide warm handoffs to outpatient treatment programs, and reduce institutional stigma related to people with substance use disorders.

Program Description

The B-Team is led by a hospital medicine physician assistant and includes physicians from internal medicine, consult-liaison psychiatry, and palliative care; advanced practice and bedside nurses; a social worker; a pharmacist; a chaplain; a peer-recovery specialist; and medical trainees. The B-Team is notified of potential candidates for buprenorphine through a secure texting platform, one that is accessible to any healthcare provider at the hospital. Patients who are referred to the B-Team either self-identify or are identified by their primary team as having an underlying OUD. One of the B-Team providers assesses the patient to determine if they are eligible to receive inpatient therapy. Patients are considered eligible for the program if they meet Diagnostic and Statistical Manual of Mental Disorders (5th edition) criteria for OUD, have a desire to cease opioid use, and receive medical clearance to take buprenorphine.

For eligible patients, the B-Team provider orders a nurse-driven protocol to initiate buprenorphine for OUD. The chaplain offers psychospiritual counseling, and the social worker provides counseling and coordination of care. The B-Team partners with a nonhospital-affiliated, publicly-funded, office-based opioid treatment (OBOT) program that combines primary care with behavioral health programming. A follow-up outpatient appointment is secured prior to hospital discharge, and a member of the B-Team who has Drug Addiction Treatment Act of 2000 (DATA 2000) X-waiver certification prescribes buprenorphine as a bridge until the follow-up appointment. The medication is dispensed from the hospital’s retail pharmacy, and the patient leaves the hospital with the medication in-hand.

Patients who are not eligible for buprenorphine therapy are offered a harm-reduction intervention or referral to the psychiatry consult liaison service to assess for alternative diagnoses or treatment. These patients are also offered psychospiritual counseling and a prescription for naloxone.

Prior to the creation of the B-Team at our hospital, there was no structure in place to facilitate initiation of buprenorphine therapy during hospitalization and no linkage to outpatient treatment after discharge; furthermore, none of the hospitalists or other providers (including consulting psychiatrists) had an X-waiver to prescribe buprenorphine for OUD.

Program Evaluation

Study data were collected using Research Electronic Data Capture software. Inpatient and outpatient data were entered by a B-Team provider or a researcher via chart review. Patients were considered to be engaged in care if they attended at least one outpatient appointment for buprenorphine therapy during each of the following time periods: (1) 0 to 27 days (initial follow-up), 28-89 days (1- to 3-month follow-up), 90-179 days (3- to 6-month follow-up), and 180 days or more (>6-month follow-up). Only visits specifically for buprenorphine maintenance therapy were counted. If multiple encounters occurred within one time frame, the encounter closest to 0, 30, 90, or 180 days from discharge was used. If a patient did not attend any encounters during a specified time frame, they were considered to no longer be engaged in care and were no longer tracked for purposes of the evaluation. Data for the percentage of patients engaged in outpatient care are presented as the number of patients who attended at least one appointment during each of the follow-up periods (1 to 3 months, 3 to 6 months, or after 6 months, as noted above) divided by the number of patients who had been discharged with coordinated follow-up.

The number of patients admitted per month for whom there was an order to initiate inpatient buprenorphine therapy was analyzed using a statistical process control chart, in addition to the number of OUD admissions based on the inclusion of the International Classification of Disease, Tenth Revision (ICD-10) F11 code (opioid-related disorders) in any position in the discharge diagnoses.

This program and study were considered quality improvement by The University of Texas Institutional Review Board and did not meet criteria for human subjects research.

RESULTS

During the first 2 years of the program (September 2018-September 2020), the B-Team received 260 patient referrals. Most of the patients were White (72%), male (62%), and between ages 25 and 44 years (53%) (Appendix Table). The team initiated buprenorphine therapy in 132 hospitalized patients. In the year prior to the creation of the B-Team program, the average number of hospitalized patients receiving buprenorphine for OUD per month was three; after the launch of the B-Team program, this number increased to 12 encounters per month (Figure 1A). The sudden decrease observed in August 2020 is likely related to a surge in COVID-19 admissions. The number of monthly admissions for OUD is also shown (Figure 1B).

The B-Team saw a total of 132 eligible patients; members of the team provided counseling, support, and resources regarding buprenorphine therapy. In addition, the B-Team’s chaplain provided emotional support and spiritual connection (if desired) to 40 of these patients (30%). In the study, no cases of precipitated withdrawal were identified. Of the 132 patients seen, 110 (83%) were accepted to an outpatient OUD program upon discharge from the hospital; 98 (89%) of these patients were accepted at our partner OBOT clinic. The remaining patients were not interested in continuing OUD treatment (13%) or were denied acceptance to an outpatient program based on administrative and/or financial eligibility guidelines (4%). Patients who would not be attending an outpatient program were discontinued on buprenorphine therapy prior to discharge, counseled about naloxone, and provided printed resources.

Outpatient appointment attendance was used to measure ongoing treatment engagement of the 110 patients who were discharged with coordinated follow-up care. A total of 65 patients (59%) attended their first outpatient appointment; the average time between discharge and the first outpatient appointment was 5.9 days. Forty-two patients (38%) attended at least one appointment between 1 and 3 months; 29 (26%) between 3 and 6 months; and 24 (22%) after 6 months (Figure 2).

Of the 128 patients who were not administered buprenorphine therapy, 64 (50%) were not interested in starting treatment and/or were not ready to engage in treatment; 36 (28%) did not meet criteria for OUD treatment; 28 (22%) were already receiving treatment or preferred another type of OUD treatment; and 13 (10%) had severe comorbid addiction and/or illness requiring treatment that contraindicates the use of buprenorphine.

A volunteer hospitalist-led interprofessional team providing evidence-based care for hospitalized patients with OUD was associated with a substantial increase in patients receiving buprenorphine therapy—both during hospitalization and after discharge. In the program, 59% of patients attended initial follow-up appointments, and 22% of patients were still engaged at 6 months. These outpatient follow-up rates appear to be similar to, or higher than, other programs described in the literature. For example, a buprenorphine OUD-treatment initiative led by the psychiatry consult service at a Boston academic medical center resulted in less than half of patients receiving buprenorphine treatment within 2 months of discharge.⁷ In another study wherein an addiction medicine consult service administered buprenorphine to patients with OUD during hospitalization, 39%, 27%, and 18% of patients were retained in outpatient treatment at 30, 90, and 180 days, respectively.⁸

The B-Team model is likely generalizable to other hospital medicine groups that may not otherwise have access to inpatient care for substance use disorder. The B-Team is not an addiction medicine consultation service; rather, it is a hospitalist-led quality improvement initiative seeking to improve the standard of care for hospitalized patients with OUD.

A significant barrier is ensuring ongoing support for patients with OUD after discharge. In the B-Team program, a parallel OBOT program was created by a local nonaffiliated federally qualified health center. Although 89% of patients received treatment at this OBOT clinic, the inpatient team also has relationships with other local treatment centers, including programs that provide methadone. Another important barrier to high-quality outpatient care for OUD is the requirement of an X-waiver. To help overcome this barrier, our inpatient program partnered with a regional medical society to offer periodic X-waiver training to outpatient providers. In less than a year, more than 100 regional prescribers participated in this program.

Our study has several limitations. There was likely some degree of selection bias among the hospitalized patients who received initial buprenorphine treatment. To our knowledge, there is no specific validated screening tool for OUD in the inpatient acute care setting; moreover, we have been unable to implement standardized screening for OUD into the electronic health record. As such, we rely on the totality of the clinical circumstances approach to identify patients with OUD.

Furthermore, we had neither a comparison group nor a prospective plan to follow patients who did not remain engaged in care after discharge. In addition, our analysis of OUD admissions included F11 ICD-10 codes, which are limited by clinical documentation.^9,10 Our program focuses exclusively on buprenorphine initiation due to insufficient immediate outpatient capacity for methadone initiated during hospitalization and lack of coverage for extended-release naltrexone. Limitations to outpatient data-sharing prevented the reporting of outpatient appointments external to the identified partner program; since these appointments were included in the analysis as “lost to follow-up,” actual engagement rates may be higher than those reported.

Moving forward, the B-Team is continuing to serve as a role model for appropriate, patient-centered, evidence-based care for hospitalized patients with OUD. Attending physicians and residents with an X-waiver are now encouraged to initiate buprenorphine treatment on their own. In June 2020, we added peer-recovery support services to the program, which has improved care for patients and increased adoption of hospital-initiated substance use disorder interventions.¹¹ Lessons learned from inpatient implementation are being applied to our hospital’s emergency department and to an inpatient obstetrics unit at a partner hospital; they are also being employed to further empower hospitalists to diagnose and treat other substance use disorders, such as alcohol use disorder.

Hospitalizations related to opioid use disorder (OUD) have increased and now account for up to 6% of hospital admissions in certain areas of the United States.¹ Patients with OUD who are started on buprenorphine during hospitalization are more likely to enter outpatient treatment, stay in treatment longer, and have more drug-free days compared with patients who only receive a referral for outpatient treatment.^2,3 Therefore, a crucial comprehensive strategy for OUD care should include hospital-based programs that support initiation of treatment in the inpatient setting and strong bridges to outpatient care. One of the common barriers to initiating treatment in the inpatient setting, however, is a lack of access to addiction medicine specialists.^4-6

In 2017, we created a hospitalist-led interprofessional team called the B-Team (Buprenorphine Team) to help primary care teams identify patients with OUD, initiate and maintain buprenorphine therapy during hospitalization, provide warm handoffs to outpatient treatment programs, and reduce institutional stigma related to people with substance use disorders.

Program Description

The B-Team is led by a hospital medicine physician assistant and includes physicians from internal medicine, consult-liaison psychiatry, and palliative care; advanced practice and bedside nurses; a social worker; a pharmacist; a chaplain; a peer-recovery specialist; and medical trainees. The B-Team is notified of potential candidates for buprenorphine through a secure texting platform, one that is accessible to any healthcare provider at the hospital. Patients who are referred to the B-Team either self-identify or are identified by their primary team as having an underlying OUD. One of the B-Team providers assesses the patient to determine if they are eligible to receive inpatient therapy. Patients are considered eligible for the program if they meet Diagnostic and Statistical Manual of Mental Disorders (5th edition) criteria for OUD, have a desire to cease opioid use, and receive medical clearance to take buprenorphine.

For eligible patients, the B-Team provider orders a nurse-driven protocol to initiate buprenorphine for OUD. The chaplain offers psychospiritual counseling, and the social worker provides counseling and coordination of care. The B-Team partners with a nonhospital-affiliated, publicly-funded, office-based opioid treatment (OBOT) program that combines primary care with behavioral health programming. A follow-up outpatient appointment is secured prior to hospital discharge, and a member of the B-Team who has Drug Addiction Treatment Act of 2000 (DATA 2000) X-waiver certification prescribes buprenorphine as a bridge until the follow-up appointment. The medication is dispensed from the hospital’s retail pharmacy, and the patient leaves the hospital with the medication in-hand.

Patients who are not eligible for buprenorphine therapy are offered a harm-reduction intervention or referral to the psychiatry consult liaison service to assess for alternative diagnoses or treatment. These patients are also offered psychospiritual counseling and a prescription for naloxone.

Prior to the creation of the B-Team at our hospital, there was no structure in place to facilitate initiation of buprenorphine therapy during hospitalization and no linkage to outpatient treatment after discharge; furthermore, none of the hospitalists or other providers (including consulting psychiatrists) had an X-waiver to prescribe buprenorphine for OUD.

Program Evaluation

Study data were collected using Research Electronic Data Capture software. Inpatient and outpatient data were entered by a B-Team provider or a researcher via chart review. Patients were considered to be engaged in care if they attended at least one outpatient appointment for buprenorphine therapy during each of the following time periods: (1) 0 to 27 days (initial follow-up), 28-89 days (1- to 3-month follow-up), 90-179 days (3- to 6-month follow-up), and 180 days or more (>6-month follow-up). Only visits specifically for buprenorphine maintenance therapy were counted. If multiple encounters occurred within one time frame, the encounter closest to 0, 30, 90, or 180 days from discharge was used. If a patient did not attend any encounters during a specified time frame, they were considered to no longer be engaged in care and were no longer tracked for purposes of the evaluation. Data for the percentage of patients engaged in outpatient care are presented as the number of patients who attended at least one appointment during each of the follow-up periods (1 to 3 months, 3 to 6 months, or after 6 months, as noted above) divided by the number of patients who had been discharged with coordinated follow-up.

The number of patients admitted per month for whom there was an order to initiate inpatient buprenorphine therapy was analyzed using a statistical process control chart, in addition to the number of OUD admissions based on the inclusion of the International Classification of Disease, Tenth Revision (ICD-10) F11 code (opioid-related disorders) in any position in the discharge diagnoses.

This program and study were considered quality improvement by The University of Texas Institutional Review Board and did not meet criteria for human subjects research.

RESULTS

During the first 2 years of the program (September 2018-September 2020), the B-Team received 260 patient referrals. Most of the patients were White (72%), male (62%), and between ages 25 and 44 years (53%) (Appendix Table). The team initiated buprenorphine therapy in 132 hospitalized patients. In the year prior to the creation of the B-Team program, the average number of hospitalized patients receiving buprenorphine for OUD per month was three; after the launch of the B-Team program, this number increased to 12 encounters per month (Figure 1A). The sudden decrease observed in August 2020 is likely related to a surge in COVID-19 admissions. The number of monthly admissions for OUD is also shown (Figure 1B).

The B-Team saw a total of 132 eligible patients; members of the team provided counseling, support, and resources regarding buprenorphine therapy. In addition, the B-Team’s chaplain provided emotional support and spiritual connection (if desired) to 40 of these patients (30%). In the study, no cases of precipitated withdrawal were identified. Of the 132 patients seen, 110 (83%) were accepted to an outpatient OUD program upon discharge from the hospital; 98 (89%) of these patients were accepted at our partner OBOT clinic. The remaining patients were not interested in continuing OUD treatment (13%) or were denied acceptance to an outpatient program based on administrative and/or financial eligibility guidelines (4%). Patients who would not be attending an outpatient program were discontinued on buprenorphine therapy prior to discharge, counseled about naloxone, and provided printed resources.

Outpatient appointment attendance was used to measure ongoing treatment engagement of the 110 patients who were discharged with coordinated follow-up care. A total of 65 patients (59%) attended their first outpatient appointment; the average time between discharge and the first outpatient appointment was 5.9 days. Forty-two patients (38%) attended at least one appointment between 1 and 3 months; 29 (26%) between 3 and 6 months; and 24 (22%) after 6 months (Figure 2).

Of the 128 patients who were not administered buprenorphine therapy, 64 (50%) were not interested in starting treatment and/or were not ready to engage in treatment; 36 (28%) did not meet criteria for OUD treatment; 28 (22%) were already receiving treatment or preferred another type of OUD treatment; and 13 (10%) had severe comorbid addiction and/or illness requiring treatment that contraindicates the use of buprenorphine.

A volunteer hospitalist-led interprofessional team providing evidence-based care for hospitalized patients with OUD was associated with a substantial increase in patients receiving buprenorphine therapy—both during hospitalization and after discharge. In the program, 59% of patients attended initial follow-up appointments, and 22% of patients were still engaged at 6 months. These outpatient follow-up rates appear to be similar to, or higher than, other programs described in the literature. For example, a buprenorphine OUD-treatment initiative led by the psychiatry consult service at a Boston academic medical center resulted in less than half of patients receiving buprenorphine treatment within 2 months of discharge.⁷ In another study wherein an addiction medicine consult service administered buprenorphine to patients with OUD during hospitalization, 39%, 27%, and 18% of patients were retained in outpatient treatment at 30, 90, and 180 days, respectively.⁸

The B-Team model is likely generalizable to other hospital medicine groups that may not otherwise have access to inpatient care for substance use disorder. The B-Team is not an addiction medicine consultation service; rather, it is a hospitalist-led quality improvement initiative seeking to improve the standard of care for hospitalized patients with OUD.

A significant barrier is ensuring ongoing support for patients with OUD after discharge. In the B-Team program, a parallel OBOT program was created by a local nonaffiliated federally qualified health center. Although 89% of patients received treatment at this OBOT clinic, the inpatient team also has relationships with other local treatment centers, including programs that provide methadone. Another important barrier to high-quality outpatient care for OUD is the requirement of an X-waiver. To help overcome this barrier, our inpatient program partnered with a regional medical society to offer periodic X-waiver training to outpatient providers. In less than a year, more than 100 regional prescribers participated in this program.

Our study has several limitations. There was likely some degree of selection bias among the hospitalized patients who received initial buprenorphine treatment. To our knowledge, there is no specific validated screening tool for OUD in the inpatient acute care setting; moreover, we have been unable to implement standardized screening for OUD into the electronic health record. As such, we rely on the totality of the clinical circumstances approach to identify patients with OUD.

Furthermore, we had neither a comparison group nor a prospective plan to follow patients who did not remain engaged in care after discharge. In addition, our analysis of OUD admissions included F11 ICD-10 codes, which are limited by clinical documentation.^9,10 Our program focuses exclusively on buprenorphine initiation due to insufficient immediate outpatient capacity for methadone initiated during hospitalization and lack of coverage for extended-release naltrexone. Limitations to outpatient data-sharing prevented the reporting of outpatient appointments external to the identified partner program; since these appointments were included in the analysis as “lost to follow-up,” actual engagement rates may be higher than those reported.

Moving forward, the B-Team is continuing to serve as a role model for appropriate, patient-centered, evidence-based care for hospitalized patients with OUD. Attending physicians and residents with an X-waiver are now encouraged to initiate buprenorphine treatment on their own. In June 2020, we added peer-recovery support services to the program, which has improved care for patients and increased adoption of hospital-initiated substance use disorder interventions.¹¹ Lessons learned from inpatient implementation are being applied to our hospital’s emergency department and to an inpatient obstetrics unit at a partner hospital; they are also being employed to further empower hospitalists to diagnose and treat other substance use disorders, such as alcohol use disorder.

Hospitalizations related to opioid use disorder (OUD) have increased and now account for up to 6% of hospital admissions in certain areas of the United States.¹ Patients with OUD who are started on buprenorphine during hospitalization are more likely to enter outpatient treatment, stay in treatment longer, and have more drug-free days compared with patients who only receive a referral for outpatient treatment.^2,3 Therefore, a crucial comprehensive strategy for OUD care should include hospital-based programs that support initiation of treatment in the inpatient setting and strong bridges to outpatient care. One of the common barriers to initiating treatment in the inpatient setting, however, is a lack of access to addiction medicine specialists.^4-6

In 2017, we created a hospitalist-led interprofessional team called the B-Team (Buprenorphine Team) to help primary care teams identify patients with OUD, initiate and maintain buprenorphine therapy during hospitalization, provide warm handoffs to outpatient treatment programs, and reduce institutional stigma related to people with substance use disorders.

Program Description

The B-Team is led by a hospital medicine physician assistant and includes physicians from internal medicine, consult-liaison psychiatry, and palliative care; advanced practice and bedside nurses; a social worker; a pharmacist; a chaplain; a peer-recovery specialist; and medical trainees. The B-Team is notified of potential candidates for buprenorphine through a secure texting platform, one that is accessible to any healthcare provider at the hospital. Patients who are referred to the B-Team either self-identify or are identified by their primary team as having an underlying OUD. One of the B-Team providers assesses the patient to determine if they are eligible to receive inpatient therapy. Patients are considered eligible for the program if they meet Diagnostic and Statistical Manual of Mental Disorders (5th edition) criteria for OUD, have a desire to cease opioid use, and receive medical clearance to take buprenorphine.

For eligible patients, the B-Team provider orders a nurse-driven protocol to initiate buprenorphine for OUD. The chaplain offers psychospiritual counseling, and the social worker provides counseling and coordination of care. The B-Team partners with a nonhospital-affiliated, publicly-funded, office-based opioid treatment (OBOT) program that combines primary care with behavioral health programming. A follow-up outpatient appointment is secured prior to hospital discharge, and a member of the B-Team who has Drug Addiction Treatment Act of 2000 (DATA 2000) X-waiver certification prescribes buprenorphine as a bridge until the follow-up appointment. The medication is dispensed from the hospital’s retail pharmacy, and the patient leaves the hospital with the medication in-hand.

Patients who are not eligible for buprenorphine therapy are offered a harm-reduction intervention or referral to the psychiatry consult liaison service to assess for alternative diagnoses or treatment. These patients are also offered psychospiritual counseling and a prescription for naloxone.

Prior to the creation of the B-Team at our hospital, there was no structure in place to facilitate initiation of buprenorphine therapy during hospitalization and no linkage to outpatient treatment after discharge; furthermore, none of the hospitalists or other providers (including consulting psychiatrists) had an X-waiver to prescribe buprenorphine for OUD.

Program Evaluation

Study data were collected using Research Electronic Data Capture software. Inpatient and outpatient data were entered by a B-Team provider or a researcher via chart review. Patients were considered to be engaged in care if they attended at least one outpatient appointment for buprenorphine therapy during each of the following time periods: (1) 0 to 27 days (initial follow-up), 28-89 days (1- to 3-month follow-up), 90-179 days (3- to 6-month follow-up), and 180 days or more (>6-month follow-up). Only visits specifically for buprenorphine maintenance therapy were counted. If multiple encounters occurred within one time frame, the encounter closest to 0, 30, 90, or 180 days from discharge was used. If a patient did not attend any encounters during a specified time frame, they were considered to no longer be engaged in care and were no longer tracked for purposes of the evaluation. Data for the percentage of patients engaged in outpatient care are presented as the number of patients who attended at least one appointment during each of the follow-up periods (1 to 3 months, 3 to 6 months, or after 6 months, as noted above) divided by the number of patients who had been discharged with coordinated follow-up.

The number of patients admitted per month for whom there was an order to initiate inpatient buprenorphine therapy was analyzed using a statistical process control chart, in addition to the number of OUD admissions based on the inclusion of the International Classification of Disease, Tenth Revision (ICD-10) F11 code (opioid-related disorders) in any position in the discharge diagnoses.

This program and study were considered quality improvement by The University of Texas Institutional Review Board and did not meet criteria for human subjects research.

RESULTS

During the first 2 years of the program (September 2018-September 2020), the B-Team received 260 patient referrals. Most of the patients were White (72%), male (62%), and between ages 25 and 44 years (53%) (Appendix Table). The team initiated buprenorphine therapy in 132 hospitalized patients. In the year prior to the creation of the B-Team program, the average number of hospitalized patients receiving buprenorphine for OUD per month was three; after the launch of the B-Team program, this number increased to 12 encounters per month (Figure 1A). The sudden decrease observed in August 2020 is likely related to a surge in COVID-19 admissions. The number of monthly admissions for OUD is also shown (Figure 1B).

The B-Team saw a total of 132 eligible patients; members of the team provided counseling, support, and resources regarding buprenorphine therapy. In addition, the B-Team’s chaplain provided emotional support and spiritual connection (if desired) to 40 of these patients (30%). In the study, no cases of precipitated withdrawal were identified. Of the 132 patients seen, 110 (83%) were accepted to an outpatient OUD program upon discharge from the hospital; 98 (89%) of these patients were accepted at our partner OBOT clinic. The remaining patients were not interested in continuing OUD treatment (13%) or were denied acceptance to an outpatient program based on administrative and/or financial eligibility guidelines (4%). Patients who would not be attending an outpatient program were discontinued on buprenorphine therapy prior to discharge, counseled about naloxone, and provided printed resources.

Outpatient appointment attendance was used to measure ongoing treatment engagement of the 110 patients who were discharged with coordinated follow-up care. A total of 65 patients (59%) attended their first outpatient appointment; the average time between discharge and the first outpatient appointment was 5.9 days. Forty-two patients (38%) attended at least one appointment between 1 and 3 months; 29 (26%) between 3 and 6 months; and 24 (22%) after 6 months (Figure 2).

Of the 128 patients who were not administered buprenorphine therapy, 64 (50%) were not interested in starting treatment and/or were not ready to engage in treatment; 36 (28%) did not meet criteria for OUD treatment; 28 (22%) were already receiving treatment or preferred another type of OUD treatment; and 13 (10%) had severe comorbid addiction and/or illness requiring treatment that contraindicates the use of buprenorphine.

A volunteer hospitalist-led interprofessional team providing evidence-based care for hospitalized patients with OUD was associated with a substantial increase in patients receiving buprenorphine therapy—both during hospitalization and after discharge. In the program, 59% of patients attended initial follow-up appointments, and 22% of patients were still engaged at 6 months. These outpatient follow-up rates appear to be similar to, or higher than, other programs described in the literature. For example, a buprenorphine OUD-treatment initiative led by the psychiatry consult service at a Boston academic medical center resulted in less than half of patients receiving buprenorphine treatment within 2 months of discharge.⁷ In another study wherein an addiction medicine consult service administered buprenorphine to patients with OUD during hospitalization, 39%, 27%, and 18% of patients were retained in outpatient treatment at 30, 90, and 180 days, respectively.⁸

The B-Team model is likely generalizable to other hospital medicine groups that may not otherwise have access to inpatient care for substance use disorder. The B-Team is not an addiction medicine consultation service; rather, it is a hospitalist-led quality improvement initiative seeking to improve the standard of care for hospitalized patients with OUD.

A significant barrier is ensuring ongoing support for patients with OUD after discharge. In the B-Team program, a parallel OBOT program was created by a local nonaffiliated federally qualified health center. Although 89% of patients received treatment at this OBOT clinic, the inpatient team also has relationships with other local treatment centers, including programs that provide methadone. Another important barrier to high-quality outpatient care for OUD is the requirement of an X-waiver. To help overcome this barrier, our inpatient program partnered with a regional medical society to offer periodic X-waiver training to outpatient providers. In less than a year, more than 100 regional prescribers participated in this program.

Our study has several limitations. There was likely some degree of selection bias among the hospitalized patients who received initial buprenorphine treatment. To our knowledge, there is no specific validated screening tool for OUD in the inpatient acute care setting; moreover, we have been unable to implement standardized screening for OUD into the electronic health record. As such, we rely on the totality of the clinical circumstances approach to identify patients with OUD.

Furthermore, we had neither a comparison group nor a prospective plan to follow patients who did not remain engaged in care after discharge. In addition, our analysis of OUD admissions included F11 ICD-10 codes, which are limited by clinical documentation.^9,10 Our program focuses exclusively on buprenorphine initiation due to insufficient immediate outpatient capacity for methadone initiated during hospitalization and lack of coverage for extended-release naltrexone. Limitations to outpatient data-sharing prevented the reporting of outpatient appointments external to the identified partner program; since these appointments were included in the analysis as “lost to follow-up,” actual engagement rates may be higher than those reported.

Moving forward, the B-Team is continuing to serve as a role model for appropriate, patient-centered, evidence-based care for hospitalized patients with OUD. Attending physicians and residents with an X-waiver are now encouraged to initiate buprenorphine treatment on their own. In June 2020, we added peer-recovery support services to the program, which has improved care for patients and increased adoption of hospital-initiated substance use disorder interventions.¹¹ Lessons learned from inpatient implementation are being applied to our hospital’s emergency department and to an inpatient obstetrics unit at a partner hospital; they are also being employed to further empower hospitalists to diagnose and treat other substance use disorders, such as alcohol use disorder.