Even in the age of intensive therapy and extensive risk stratification, there are small but significant differences in outcomes between boys and girls with B-lineage acute lymphoblastic leukemia (B-ALL).

This finding comes from a review of 10 years of clinical trials by the Children’s Oncology Group (COG), which showed that, among patients with B-ALL, 5-year event-free survival (EFS) and overall survival (OS) were inferior with boys, compared with girls, even when adjusted for prognostic factors, reported Sumit Gupta, MD, PhD, FRCPC, from the Hospital for Sick Children in Toronto.

“Inferior outcomes, although small in absolute terms, continue to exist among boys versus girls despite modern therapy and after adjusting for other risk factors. These persist also despite the longer duration of therapy among boys,” he said in an oral abstract presentation during the annual meeting of the American Society of Pediatric Hematology/Oncology. (Abstract 2025).

Among pediatric patients with T-cell lineage ALL (T-ALL), however, there were no significant sex-based differences in either EFS or OS, he said.

Although survival for children with ALL has continued to improve, previous studies found inferior survival outcomes in boys, and suggested that the difference might be explained by imbalances in risk factors.

To see whether sex-based disparities persist with modern intensive therapy protocols after adjustment for risk factors, and to determine whether there are sex-based differences in toxicities or patterns of treatment failure, Dr. Gupta and colleagues created a cohort of all patients age 1-30 years enrolled in frontline COG trial for B-ALL and T-ALL from 2004 to 2014.

During this period, boys received an extra year of maintenance. Cranial radiation was limited to B-ALL patients with slow treatment responses and central nervous system status 3, signifying definite CNS involvement. Among patients with T-ALL, cranial radiation was given to all intermediate- and high-risk patients.
 

Sex differences small, but significant

The investigators identified a total of 8,202 patients (4,463 males and 3,739 females) with B-ALL, and 1,562 (1,161 males and 401 females) with T-ALL. Boys were likely to be older (P < .0001), and to have a small but significantly greater likelihood of having unfavorable B-ALL cytogenetics, compared with girls (P = .05).

Boys with B-ALL were less likely to be negative for minimal residual disease (76.1% vs. 78.1%, P = .04), but the opposite was true for those with T-ALL (59% vs. 56.8%, P = .01).

As noted before, among pediatric patients with B-ALL, EFS and OS were both inferior for males, with a hazard ratio for higher EFS rates in girls of 1.19 (P = .001) and a HR for OS of 1.17 (P = .046).

Both EFS and OS were similar between the sexes among patients with T-ALL.

The differences in EFS in patients with B-ALL was attributable to higher CNS relapses among boys (4.2% vs. 2.5%, P < .0001). The CNS relapses occurred at a median of 2.5 years in boys versus 2.1 years in girls, although most relapses occurred during therapy.

There were no differences in cumulative isolated bone marrow relapses, however.

Treatment-related mortality rates were the same, but osteonecrosis rates were significantly lower for boys, with a 5-year cumulative incidence of 5.2% versus 6.7% for girls (P = .001).
 

Possible explanations

Dr. Gupta noted that the inferior outcomes among boys may be attributable to extramedullary relapses among patients with B-ALL.

In addition, the lack of sex-based differences in T-ALL may be caused in part by the increased use of CNS radiation in this population. Previous studies in which CNS radiation was omitted showed an increase in CNS relapsed rates among boys but not girls, he pointed out.

“This does imply that in the more recent generation of T-lineage ALL treatment trials that we’ll need to monitor sex-based differences in outcome, as fewer and fewer patients with T-ALL disease received cranial radiation in these more recent trials and in contemporary therapy,” he said.

One possible mechanism for sex-based outcome differences might be differences in steroid metabolism, as suggested by the higher osteonecrosis rate among girls, he added.

In the question-and-answer following the presentation, William G. Woods, MD, from Emory University, Atlanta, asked what role testicular relapse played in outcomes.

Dr. Gupta replied that the investigators had considered that the excess risk for extramedullary relapse in boys might be accounted for by testicular relapse, but “when you take away testicular relapse from those numbers and really just concentrate on CNS, it’s still that substantial difference when you’re talking about B-lineage disease.”

In patients with T-ALL as well, CNS relapse was more common in boys after controlling for testicular relapse, he said.

Another audience member asked whether the data suggest a benefit to treating boys with CNS-penetrating drugs such as dexamethasone or high-dose methotrexate,

Dr. Gupta said that it’s still uncertain whether it is clinically sound to subject a boy with otherwise–standard-risk disease to more intensive high-risk therapy, given the relatively small absolute differences in outcomes between the sexes.

The study was supported by grants from the National Cancer Institute and the St. Baldrick’s Foundation. Dr. Gupta, Dr. Woods, and Dr. Meret had no relevant conflicts of interest to report.

Even in the age of intensive therapy and extensive risk stratification, there are small but significant differences in outcomes between boys and girls with B-lineage acute lymphoblastic leukemia (B-ALL).

This finding comes from a review of 10 years of clinical trials by the Children’s Oncology Group (COG), which showed that, among patients with B-ALL, 5-year event-free survival (EFS) and overall survival (OS) were inferior with boys, compared with girls, even when adjusted for prognostic factors, reported Sumit Gupta, MD, PhD, FRCPC, from the Hospital for Sick Children in Toronto.

“Inferior outcomes, although small in absolute terms, continue to exist among boys versus girls despite modern therapy and after adjusting for other risk factors. These persist also despite the longer duration of therapy among boys,” he said in an oral abstract presentation during the annual meeting of the American Society of Pediatric Hematology/Oncology. (Abstract 2025).

Among pediatric patients with T-cell lineage ALL (T-ALL), however, there were no significant sex-based differences in either EFS or OS, he said.

Although survival for children with ALL has continued to improve, previous studies found inferior survival outcomes in boys, and suggested that the difference might be explained by imbalances in risk factors.

To see whether sex-based disparities persist with modern intensive therapy protocols after adjustment for risk factors, and to determine whether there are sex-based differences in toxicities or patterns of treatment failure, Dr. Gupta and colleagues created a cohort of all patients age 1-30 years enrolled in frontline COG trial for B-ALL and T-ALL from 2004 to 2014.

During this period, boys received an extra year of maintenance. Cranial radiation was limited to B-ALL patients with slow treatment responses and central nervous system status 3, signifying definite CNS involvement. Among patients with T-ALL, cranial radiation was given to all intermediate- and high-risk patients.
 

Sex differences small, but significant

The investigators identified a total of 8,202 patients (4,463 males and 3,739 females) with B-ALL, and 1,562 (1,161 males and 401 females) with T-ALL. Boys were likely to be older (P < .0001), and to have a small but significantly greater likelihood of having unfavorable B-ALL cytogenetics, compared with girls (P = .05).

Boys with B-ALL were less likely to be negative for minimal residual disease (76.1% vs. 78.1%, P = .04), but the opposite was true for those with T-ALL (59% vs. 56.8%, P = .01).

As noted before, among pediatric patients with B-ALL, EFS and OS were both inferior for males, with a hazard ratio for higher EFS rates in girls of 1.19 (P = .001) and a HR for OS of 1.17 (P = .046).

Both EFS and OS were similar between the sexes among patients with T-ALL.

The differences in EFS in patients with B-ALL was attributable to higher CNS relapses among boys (4.2% vs. 2.5%, P < .0001). The CNS relapses occurred at a median of 2.5 years in boys versus 2.1 years in girls, although most relapses occurred during therapy.

There were no differences in cumulative isolated bone marrow relapses, however.

Treatment-related mortality rates were the same, but osteonecrosis rates were significantly lower for boys, with a 5-year cumulative incidence of 5.2% versus 6.7% for girls (P = .001).
 

Possible explanations

Dr. Gupta noted that the inferior outcomes among boys may be attributable to extramedullary relapses among patients with B-ALL.

In addition, the lack of sex-based differences in T-ALL may be caused in part by the increased use of CNS radiation in this population. Previous studies in which CNS radiation was omitted showed an increase in CNS relapsed rates among boys but not girls, he pointed out.

“This does imply that in the more recent generation of T-lineage ALL treatment trials that we’ll need to monitor sex-based differences in outcome, as fewer and fewer patients with T-ALL disease received cranial radiation in these more recent trials and in contemporary therapy,” he said.

One possible mechanism for sex-based outcome differences might be differences in steroid metabolism, as suggested by the higher osteonecrosis rate among girls, he added.

In the question-and-answer following the presentation, William G. Woods, MD, from Emory University, Atlanta, asked what role testicular relapse played in outcomes.

Dr. Gupta replied that the investigators had considered that the excess risk for extramedullary relapse in boys might be accounted for by testicular relapse, but “when you take away testicular relapse from those numbers and really just concentrate on CNS, it’s still that substantial difference when you’re talking about B-lineage disease.”

In patients with T-ALL as well, CNS relapse was more common in boys after controlling for testicular relapse, he said.

Another audience member asked whether the data suggest a benefit to treating boys with CNS-penetrating drugs such as dexamethasone or high-dose methotrexate,

Dr. Gupta said that it’s still uncertain whether it is clinically sound to subject a boy with otherwise–standard-risk disease to more intensive high-risk therapy, given the relatively small absolute differences in outcomes between the sexes.

The study was supported by grants from the National Cancer Institute and the St. Baldrick’s Foundation. Dr. Gupta, Dr. Woods, and Dr. Meret had no relevant conflicts of interest to report.

Even in the age of intensive therapy and extensive risk stratification, there are small but significant differences in outcomes between boys and girls with B-lineage acute lymphoblastic leukemia (B-ALL).

This finding comes from a review of 10 years of clinical trials by the Children’s Oncology Group (COG), which showed that, among patients with B-ALL, 5-year event-free survival (EFS) and overall survival (OS) were inferior with boys, compared with girls, even when adjusted for prognostic factors, reported Sumit Gupta, MD, PhD, FRCPC, from the Hospital for Sick Children in Toronto.

“Inferior outcomes, although small in absolute terms, continue to exist among boys versus girls despite modern therapy and after adjusting for other risk factors. These persist also despite the longer duration of therapy among boys,” he said in an oral abstract presentation during the annual meeting of the American Society of Pediatric Hematology/Oncology. (Abstract 2025).

Among pediatric patients with T-cell lineage ALL (T-ALL), however, there were no significant sex-based differences in either EFS or OS, he said.

Although survival for children with ALL has continued to improve, previous studies found inferior survival outcomes in boys, and suggested that the difference might be explained by imbalances in risk factors.

To see whether sex-based disparities persist with modern intensive therapy protocols after adjustment for risk factors, and to determine whether there are sex-based differences in toxicities or patterns of treatment failure, Dr. Gupta and colleagues created a cohort of all patients age 1-30 years enrolled in frontline COG trial for B-ALL and T-ALL from 2004 to 2014.

During this period, boys received an extra year of maintenance. Cranial radiation was limited to B-ALL patients with slow treatment responses and central nervous system status 3, signifying definite CNS involvement. Among patients with T-ALL, cranial radiation was given to all intermediate- and high-risk patients.
 

Sex differences small, but significant

The investigators identified a total of 8,202 patients (4,463 males and 3,739 females) with B-ALL, and 1,562 (1,161 males and 401 females) with T-ALL. Boys were likely to be older (P < .0001), and to have a small but significantly greater likelihood of having unfavorable B-ALL cytogenetics, compared with girls (P = .05).

Boys with B-ALL were less likely to be negative for minimal residual disease (76.1% vs. 78.1%, P = .04), but the opposite was true for those with T-ALL (59% vs. 56.8%, P = .01).

As noted before, among pediatric patients with B-ALL, EFS and OS were both inferior for males, with a hazard ratio for higher EFS rates in girls of 1.19 (P = .001) and a HR for OS of 1.17 (P = .046).

Both EFS and OS were similar between the sexes among patients with T-ALL.

The differences in EFS in patients with B-ALL was attributable to higher CNS relapses among boys (4.2% vs. 2.5%, P < .0001). The CNS relapses occurred at a median of 2.5 years in boys versus 2.1 years in girls, although most relapses occurred during therapy.

There were no differences in cumulative isolated bone marrow relapses, however.

Treatment-related mortality rates were the same, but osteonecrosis rates were significantly lower for boys, with a 5-year cumulative incidence of 5.2% versus 6.7% for girls (P = .001).
 

Possible explanations

Dr. Gupta noted that the inferior outcomes among boys may be attributable to extramedullary relapses among patients with B-ALL.

In addition, the lack of sex-based differences in T-ALL may be caused in part by the increased use of CNS radiation in this population. Previous studies in which CNS radiation was omitted showed an increase in CNS relapsed rates among boys but not girls, he pointed out.

“This does imply that in the more recent generation of T-lineage ALL treatment trials that we’ll need to monitor sex-based differences in outcome, as fewer and fewer patients with T-ALL disease received cranial radiation in these more recent trials and in contemporary therapy,” he said.

One possible mechanism for sex-based outcome differences might be differences in steroid metabolism, as suggested by the higher osteonecrosis rate among girls, he added.

In the question-and-answer following the presentation, William G. Woods, MD, from Emory University, Atlanta, asked what role testicular relapse played in outcomes.

Dr. Gupta replied that the investigators had considered that the excess risk for extramedullary relapse in boys might be accounted for by testicular relapse, but “when you take away testicular relapse from those numbers and really just concentrate on CNS, it’s still that substantial difference when you’re talking about B-lineage disease.”

In patients with T-ALL as well, CNS relapse was more common in boys after controlling for testicular relapse, he said.

Another audience member asked whether the data suggest a benefit to treating boys with CNS-penetrating drugs such as dexamethasone or high-dose methotrexate,

Dr. Gupta said that it’s still uncertain whether it is clinically sound to subject a boy with otherwise–standard-risk disease to more intensive high-risk therapy, given the relatively small absolute differences in outcomes between the sexes.

The study was supported by grants from the National Cancer Institute and the St. Baldrick’s Foundation. Dr. Gupta, Dr. Woods, and Dr. Meret had no relevant conflicts of interest to report.

Three-year survival rates were similarly high among postmenopausal women with high-risk early luminal breast cancer who were treated with either the neoadjuvant combination of letrozole and palbociclib (Ibrance) or standard neoadjuvant chemotherapy in the phase 2 NeoPAL study.

Progression-free survival (PFS) was a respective 86.7% and 87.2%, with a hazard ratio (HR) of 1.01 (P = .98) comparing the endocrine therapy and cyclin-dependent kinase (CDK) 4/6 inhibitor combination versus FEC/taxane chemotherapy.

There were also no differences between the two treatment arms in terms of invasive disease-free survival (iDFS, HR = 0.83, P = .71) or breast cancer–specific survival (BCSS), although the latter was an exploratory endpoint alongside overall survival (OS).

“The lack of difference is impressive,” said Hope S. Rugo, MD, FASCO, who commented independently on the study’s findings after their presentation at the European Society for Medical Oncology: Breast Cancer virtual meeting.

“Overall survival in patients who received chemotherapy appears to be better, but the very small numbers here make interpretation of this difference impossible,” observed Dr. Rugo, professor of medicine at the University of California San Francisco’s Helen Diller Family Comprehensive Cancer Center.

“Unfortunately, this study is underpowered for definitive conclusions,” acknowledged study investigator Suzette Delaloge, MD, associate professor of medical oncology at Institut Gustave Roussy in Villejuif, France.

However, “it shows that the nonchemotherapy, preoperative letrozole/palbociclib approach deserves further exploration and could be an option for a chemotherapy-free regimen in some specific cases.”
 

Primary data already reported

The NeoPAL study was an open-label, randomized study conducted in 27 centers throughout France that compared the preoperative use of letrozole plus palbociclib to neoadjuvant chemotherapy in 106 postmenopausal patients with either luminal A or B node-positive disease.

Patients were considered for inclusion in the trial if they had been newly diagnosed with estrogen receptor (ER)-positive, HER2-negative stage I-III breast cancer and were not candidates for breast conservation. Genetic testing was used to confirm that only those with luminal B, or luminal A and who were node positive were recruited.

Neoadjuvant treatment consisted of either letrozole (2.5 mg/day) and palbociclib (125 mg daily for 3 weeks out of 4 weeks) for 19 weeks or three 21-day cycles of 5-fluorouracil (500 mg/m²), epirubicin (100 mg/m²), and cyclophosphamide (500 mg/m²), followed by three 21-day cycles of docetaxel (100 mg/m²).

The primary endpoint was the pathological complete response (pCR), defined as a residual cancer burden (RCB) of 0 to 1. Results, which have already been reported, showed equivalent, but perhaps disappointingly low, pathological responses in both the letrozole/palbociclib and chemotherapy arms (3.8% and 5.9%, respectively).

There were, however, identical clinical responses (at around 75%) and “encouraging biomarker responses in the Prosigna-defined high risk luminal breast cancer population,” Dr. Delaloge said.

The NeoPAL findings were on par with those of the CORALLEEN study, Dr. Delaloge suggested. That trial, as Dr. Rugo has also pointed out, was conducted in 106 patients with luminal B early breast cancer and used a combination of letrozole and the CDK 4/6 inhibitor ribociclib (Kisquali).
 

Future studies needed

NeoPAL “is a small study with relatively short follow-up even for hormone receptor-positive, high-risk disease,” Dr. Rugo observed. However, she qualified “this short follow-up can be very meaningful in high-risk disease.” as shown by other CDK 4/6 inhibitor trials.

Dr. Rugo also noted: “Short-term biologic endpoints are clearly more informative following and during neoadjuvant endocrine therapy than pCR and this trial, as well as the data from previous studies, indicates that this is the case.”

Further, Dr. Rugo said: “Antiproliferative response is enhanced with CDK 4/6 inhibitors, but this doesn’t seem to translate into a difference in pCR. The lack of impact on longer term, outcome to date, provides support for ongoing trials.”

Two such trials are already underway. The 200-patient CARABELA trial started recruitment in March last year and is comparing endocrine therapy with letrozole plus the CDK 4/6 inhibitor abemaciclib (Verzenio) to standard chemotherapy in patients with hormone receptor–positive, high-risk Ki67 disease.

Then there is the ADAPTcycle trial, a large open-label, phase 3 trial that is randomizing patients based on Ki67 and recurrence score after a short preoperative induction with endocrine therapy to postoperative chemotherapy or to 2 years of endocrine therapy plus ribociclib, with both arms receiving a standard course of 5 years of endocrine therapy.

“These two studies have provided interesting information that will help us design studies in the future,” said Dr. Rugo.

Not only that, but they will also help “investigate the subgroups of patients that benefit the most from CDK 4/6 inhibitors and better study neoadjuvant endocrine therapy which is an important option for patients that can be evaluated in terms of its efficacy by short term measures of antiproliferative response.”

NeoPAL was sponsored by UNICANCER with funding from Pfizer and NanoString Technologies. Dr. Delaloge disclosed receiving research grants or funding via her institution from Pfizer, AstraZeneca, Roche, Merck, Sanofi, Lilly, Novartis, BMS, Orion, Daiichi, Puma, and Pierre Fabre. Dr. Rugo reported receipt of grants via her institution to perform clinical trials from Pfizer and multiple other companies. She disclosed receiving honoraria from PUMA, Samsung, and Mylan.

Three-year survival rates were similarly high among postmenopausal women with high-risk early luminal breast cancer who were treated with either the neoadjuvant combination of letrozole and palbociclib (Ibrance) or standard neoadjuvant chemotherapy in the phase 2 NeoPAL study.

Progression-free survival (PFS) was a respective 86.7% and 87.2%, with a hazard ratio (HR) of 1.01 (P = .98) comparing the endocrine therapy and cyclin-dependent kinase (CDK) 4/6 inhibitor combination versus FEC/taxane chemotherapy.

There were also no differences between the two treatment arms in terms of invasive disease-free survival (iDFS, HR = 0.83, P = .71) or breast cancer–specific survival (BCSS), although the latter was an exploratory endpoint alongside overall survival (OS).

“The lack of difference is impressive,” said Hope S. Rugo, MD, FASCO, who commented independently on the study’s findings after their presentation at the European Society for Medical Oncology: Breast Cancer virtual meeting.

“Overall survival in patients who received chemotherapy appears to be better, but the very small numbers here make interpretation of this difference impossible,” observed Dr. Rugo, professor of medicine at the University of California San Francisco’s Helen Diller Family Comprehensive Cancer Center.

“Unfortunately, this study is underpowered for definitive conclusions,” acknowledged study investigator Suzette Delaloge, MD, associate professor of medical oncology at Institut Gustave Roussy in Villejuif, France.

However, “it shows that the nonchemotherapy, preoperative letrozole/palbociclib approach deserves further exploration and could be an option for a chemotherapy-free regimen in some specific cases.”
 

Primary data already reported

The NeoPAL study was an open-label, randomized study conducted in 27 centers throughout France that compared the preoperative use of letrozole plus palbociclib to neoadjuvant chemotherapy in 106 postmenopausal patients with either luminal A or B node-positive disease.

Patients were considered for inclusion in the trial if they had been newly diagnosed with estrogen receptor (ER)-positive, HER2-negative stage I-III breast cancer and were not candidates for breast conservation. Genetic testing was used to confirm that only those with luminal B, or luminal A and who were node positive were recruited.

Neoadjuvant treatment consisted of either letrozole (2.5 mg/day) and palbociclib (125 mg daily for 3 weeks out of 4 weeks) for 19 weeks or three 21-day cycles of 5-fluorouracil (500 mg/m²), epirubicin (100 mg/m²), and cyclophosphamide (500 mg/m²), followed by three 21-day cycles of docetaxel (100 mg/m²).

The primary endpoint was the pathological complete response (pCR), defined as a residual cancer burden (RCB) of 0 to 1. Results, which have already been reported, showed equivalent, but perhaps disappointingly low, pathological responses in both the letrozole/palbociclib and chemotherapy arms (3.8% and 5.9%, respectively).

There were, however, identical clinical responses (at around 75%) and “encouraging biomarker responses in the Prosigna-defined high risk luminal breast cancer population,” Dr. Delaloge said.

The NeoPAL findings were on par with those of the CORALLEEN study, Dr. Delaloge suggested. That trial, as Dr. Rugo has also pointed out, was conducted in 106 patients with luminal B early breast cancer and used a combination of letrozole and the CDK 4/6 inhibitor ribociclib (Kisquali).
 

Future studies needed

NeoPAL “is a small study with relatively short follow-up even for hormone receptor-positive, high-risk disease,” Dr. Rugo observed. However, she qualified “this short follow-up can be very meaningful in high-risk disease.” as shown by other CDK 4/6 inhibitor trials.

Dr. Rugo also noted: “Short-term biologic endpoints are clearly more informative following and during neoadjuvant endocrine therapy than pCR and this trial, as well as the data from previous studies, indicates that this is the case.”

Further, Dr. Rugo said: “Antiproliferative response is enhanced with CDK 4/6 inhibitors, but this doesn’t seem to translate into a difference in pCR. The lack of impact on longer term, outcome to date, provides support for ongoing trials.”

Two such trials are already underway. The 200-patient CARABELA trial started recruitment in March last year and is comparing endocrine therapy with letrozole plus the CDK 4/6 inhibitor abemaciclib (Verzenio) to standard chemotherapy in patients with hormone receptor–positive, high-risk Ki67 disease.

Then there is the ADAPTcycle trial, a large open-label, phase 3 trial that is randomizing patients based on Ki67 and recurrence score after a short preoperative induction with endocrine therapy to postoperative chemotherapy or to 2 years of endocrine therapy plus ribociclib, with both arms receiving a standard course of 5 years of endocrine therapy.

“These two studies have provided interesting information that will help us design studies in the future,” said Dr. Rugo.

Not only that, but they will also help “investigate the subgroups of patients that benefit the most from CDK 4/6 inhibitors and better study neoadjuvant endocrine therapy which is an important option for patients that can be evaluated in terms of its efficacy by short term measures of antiproliferative response.”

NeoPAL was sponsored by UNICANCER with funding from Pfizer and NanoString Technologies. Dr. Delaloge disclosed receiving research grants or funding via her institution from Pfizer, AstraZeneca, Roche, Merck, Sanofi, Lilly, Novartis, BMS, Orion, Daiichi, Puma, and Pierre Fabre. Dr. Rugo reported receipt of grants via her institution to perform clinical trials from Pfizer and multiple other companies. She disclosed receiving honoraria from PUMA, Samsung, and Mylan.

Three-year survival rates were similarly high among postmenopausal women with high-risk early luminal breast cancer who were treated with either the neoadjuvant combination of letrozole and palbociclib (Ibrance) or standard neoadjuvant chemotherapy in the phase 2 NeoPAL study.

Progression-free survival (PFS) was a respective 86.7% and 87.2%, with a hazard ratio (HR) of 1.01 (P = .98) comparing the endocrine therapy and cyclin-dependent kinase (CDK) 4/6 inhibitor combination versus FEC/taxane chemotherapy.

There were also no differences between the two treatment arms in terms of invasive disease-free survival (iDFS, HR = 0.83, P = .71) or breast cancer–specific survival (BCSS), although the latter was an exploratory endpoint alongside overall survival (OS).

“The lack of difference is impressive,” said Hope S. Rugo, MD, FASCO, who commented independently on the study’s findings after their presentation at the European Society for Medical Oncology: Breast Cancer virtual meeting.

“Overall survival in patients who received chemotherapy appears to be better, but the very small numbers here make interpretation of this difference impossible,” observed Dr. Rugo, professor of medicine at the University of California San Francisco’s Helen Diller Family Comprehensive Cancer Center.

“Unfortunately, this study is underpowered for definitive conclusions,” acknowledged study investigator Suzette Delaloge, MD, associate professor of medical oncology at Institut Gustave Roussy in Villejuif, France.

However, “it shows that the nonchemotherapy, preoperative letrozole/palbociclib approach deserves further exploration and could be an option for a chemotherapy-free regimen in some specific cases.”
 

Primary data already reported

The NeoPAL study was an open-label, randomized study conducted in 27 centers throughout France that compared the preoperative use of letrozole plus palbociclib to neoadjuvant chemotherapy in 106 postmenopausal patients with either luminal A or B node-positive disease.

Patients were considered for inclusion in the trial if they had been newly diagnosed with estrogen receptor (ER)-positive, HER2-negative stage I-III breast cancer and were not candidates for breast conservation. Genetic testing was used to confirm that only those with luminal B, or luminal A and who were node positive were recruited.

Neoadjuvant treatment consisted of either letrozole (2.5 mg/day) and palbociclib (125 mg daily for 3 weeks out of 4 weeks) for 19 weeks or three 21-day cycles of 5-fluorouracil (500 mg/m²), epirubicin (100 mg/m²), and cyclophosphamide (500 mg/m²), followed by three 21-day cycles of docetaxel (100 mg/m²).

The primary endpoint was the pathological complete response (pCR), defined as a residual cancer burden (RCB) of 0 to 1. Results, which have already been reported, showed equivalent, but perhaps disappointingly low, pathological responses in both the letrozole/palbociclib and chemotherapy arms (3.8% and 5.9%, respectively).

There were, however, identical clinical responses (at around 75%) and “encouraging biomarker responses in the Prosigna-defined high risk luminal breast cancer population,” Dr. Delaloge said.

The NeoPAL findings were on par with those of the CORALLEEN study, Dr. Delaloge suggested. That trial, as Dr. Rugo has also pointed out, was conducted in 106 patients with luminal B early breast cancer and used a combination of letrozole and the CDK 4/6 inhibitor ribociclib (Kisquali).
 

Future studies needed

NeoPAL “is a small study with relatively short follow-up even for hormone receptor-positive, high-risk disease,” Dr. Rugo observed. However, she qualified “this short follow-up can be very meaningful in high-risk disease.” as shown by other CDK 4/6 inhibitor trials.

Dr. Rugo also noted: “Short-term biologic endpoints are clearly more informative following and during neoadjuvant endocrine therapy than pCR and this trial, as well as the data from previous studies, indicates that this is the case.”

Further, Dr. Rugo said: “Antiproliferative response is enhanced with CDK 4/6 inhibitors, but this doesn’t seem to translate into a difference in pCR. The lack of impact on longer term, outcome to date, provides support for ongoing trials.”

Two such trials are already underway. The 200-patient CARABELA trial started recruitment in March last year and is comparing endocrine therapy with letrozole plus the CDK 4/6 inhibitor abemaciclib (Verzenio) to standard chemotherapy in patients with hormone receptor–positive, high-risk Ki67 disease.

Then there is the ADAPTcycle trial, a large open-label, phase 3 trial that is randomizing patients based on Ki67 and recurrence score after a short preoperative induction with endocrine therapy to postoperative chemotherapy or to 2 years of endocrine therapy plus ribociclib, with both arms receiving a standard course of 5 years of endocrine therapy.

“These two studies have provided interesting information that will help us design studies in the future,” said Dr. Rugo.

Not only that, but they will also help “investigate the subgroups of patients that benefit the most from CDK 4/6 inhibitors and better study neoadjuvant endocrine therapy which is an important option for patients that can be evaluated in terms of its efficacy by short term measures of antiproliferative response.”

NeoPAL was sponsored by UNICANCER with funding from Pfizer and NanoString Technologies. Dr. Delaloge disclosed receiving research grants or funding via her institution from Pfizer, AstraZeneca, Roche, Merck, Sanofi, Lilly, Novartis, BMS, Orion, Daiichi, Puma, and Pierre Fabre. Dr. Rugo reported receipt of grants via her institution to perform clinical trials from Pfizer and multiple other companies. She disclosed receiving honoraria from PUMA, Samsung, and Mylan.

For patients with COVID-19 admitted to intensive care, giving atorvastatin 20 mg/d did not result in a significant reduction in risk for venous or arterial thrombosis, for treatment with extracorporeal membrane oxygenation (ECMO), or for all-cause mortality, compared with placebo in the INSPIRATION-S study.

However, there was a suggestion of benefit in the subgroup of patients who were treated within 7 days of COVID-19 symptom onset.

The study was presented by Behnood Bikdeli, MD, Brigham and Women’s Hospital, Boston, on May 16 at the annual scientific sessions of the American College of Cardiology.

He explained that COVID-19 is characterized by an exuberant immune response and that there is a potential for thrombotic events because of enhanced endothelial activation and a prothrombotic state.

“In this context, it is interesting to think about statins as potential agents to be studied in COVID-19, because as well as having lipid-lowering actions, they are also thought to have anti-inflammatory and antithrombotic effects,” he said.

In the HARP-2 trial of simvastatin in acute respiratory distress syndrome (ARDS), published a few years ago, the main results were neutral, but in the subgroup of patients with hyperinflammatory ARDS, there was a reduction in mortality with simvastatin in comparison with placebo, Dr. Bikdeli noted.

Moreover, in a series of observational studies of patients with COVID-19, use of statins was associated with a reduction in mortality among hospitalized patients. However, there are limited high-quality data to guide clinical practice, he said.

The INSPIRATION study, conducted in 11 hospitals in Iran, had a two-by-two factorial design to investigate different anticoagulant strategies and the use of atorvastatin for COVID-19 patients in the ICU.

In the anticoagulation part of the trial, which was published in JAMA in March 2020, there was no difference in the primary endpoint of an intermediate dose and standard dose of enoxaparin.

For the statin part of the trial (INSPIRATION-S), 605 patients were randomly assigned to receive atorvastatin 20 mg daily or placebo. Patients who had been taking statins beforehand were excluded. Baseline characteristics were similar for the two groups, with around a quarter of patients taking aspirin and more than 90% taking steroids.

Results showed that atorvastatin was not associated with a significant reduction in the primary outcome – a composite of adjudicated venous or arterial thrombosis, treatment with ECMO, or mortality within 30 days – which occurred in 32.7% of the statin group versus 36.3% of the placebo group (odds ratio, 0.84; P = .35).

Atorvastatin was not associated with any significant differences in any of the individual components of the primary composite endpoint. There was also no significant difference in any of the safety endpoints, which included major bleeding and elevations in liver enzyme levels.

Subgroup analyses were mostly consistent with the main findings, with one exception.

In the subgroup of patients who presented within the first 7 days of COVID-19 symptom onset, there was a hint of a potential protective effect with atorvastatin.

In this group of 171 patients, the primary endpoint occurred in 30.9% of those taking atorvastatin versus 40.3% of those taking placebo (OR, 0.60; P = .055).

“This is an interesting observation, and it is plausible, as these patients may be in a different phase of COVID-19 disease. But we need to be cognizant of the multiplicity of comparisons, and this needs to be further investigated in subsequent studies,” Dr. Bikdeli said.
 

Higher dose in less sick patients a better strategy?

Discussing the study at the ACC presentation, Binita Shah, MD, said the importance of enrolling COVID-19 patients into clinical trials was paramount but that these patients in the ICU may not have been the right population in which to test a statin.

“Maybe for these very sick patients, it is just too late. Trying to rein in the inflammatory cytokine storm and the interaction with thrombosis at this point is very difficult,” Dr. Shah commented.

She suggested that it might be appropriate to try statins in an earlier phase of the disease in order to prevent the inflammatory process, rather than trying to stop it after it had already started.

Dr. Shah also questioned the use of such a low dose of atorvastatin for these patients. “In the cardiovascular literature – at least in ACS [acute coronary syndrome] – high statin doses are used to see short-term benefits. In this very inflammatory milieu, I wonder whether a high-intensity regimen would be more beneficial,” she speculated.

Dr. Bikdeli replied that a low dose of atorvastatin was chosen because early on, several antiviral agents, such as ritonavir, were being used for COVID-19 patients, and these drugs were associated with increases in liver enzyme levels.

“We didn’t want to exacerbate that with high doses of statins,” he said. “But we have now established the safety profile of atorvastatin in these patients, and in retrospect, yes, a higher dose might have been better.”

The INSPIRATION study was funded by the Rajaie Cardiovascular Medical and Research Center, Tehran, Iran. Dr. Bikdeli has disclosed no relevant financial relationships.

A version of this article first appeared on Medscape.com.

For patients with COVID-19 admitted to intensive care, giving atorvastatin 20 mg/d did not result in a significant reduction in risk for venous or arterial thrombosis, for treatment with extracorporeal membrane oxygenation (ECMO), or for all-cause mortality, compared with placebo in the INSPIRATION-S study.

However, there was a suggestion of benefit in the subgroup of patients who were treated within 7 days of COVID-19 symptom onset.

The study was presented by Behnood Bikdeli, MD, Brigham and Women’s Hospital, Boston, on May 16 at the annual scientific sessions of the American College of Cardiology.

He explained that COVID-19 is characterized by an exuberant immune response and that there is a potential for thrombotic events because of enhanced endothelial activation and a prothrombotic state.

“In this context, it is interesting to think about statins as potential agents to be studied in COVID-19, because as well as having lipid-lowering actions, they are also thought to have anti-inflammatory and antithrombotic effects,” he said.

In the HARP-2 trial of simvastatin in acute respiratory distress syndrome (ARDS), published a few years ago, the main results were neutral, but in the subgroup of patients with hyperinflammatory ARDS, there was a reduction in mortality with simvastatin in comparison with placebo, Dr. Bikdeli noted.

Moreover, in a series of observational studies of patients with COVID-19, use of statins was associated with a reduction in mortality among hospitalized patients. However, there are limited high-quality data to guide clinical practice, he said.

The INSPIRATION study, conducted in 11 hospitals in Iran, had a two-by-two factorial design to investigate different anticoagulant strategies and the use of atorvastatin for COVID-19 patients in the ICU.

In the anticoagulation part of the trial, which was published in JAMA in March 2020, there was no difference in the primary endpoint of an intermediate dose and standard dose of enoxaparin.

For the statin part of the trial (INSPIRATION-S), 605 patients were randomly assigned to receive atorvastatin 20 mg daily or placebo. Patients who had been taking statins beforehand were excluded. Baseline characteristics were similar for the two groups, with around a quarter of patients taking aspirin and more than 90% taking steroids.

Results showed that atorvastatin was not associated with a significant reduction in the primary outcome – a composite of adjudicated venous or arterial thrombosis, treatment with ECMO, or mortality within 30 days – which occurred in 32.7% of the statin group versus 36.3% of the placebo group (odds ratio, 0.84; P = .35).

Atorvastatin was not associated with any significant differences in any of the individual components of the primary composite endpoint. There was also no significant difference in any of the safety endpoints, which included major bleeding and elevations in liver enzyme levels.

Subgroup analyses were mostly consistent with the main findings, with one exception.

In the subgroup of patients who presented within the first 7 days of COVID-19 symptom onset, there was a hint of a potential protective effect with atorvastatin.

In this group of 171 patients, the primary endpoint occurred in 30.9% of those taking atorvastatin versus 40.3% of those taking placebo (OR, 0.60; P = .055).

“This is an interesting observation, and it is plausible, as these patients may be in a different phase of COVID-19 disease. But we need to be cognizant of the multiplicity of comparisons, and this needs to be further investigated in subsequent studies,” Dr. Bikdeli said.
 

Higher dose in less sick patients a better strategy?

Discussing the study at the ACC presentation, Binita Shah, MD, said the importance of enrolling COVID-19 patients into clinical trials was paramount but that these patients in the ICU may not have been the right population in which to test a statin.

“Maybe for these very sick patients, it is just too late. Trying to rein in the inflammatory cytokine storm and the interaction with thrombosis at this point is very difficult,” Dr. Shah commented.

She suggested that it might be appropriate to try statins in an earlier phase of the disease in order to prevent the inflammatory process, rather than trying to stop it after it had already started.

Dr. Shah also questioned the use of such a low dose of atorvastatin for these patients. “In the cardiovascular literature – at least in ACS [acute coronary syndrome] – high statin doses are used to see short-term benefits. In this very inflammatory milieu, I wonder whether a high-intensity regimen would be more beneficial,” she speculated.

Dr. Bikdeli replied that a low dose of atorvastatin was chosen because early on, several antiviral agents, such as ritonavir, were being used for COVID-19 patients, and these drugs were associated with increases in liver enzyme levels.

“We didn’t want to exacerbate that with high doses of statins,” he said. “But we have now established the safety profile of atorvastatin in these patients, and in retrospect, yes, a higher dose might have been better.”

The INSPIRATION study was funded by the Rajaie Cardiovascular Medical and Research Center, Tehran, Iran. Dr. Bikdeli has disclosed no relevant financial relationships.

A version of this article first appeared on Medscape.com.

For patients with COVID-19 admitted to intensive care, giving atorvastatin 20 mg/d did not result in a significant reduction in risk for venous or arterial thrombosis, for treatment with extracorporeal membrane oxygenation (ECMO), or for all-cause mortality, compared with placebo in the INSPIRATION-S study.

However, there was a suggestion of benefit in the subgroup of patients who were treated within 7 days of COVID-19 symptom onset.

The study was presented by Behnood Bikdeli, MD, Brigham and Women’s Hospital, Boston, on May 16 at the annual scientific sessions of the American College of Cardiology.

He explained that COVID-19 is characterized by an exuberant immune response and that there is a potential for thrombotic events because of enhanced endothelial activation and a prothrombotic state.

“In this context, it is interesting to think about statins as potential agents to be studied in COVID-19, because as well as having lipid-lowering actions, they are also thought to have anti-inflammatory and antithrombotic effects,” he said.

In the HARP-2 trial of simvastatin in acute respiratory distress syndrome (ARDS), published a few years ago, the main results were neutral, but in the subgroup of patients with hyperinflammatory ARDS, there was a reduction in mortality with simvastatin in comparison with placebo, Dr. Bikdeli noted.

Moreover, in a series of observational studies of patients with COVID-19, use of statins was associated with a reduction in mortality among hospitalized patients. However, there are limited high-quality data to guide clinical practice, he said.

The INSPIRATION study, conducted in 11 hospitals in Iran, had a two-by-two factorial design to investigate different anticoagulant strategies and the use of atorvastatin for COVID-19 patients in the ICU.

In the anticoagulation part of the trial, which was published in JAMA in March 2020, there was no difference in the primary endpoint of an intermediate dose and standard dose of enoxaparin.

For the statin part of the trial (INSPIRATION-S), 605 patients were randomly assigned to receive atorvastatin 20 mg daily or placebo. Patients who had been taking statins beforehand were excluded. Baseline characteristics were similar for the two groups, with around a quarter of patients taking aspirin and more than 90% taking steroids.

Results showed that atorvastatin was not associated with a significant reduction in the primary outcome – a composite of adjudicated venous or arterial thrombosis, treatment with ECMO, or mortality within 30 days – which occurred in 32.7% of the statin group versus 36.3% of the placebo group (odds ratio, 0.84; P = .35).

Atorvastatin was not associated with any significant differences in any of the individual components of the primary composite endpoint. There was also no significant difference in any of the safety endpoints, which included major bleeding and elevations in liver enzyme levels.

Subgroup analyses were mostly consistent with the main findings, with one exception.

In the subgroup of patients who presented within the first 7 days of COVID-19 symptom onset, there was a hint of a potential protective effect with atorvastatin.

In this group of 171 patients, the primary endpoint occurred in 30.9% of those taking atorvastatin versus 40.3% of those taking placebo (OR, 0.60; P = .055).

“This is an interesting observation, and it is plausible, as these patients may be in a different phase of COVID-19 disease. But we need to be cognizant of the multiplicity of comparisons, and this needs to be further investigated in subsequent studies,” Dr. Bikdeli said.
 

Higher dose in less sick patients a better strategy?

Discussing the study at the ACC presentation, Binita Shah, MD, said the importance of enrolling COVID-19 patients into clinical trials was paramount but that these patients in the ICU may not have been the right population in which to test a statin.

“Maybe for these very sick patients, it is just too late. Trying to rein in the inflammatory cytokine storm and the interaction with thrombosis at this point is very difficult,” Dr. Shah commented.

She suggested that it might be appropriate to try statins in an earlier phase of the disease in order to prevent the inflammatory process, rather than trying to stop it after it had already started.

Dr. Shah also questioned the use of such a low dose of atorvastatin for these patients. “In the cardiovascular literature – at least in ACS [acute coronary syndrome] – high statin doses are used to see short-term benefits. In this very inflammatory milieu, I wonder whether a high-intensity regimen would be more beneficial,” she speculated.

Dr. Bikdeli replied that a low dose of atorvastatin was chosen because early on, several antiviral agents, such as ritonavir, were being used for COVID-19 patients, and these drugs were associated with increases in liver enzyme levels.

“We didn’t want to exacerbate that with high doses of statins,” he said. “But we have now established the safety profile of atorvastatin in these patients, and in retrospect, yes, a higher dose might have been better.”

The INSPIRATION study was funded by the Rajaie Cardiovascular Medical and Research Center, Tehran, Iran. Dr. Bikdeli has disclosed no relevant financial relationships.

A version of this article first appeared on Medscape.com.

Infectious disease physicians are among the doctors carrying the largest burdens in the COVID-19 pandemic.

Perhaps not surprisingly, they were the specialists least likely to feel they were fairly compensated in the Medscape Infectious Diseases Physician Compensation Report 2021.

Only 44% said the pay was fair (down from 51% the prior year) compared with those at the high end – 79% in oncology, 69% in psychiatry, and 68% in plastic surgery who answered that way.

Income, which averaged $245,000, varied little from the previous year overall, according to the survey, but nearly one-third of ID physicians saw a decline in pay.

Again this year, ID physicians ranked near the bottom on the compensation spectrum. Pediatricians were lowest paid at $221,000. Plastic surgeons topped the chart at $526,000, followed by orthopedists at $511,000.

At the same time, the ID specialty is facing increasing shortages, a gap made even more visible in the pandemic. Medscape reported last year that nearly 80% of U.S. counties have no infectious disease specialists.

Thomas File Jr., MD, last year’s president of the Infectious Diseases Society of America, emphasized that COVID-19 is not the only threat that ID specialists have had to deal with or will have. He cited the threats that Zika and SARS posed in past years.

“COVID-19 illustrates the need for more trained ID specialists, because we know we’re going to be seeing more outbreaks in the future,” he said in an interview at the onset of the pandemic in March 2020.
 

Longer hours in pandemic

ID physicians’ hours generally increased during the pandemic, and they remain inflated by 8 hours per week (60 compared with 52 prepandemic) as the nation struggles to manage continuing COVID-19 infections. Physicians in critical care and public health and preventive medicine are seeing heavier workloads as well, by an average of 6-7 hours per week.

At the same time, ID physicians spent the most time of physicians in all specialties on paperwork and administrative tasks. Those tasks, which include electronic health record entry and clinical reading, took ID doctors 24.2 hours a week, more the twice the hours spent by those in anesthesiology (10.1), ophthalmology (10.3), and radiology (11.6).

The 24.2 hours was a substantial increase from the last report, when ID physicians said they spent 18.5 hours on the tasks.

The survey asked about the most challenging part of the job. ID physicians reported “long hours” as number one followed by “having so many rules and regulations.”

Only 4% said the danger or risk associated with treating COVID-19 patients was the most challenging part.

The top two aspects of their work they deemed most rewarding were “being very good at what I do” (chosen by 33%) and “knowing that I’m making the world a better place” (31%).
 

Patient volume up 17%

ID physicians reported seeing 78 patients per week in this report compared with 66 prepandemic, a 17% increase. Conversely, pediatricians saw an 18% drop in patient visits, followed by dermatologists, orthopedists and otolaryngologists (all down about 15%).

Despite the challenges and dissatisfaction with pay, the great majority of  ID physicians said they would choose both medicine (83%) and their specialty (89%, up from 85% last year) again.
 

A version of this article first appeared on Medscape.com.

Infectious disease physicians are among the doctors carrying the largest burdens in the COVID-19 pandemic.

Perhaps not surprisingly, they were the specialists least likely to feel they were fairly compensated in the Medscape Infectious Diseases Physician Compensation Report 2021.

Only 44% said the pay was fair (down from 51% the prior year) compared with those at the high end – 79% in oncology, 69% in psychiatry, and 68% in plastic surgery who answered that way.

Income, which averaged $245,000, varied little from the previous year overall, according to the survey, but nearly one-third of ID physicians saw a decline in pay.

Again this year, ID physicians ranked near the bottom on the compensation spectrum. Pediatricians were lowest paid at $221,000. Plastic surgeons topped the chart at $526,000, followed by orthopedists at $511,000.

At the same time, the ID specialty is facing increasing shortages, a gap made even more visible in the pandemic. Medscape reported last year that nearly 80% of U.S. counties have no infectious disease specialists.

Thomas File Jr., MD, last year’s president of the Infectious Diseases Society of America, emphasized that COVID-19 is not the only threat that ID specialists have had to deal with or will have. He cited the threats that Zika and SARS posed in past years.

“COVID-19 illustrates the need for more trained ID specialists, because we know we’re going to be seeing more outbreaks in the future,” he said in an interview at the onset of the pandemic in March 2020.
 

Longer hours in pandemic

ID physicians’ hours generally increased during the pandemic, and they remain inflated by 8 hours per week (60 compared with 52 prepandemic) as the nation struggles to manage continuing COVID-19 infections. Physicians in critical care and public health and preventive medicine are seeing heavier workloads as well, by an average of 6-7 hours per week.

At the same time, ID physicians spent the most time of physicians in all specialties on paperwork and administrative tasks. Those tasks, which include electronic health record entry and clinical reading, took ID doctors 24.2 hours a week, more the twice the hours spent by those in anesthesiology (10.1), ophthalmology (10.3), and radiology (11.6).

The 24.2 hours was a substantial increase from the last report, when ID physicians said they spent 18.5 hours on the tasks.

The survey asked about the most challenging part of the job. ID physicians reported “long hours” as number one followed by “having so many rules and regulations.”

Only 4% said the danger or risk associated with treating COVID-19 patients was the most challenging part.

The top two aspects of their work they deemed most rewarding were “being very good at what I do” (chosen by 33%) and “knowing that I’m making the world a better place” (31%).
 

Patient volume up 17%

ID physicians reported seeing 78 patients per week in this report compared with 66 prepandemic, a 17% increase. Conversely, pediatricians saw an 18% drop in patient visits, followed by dermatologists, orthopedists and otolaryngologists (all down about 15%).

Despite the challenges and dissatisfaction with pay, the great majority of  ID physicians said they would choose both medicine (83%) and their specialty (89%, up from 85% last year) again.
 

A version of this article first appeared on Medscape.com.

Infectious disease physicians are among the doctors carrying the largest burdens in the COVID-19 pandemic.

Perhaps not surprisingly, they were the specialists least likely to feel they were fairly compensated in the Medscape Infectious Diseases Physician Compensation Report 2021.

Only 44% said the pay was fair (down from 51% the prior year) compared with those at the high end – 79% in oncology, 69% in psychiatry, and 68% in plastic surgery who answered that way.

Income, which averaged $245,000, varied little from the previous year overall, according to the survey, but nearly one-third of ID physicians saw a decline in pay.

Again this year, ID physicians ranked near the bottom on the compensation spectrum. Pediatricians were lowest paid at $221,000. Plastic surgeons topped the chart at $526,000, followed by orthopedists at $511,000.

At the same time, the ID specialty is facing increasing shortages, a gap made even more visible in the pandemic. Medscape reported last year that nearly 80% of U.S. counties have no infectious disease specialists.

Thomas File Jr., MD, last year’s president of the Infectious Diseases Society of America, emphasized that COVID-19 is not the only threat that ID specialists have had to deal with or will have. He cited the threats that Zika and SARS posed in past years.

“COVID-19 illustrates the need for more trained ID specialists, because we know we’re going to be seeing more outbreaks in the future,” he said in an interview at the onset of the pandemic in March 2020.
 

Longer hours in pandemic

ID physicians’ hours generally increased during the pandemic, and they remain inflated by 8 hours per week (60 compared with 52 prepandemic) as the nation struggles to manage continuing COVID-19 infections. Physicians in critical care and public health and preventive medicine are seeing heavier workloads as well, by an average of 6-7 hours per week.

At the same time, ID physicians spent the most time of physicians in all specialties on paperwork and administrative tasks. Those tasks, which include electronic health record entry and clinical reading, took ID doctors 24.2 hours a week, more the twice the hours spent by those in anesthesiology (10.1), ophthalmology (10.3), and radiology (11.6).

The 24.2 hours was a substantial increase from the last report, when ID physicians said they spent 18.5 hours on the tasks.

The survey asked about the most challenging part of the job. ID physicians reported “long hours” as number one followed by “having so many rules and regulations.”

Only 4% said the danger or risk associated with treating COVID-19 patients was the most challenging part.

The top two aspects of their work they deemed most rewarding were “being very good at what I do” (chosen by 33%) and “knowing that I’m making the world a better place” (31%).
 

Patient volume up 17%

ID physicians reported seeing 78 patients per week in this report compared with 66 prepandemic, a 17% increase. Conversely, pediatricians saw an 18% drop in patient visits, followed by dermatologists, orthopedists and otolaryngologists (all down about 15%).

Despite the challenges and dissatisfaction with pay, the great majority of  ID physicians said they would choose both medicine (83%) and their specialty (89%, up from 85% last year) again.
 

A version of this article first appeared on Medscape.com.

GI Genius recently became the first Food and Drug Administration–approved device to use artificial intelligence (AI) for endoscopy. Soon, similar technology may give gastroenterologists an edge before they even walk into the procedure room.

AI can provide highly accurate risk scores for patients with suspected upper GI bleeding, and make a recommendation for discharge or hospitalization, according to Dennis Shung, MD, MHS, a clinical instructor at Yale University, New Haven, Conn. And this could provide extensive benefit.

“Acute gastrointestinal bleeding is the most common gastrointestinal diagnosis requiring hospitalization. It costs around $19.2 billion per year,” Dr. Shung said, citing a study from Gastroenterology. He made these remarks during a virtual presentation at the 2021 AGA Tech Summit sponsored by the AGA Center for GI Innovation and Technology.

Emergency department visits for upper GI bleeding increased 17% from 2006 to 2014, Dr. Shung added, suggesting a rising trend.
 

The trouble with using risk scores

A variety of conventional risk scores are presently available to help manage these patients. Generally, they use a composite outcome of hemostatic intervention, transfusion, or death to determine which patients should be hospitalized (high risk) and which patients can go home (low risk). Although these models can offer high sensitivity, they remain underutilized.

“[Clinical risk scores] are cumbersome, it’s difficult to calculate them, [and] you may not remember to do that in your busy workflow,” Dr. Shung said.

He pointed out that low implementation may also stem from poorly defined clinical responsibilities.

“[Observing] providers caring for patients with GI bleeding showed that there was a culture of not taking ownership,” he said. “Emergency department physicians thought that it was the gastroenterologists who needed to [perform risk scoring]. Gastroenterologists thought it was the ED [physicians’ responsibility].”

To overcome these pitfalls, Dr. Shung and colleagues are developing AI that automates risk analysis for upper GI bleeding by integrating the process into the clinical workflow. Like GI Genius, their strategy relies upon machine learning, which is a type of AI that can improve automatically without being explicitly programmed.

Their most recent study (Sci Rep. 2021 Apr 23;11[1]:8827) involved a machine learning model that could predict transfusion in patients admitted for acute GI bleeding. The model was developed and internally validated in a cohort of 2,524 patients, then shown to outperform conventional regression-based models when externally validated in 1,526 patients similarly admitted at large urban hospitals.
 

Google Maps for GI bleeding

“The future, as I envision it, is a Google Maps for GI bleeding,” Dr. Shung said, referring to how the popular web-mapping product analyzes real-time data, such as weather and traffic patterns, to provide the best route and an estimated time of arrival. “With the electronic health record, we have the ability to personalize care by basically using data obtained during the clinical encounter to generate risk assessment in real time.”

In other words, machine learning software reads a patient’s electronic health record, runs relevant data through an algorithm, and produces both a risk score and a clinical recommendation. In the case of suspected upper GI bleeding, the clinician is advised to either discharge for outpatient endoscopy or hospitalize for inpatient evaluation.

Because the quality and consistency of data in EHRs can vary, the most advanced form of machine learning – deep learning – is needed to make this a clinical reality. Deep learning converts simpler concepts into complex ones. In this scenario, that would mean deciding which clinical data are relevant and which are just noise. Taking this a step further, deep learning can actually “draw conclusions” from what’s missing.

“There are huge challenges in [irregular data] that need to be overcome,” Dr. Shung said in an interview. “But I see it as an opportunity. When you see things that are irregularly sampled, when you see things are missing – they mean something. They mean that a human has decided that that is not the way we should do things because this patient doesn’t need it. And I think there is a lot of value in learning how to model those things.”
 

The road to clinical implementation

With further research and validation, deep learning models for gastroenterology are likely to play a role in clinical decision-making, according to Dr. Shung. But to reach the clinic floor, developers will need to outsmart some more fundamental obstacles. “The main thing that’s really barring [AI risk modeling] from being used is the reimbursement issue,” he said, referring to uncertainty in how payers will cover associated costs.

In an interview, Sushovan Guha, MD, PhD, moderator of the virtual session and codirector of the center for interventional gastroenterology at UTHealth (iGUT) in Houston, pointed out another financial unknown: liability.

“What happens if there is an error?” he asked. “It’s done by the computers, but who is at fault?”

In addition to these challenges, some clinicians may need to be persuaded before they are willing to trust an algorithm with a patient’s life.

“We have to have community physicians convinced about the importance of using these tools to further improve their clinical practice,” Dr. Guha said. To this end, he added, “It’s time for us to accept and adapt, and make our decision-making process much more efficient.”

The investigators disclosed no relevant conflicts of interest.

GI Genius recently became the first Food and Drug Administration–approved device to use artificial intelligence (AI) for endoscopy. Soon, similar technology may give gastroenterologists an edge before they even walk into the procedure room.

AI can provide highly accurate risk scores for patients with suspected upper GI bleeding, and make a recommendation for discharge or hospitalization, according to Dennis Shung, MD, MHS, a clinical instructor at Yale University, New Haven, Conn. And this could provide extensive benefit.

“Acute gastrointestinal bleeding is the most common gastrointestinal diagnosis requiring hospitalization. It costs around $19.2 billion per year,” Dr. Shung said, citing a study from Gastroenterology. He made these remarks during a virtual presentation at the 2021 AGA Tech Summit sponsored by the AGA Center for GI Innovation and Technology.

Emergency department visits for upper GI bleeding increased 17% from 2006 to 2014, Dr. Shung added, suggesting a rising trend.
 

The trouble with using risk scores

A variety of conventional risk scores are presently available to help manage these patients. Generally, they use a composite outcome of hemostatic intervention, transfusion, or death to determine which patients should be hospitalized (high risk) and which patients can go home (low risk). Although these models can offer high sensitivity, they remain underutilized.

“[Clinical risk scores] are cumbersome, it’s difficult to calculate them, [and] you may not remember to do that in your busy workflow,” Dr. Shung said.

He pointed out that low implementation may also stem from poorly defined clinical responsibilities.

“[Observing] providers caring for patients with GI bleeding showed that there was a culture of not taking ownership,” he said. “Emergency department physicians thought that it was the gastroenterologists who needed to [perform risk scoring]. Gastroenterologists thought it was the ED [physicians’ responsibility].”

To overcome these pitfalls, Dr. Shung and colleagues are developing AI that automates risk analysis for upper GI bleeding by integrating the process into the clinical workflow. Like GI Genius, their strategy relies upon machine learning, which is a type of AI that can improve automatically without being explicitly programmed.

Their most recent study (Sci Rep. 2021 Apr 23;11[1]:8827) involved a machine learning model that could predict transfusion in patients admitted for acute GI bleeding. The model was developed and internally validated in a cohort of 2,524 patients, then shown to outperform conventional regression-based models when externally validated in 1,526 patients similarly admitted at large urban hospitals.
 

Google Maps for GI bleeding

“The future, as I envision it, is a Google Maps for GI bleeding,” Dr. Shung said, referring to how the popular web-mapping product analyzes real-time data, such as weather and traffic patterns, to provide the best route and an estimated time of arrival. “With the electronic health record, we have the ability to personalize care by basically using data obtained during the clinical encounter to generate risk assessment in real time.”

In other words, machine learning software reads a patient’s electronic health record, runs relevant data through an algorithm, and produces both a risk score and a clinical recommendation. In the case of suspected upper GI bleeding, the clinician is advised to either discharge for outpatient endoscopy or hospitalize for inpatient evaluation.

Because the quality and consistency of data in EHRs can vary, the most advanced form of machine learning – deep learning – is needed to make this a clinical reality. Deep learning converts simpler concepts into complex ones. In this scenario, that would mean deciding which clinical data are relevant and which are just noise. Taking this a step further, deep learning can actually “draw conclusions” from what’s missing.

“There are huge challenges in [irregular data] that need to be overcome,” Dr. Shung said in an interview. “But I see it as an opportunity. When you see things that are irregularly sampled, when you see things are missing – they mean something. They mean that a human has decided that that is not the way we should do things because this patient doesn’t need it. And I think there is a lot of value in learning how to model those things.”
 

The road to clinical implementation

With further research and validation, deep learning models for gastroenterology are likely to play a role in clinical decision-making, according to Dr. Shung. But to reach the clinic floor, developers will need to outsmart some more fundamental obstacles. “The main thing that’s really barring [AI risk modeling] from being used is the reimbursement issue,” he said, referring to uncertainty in how payers will cover associated costs.

In an interview, Sushovan Guha, MD, PhD, moderator of the virtual session and codirector of the center for interventional gastroenterology at UTHealth (iGUT) in Houston, pointed out another financial unknown: liability.

“What happens if there is an error?” he asked. “It’s done by the computers, but who is at fault?”

In addition to these challenges, some clinicians may need to be persuaded before they are willing to trust an algorithm with a patient’s life.

“We have to have community physicians convinced about the importance of using these tools to further improve their clinical practice,” Dr. Guha said. To this end, he added, “It’s time for us to accept and adapt, and make our decision-making process much more efficient.”

The investigators disclosed no relevant conflicts of interest.

GI Genius recently became the first Food and Drug Administration–approved device to use artificial intelligence (AI) for endoscopy. Soon, similar technology may give gastroenterologists an edge before they even walk into the procedure room.

AI can provide highly accurate risk scores for patients with suspected upper GI bleeding, and make a recommendation for discharge or hospitalization, according to Dennis Shung, MD, MHS, a clinical instructor at Yale University, New Haven, Conn. And this could provide extensive benefit.

“Acute gastrointestinal bleeding is the most common gastrointestinal diagnosis requiring hospitalization. It costs around $19.2 billion per year,” Dr. Shung said, citing a study from Gastroenterology. He made these remarks during a virtual presentation at the 2021 AGA Tech Summit sponsored by the AGA Center for GI Innovation and Technology.

Emergency department visits for upper GI bleeding increased 17% from 2006 to 2014, Dr. Shung added, suggesting a rising trend.
 

The trouble with using risk scores

A variety of conventional risk scores are presently available to help manage these patients. Generally, they use a composite outcome of hemostatic intervention, transfusion, or death to determine which patients should be hospitalized (high risk) and which patients can go home (low risk). Although these models can offer high sensitivity, they remain underutilized.

“[Clinical risk scores] are cumbersome, it’s difficult to calculate them, [and] you may not remember to do that in your busy workflow,” Dr. Shung said.

He pointed out that low implementation may also stem from poorly defined clinical responsibilities.

“[Observing] providers caring for patients with GI bleeding showed that there was a culture of not taking ownership,” he said. “Emergency department physicians thought that it was the gastroenterologists who needed to [perform risk scoring]. Gastroenterologists thought it was the ED [physicians’ responsibility].”

To overcome these pitfalls, Dr. Shung and colleagues are developing AI that automates risk analysis for upper GI bleeding by integrating the process into the clinical workflow. Like GI Genius, their strategy relies upon machine learning, which is a type of AI that can improve automatically without being explicitly programmed.

Their most recent study (Sci Rep. 2021 Apr 23;11[1]:8827) involved a machine learning model that could predict transfusion in patients admitted for acute GI bleeding. The model was developed and internally validated in a cohort of 2,524 patients, then shown to outperform conventional regression-based models when externally validated in 1,526 patients similarly admitted at large urban hospitals.
 

Google Maps for GI bleeding

“The future, as I envision it, is a Google Maps for GI bleeding,” Dr. Shung said, referring to how the popular web-mapping product analyzes real-time data, such as weather and traffic patterns, to provide the best route and an estimated time of arrival. “With the electronic health record, we have the ability to personalize care by basically using data obtained during the clinical encounter to generate risk assessment in real time.”

In other words, machine learning software reads a patient’s electronic health record, runs relevant data through an algorithm, and produces both a risk score and a clinical recommendation. In the case of suspected upper GI bleeding, the clinician is advised to either discharge for outpatient endoscopy or hospitalize for inpatient evaluation.

Because the quality and consistency of data in EHRs can vary, the most advanced form of machine learning – deep learning – is needed to make this a clinical reality. Deep learning converts simpler concepts into complex ones. In this scenario, that would mean deciding which clinical data are relevant and which are just noise. Taking this a step further, deep learning can actually “draw conclusions” from what’s missing.

“There are huge challenges in [irregular data] that need to be overcome,” Dr. Shung said in an interview. “But I see it as an opportunity. When you see things that are irregularly sampled, when you see things are missing – they mean something. They mean that a human has decided that that is not the way we should do things because this patient doesn’t need it. And I think there is a lot of value in learning how to model those things.”
 

The road to clinical implementation

With further research and validation, deep learning models for gastroenterology are likely to play a role in clinical decision-making, according to Dr. Shung. But to reach the clinic floor, developers will need to outsmart some more fundamental obstacles. “The main thing that’s really barring [AI risk modeling] from being used is the reimbursement issue,” he said, referring to uncertainty in how payers will cover associated costs.

In an interview, Sushovan Guha, MD, PhD, moderator of the virtual session and codirector of the center for interventional gastroenterology at UTHealth (iGUT) in Houston, pointed out another financial unknown: liability.

“What happens if there is an error?” he asked. “It’s done by the computers, but who is at fault?”

In addition to these challenges, some clinicians may need to be persuaded before they are willing to trust an algorithm with a patient’s life.

“We have to have community physicians convinced about the importance of using these tools to further improve their clinical practice,” Dr. Guha said. To this end, he added, “It’s time for us to accept and adapt, and make our decision-making process much more efficient.”

The investigators disclosed no relevant conflicts of interest.

When the Biden administration announced who would serve on its COVID-19 task force, some asked why a mental health expert had not been included. I have a broader question: In light of the magnitude of the pandemic’s fallout, why doesn’t the administration create a mental health post parallel to the surgeon general?

I have been making the case for creation of a high-level mental health post for quite some time. In fact, in the late 1970s, toward the end of then-President Jimmy Carter’s term, I wrote and talked about the need for a special cabinet post of mental health. At the time I realized that, besides chronic mental disorders, the amount of mental distress people experienced from a myriad of life issues leading to anxiety, depression, even posttraumatic stress disorder (although not labeled as such then), needed focused and informed leadership.

Before the pandemic, the World Health Organization reported that depression was the leading cause of disability worldwide. In the prepandemic United States, mental and substance use disorders were the top cause of disability among younger people.

We’ve lost almost 600,000 people to COVID-19, and people have been unable to grieve properly. More than 2 million women have left the labor force to care for children and sick family members. As we continue to learn about the mental health–related devastation wrought by SARS-CoV-2 – particularly long-haul COVID-19 – it’s time to dust off my proposal, update it, and implement it.
 

Building on a good decision

Back in 2017, President Trump appointed Elinore F. McCance-Katz, MD, PhD, to a new post officially called “assistant secretary for mental health and substance use” and unofficially called the “mental health czar.” This was a groundbreaking step, because Dr. McCance-Katz, a psychiatrist, is known for developing innovative approaches to addressing the opioid crisis in her home state of Rhode Island. She resigned from her post on Jan. 7, 2021, citing her concerns about the Jan. 6 insurrection on the U.S. Capitol.

As of this writing, President Biden has nominated psychologist Miriam Delphin-Rittmon, PhD, who is commissioner of Connecticut Department of Mental Health and Addiction Services, as mental health czar. I’m glad to see that the new administration wants a new czar, but I would prefer to see a more expansive role for a mental health professional at the federal level. The reason is because the COVID-19 fallout is requiring us to offer a greater array of mental health and substance use disorder services than ever before.
 

Processing the current crisis

Americans managed to recover emotionally from the ravages of death and dying from World War II; we lived through the “atomic age” of mutual destruction, sometimes calling it the age of anxiety. But nothing has come close to the overwhelming devastation that COVID-19 has brought to the world – and to this country.

A recent Government Accountability Office report shows 38% of U.S. adults reported symptoms of anxiety or depression from April 2020 through February 2021. That was up from 11% from January to June 2019, the report said, citing data from the Centers for Disease Control and Prevention. Meanwhile, the report cites data from the Substance Abuse and Mental Health Services Administration showing that opioid deaths were 25%-50% higher during the pandemic than a year earlier.

My sense is that people generally have opened up regarding their emotional problems in a freer manner, thus allowing us to speak about and accept mental health problems as part of our human reality – just as we accept physical disorders and search for treatment and care.

In terms of talk therapy, I still believe that the “thinking” therapies, that is, cognitive therapies that involved getting a new perspective on problems, are most effective in dealing with the myriad of emotional issues people experience as well as those that have arisen because of COVID-19, and the tremendous fear of severe illness and death that the virus can bring. Besides anxiety, depression, and fear, the psychological toll of a fractured lifestyle, coupled with social isolation, will lead many into a variety of PTSD-related conditions. Many of those conditions, including PTSD, might lift when COVID-19 is controlled, but the time frame for resolution is far from clear and will vary, depending on each person. National leadership, as well as therapists, need to be ready to work with the many mental health problems COVID-19 will leave in its wake.

Therapeutically, as we develop our cognitive approaches to the problems this pandemic has brought, whether affecting people with no past psychiatric history or those with a previous or ongoing problems, we are in a unique position ourselves to offer even more support based on our own experiences during the pandemic. Our patients have seen us wear masks and work remotely, and just as we know about their suffering, they know we have been affected as well. These shared experiences with patients can allow us to express even greater empathy and offer even greater support – which I believe enhances the cognitive process and adds more humanism to the therapeutic process.

The therapists I’ve talked with believe that sharing coping skills – even generally sharing anxieties – can be very therapeutic. They compared these exchanges to what is done in support or educational groups.

As a psychiatrist who has been treating patients using cognitive-behavioral therapy – the thinking therapy – for more than 40 years, I agree that sharing our experiences in this worldwide pandemic with those we are helping can be extremely beneficial. Using this approach would not distract from other cognitive work. CBT, after all, is a far cry from dynamic or psychoanalytic talking or listening.

Change is in the air. More and more Americans are getting vaccinated, and the CDC is constantly updating its guidance on COVID-19. That guidance should have a mental health component.

I urge the president to put mental health at the forefront by nominating an expert who could offer mental health solutions on a daily basis. This person should be on equal footing with the surgeon general. Taking this step would help destigmatize mental suffering and despair – and create greater awareness about how to address those conditions.
 

Dr. London has been a practicing psychiatrist for 4 decades and a newspaper columnist for almost as long. He has a private practice in New York and is author of “Find Freedom Fast: Short-Term Therapy That Works” (New York: Kettlehole Publishing, 2019). Dr. London has no conflicts of interest.

When the Biden administration announced who would serve on its COVID-19 task force, some asked why a mental health expert had not been included. I have a broader question: In light of the magnitude of the pandemic’s fallout, why doesn’t the administration create a mental health post parallel to the surgeon general?

I have been making the case for creation of a high-level mental health post for quite some time. In fact, in the late 1970s, toward the end of then-President Jimmy Carter’s term, I wrote and talked about the need for a special cabinet post of mental health. At the time I realized that, besides chronic mental disorders, the amount of mental distress people experienced from a myriad of life issues leading to anxiety, depression, even posttraumatic stress disorder (although not labeled as such then), needed focused and informed leadership.

Before the pandemic, the World Health Organization reported that depression was the leading cause of disability worldwide. In the prepandemic United States, mental and substance use disorders were the top cause of disability among younger people.

We’ve lost almost 600,000 people to COVID-19, and people have been unable to grieve properly. More than 2 million women have left the labor force to care for children and sick family members. As we continue to learn about the mental health–related devastation wrought by SARS-CoV-2 – particularly long-haul COVID-19 – it’s time to dust off my proposal, update it, and implement it.
 

Building on a good decision

Back in 2017, President Trump appointed Elinore F. McCance-Katz, MD, PhD, to a new post officially called “assistant secretary for mental health and substance use” and unofficially called the “mental health czar.” This was a groundbreaking step, because Dr. McCance-Katz, a psychiatrist, is known for developing innovative approaches to addressing the opioid crisis in her home state of Rhode Island. She resigned from her post on Jan. 7, 2021, citing her concerns about the Jan. 6 insurrection on the U.S. Capitol.

As of this writing, President Biden has nominated psychologist Miriam Delphin-Rittmon, PhD, who is commissioner of Connecticut Department of Mental Health and Addiction Services, as mental health czar. I’m glad to see that the new administration wants a new czar, but I would prefer to see a more expansive role for a mental health professional at the federal level. The reason is because the COVID-19 fallout is requiring us to offer a greater array of mental health and substance use disorder services than ever before.
 

Processing the current crisis

Americans managed to recover emotionally from the ravages of death and dying from World War II; we lived through the “atomic age” of mutual destruction, sometimes calling it the age of anxiety. But nothing has come close to the overwhelming devastation that COVID-19 has brought to the world – and to this country.

A recent Government Accountability Office report shows 38% of U.S. adults reported symptoms of anxiety or depression from April 2020 through February 2021. That was up from 11% from January to June 2019, the report said, citing data from the Centers for Disease Control and Prevention. Meanwhile, the report cites data from the Substance Abuse and Mental Health Services Administration showing that opioid deaths were 25%-50% higher during the pandemic than a year earlier.

My sense is that people generally have opened up regarding their emotional problems in a freer manner, thus allowing us to speak about and accept mental health problems as part of our human reality – just as we accept physical disorders and search for treatment and care.

In terms of talk therapy, I still believe that the “thinking” therapies, that is, cognitive therapies that involved getting a new perspective on problems, are most effective in dealing with the myriad of emotional issues people experience as well as those that have arisen because of COVID-19, and the tremendous fear of severe illness and death that the virus can bring. Besides anxiety, depression, and fear, the psychological toll of a fractured lifestyle, coupled with social isolation, will lead many into a variety of PTSD-related conditions. Many of those conditions, including PTSD, might lift when COVID-19 is controlled, but the time frame for resolution is far from clear and will vary, depending on each person. National leadership, as well as therapists, need to be ready to work with the many mental health problems COVID-19 will leave in its wake.

Therapeutically, as we develop our cognitive approaches to the problems this pandemic has brought, whether affecting people with no past psychiatric history or those with a previous or ongoing problems, we are in a unique position ourselves to offer even more support based on our own experiences during the pandemic. Our patients have seen us wear masks and work remotely, and just as we know about their suffering, they know we have been affected as well. These shared experiences with patients can allow us to express even greater empathy and offer even greater support – which I believe enhances the cognitive process and adds more humanism to the therapeutic process.

The therapists I’ve talked with believe that sharing coping skills – even generally sharing anxieties – can be very therapeutic. They compared these exchanges to what is done in support or educational groups.

As a psychiatrist who has been treating patients using cognitive-behavioral therapy – the thinking therapy – for more than 40 years, I agree that sharing our experiences in this worldwide pandemic with those we are helping can be extremely beneficial. Using this approach would not distract from other cognitive work. CBT, after all, is a far cry from dynamic or psychoanalytic talking or listening.

Change is in the air. More and more Americans are getting vaccinated, and the CDC is constantly updating its guidance on COVID-19. That guidance should have a mental health component.

I urge the president to put mental health at the forefront by nominating an expert who could offer mental health solutions on a daily basis. This person should be on equal footing with the surgeon general. Taking this step would help destigmatize mental suffering and despair – and create greater awareness about how to address those conditions.
 

Dr. London has been a practicing psychiatrist for 4 decades and a newspaper columnist for almost as long. He has a private practice in New York and is author of “Find Freedom Fast: Short-Term Therapy That Works” (New York: Kettlehole Publishing, 2019). Dr. London has no conflicts of interest.

When the Biden administration announced who would serve on its COVID-19 task force, some asked why a mental health expert had not been included. I have a broader question: In light of the magnitude of the pandemic’s fallout, why doesn’t the administration create a mental health post parallel to the surgeon general?

I have been making the case for creation of a high-level mental health post for quite some time. In fact, in the late 1970s, toward the end of then-President Jimmy Carter’s term, I wrote and talked about the need for a special cabinet post of mental health. At the time I realized that, besides chronic mental disorders, the amount of mental distress people experienced from a myriad of life issues leading to anxiety, depression, even posttraumatic stress disorder (although not labeled as such then), needed focused and informed leadership.

Before the pandemic, the World Health Organization reported that depression was the leading cause of disability worldwide. In the prepandemic United States, mental and substance use disorders were the top cause of disability among younger people.

We’ve lost almost 600,000 people to COVID-19, and people have been unable to grieve properly. More than 2 million women have left the labor force to care for children and sick family members. As we continue to learn about the mental health–related devastation wrought by SARS-CoV-2 – particularly long-haul COVID-19 – it’s time to dust off my proposal, update it, and implement it.
 

Building on a good decision

Back in 2017, President Trump appointed Elinore F. McCance-Katz, MD, PhD, to a new post officially called “assistant secretary for mental health and substance use” and unofficially called the “mental health czar.” This was a groundbreaking step, because Dr. McCance-Katz, a psychiatrist, is known for developing innovative approaches to addressing the opioid crisis in her home state of Rhode Island. She resigned from her post on Jan. 7, 2021, citing her concerns about the Jan. 6 insurrection on the U.S. Capitol.

As of this writing, President Biden has nominated psychologist Miriam Delphin-Rittmon, PhD, who is commissioner of Connecticut Department of Mental Health and Addiction Services, as mental health czar. I’m glad to see that the new administration wants a new czar, but I would prefer to see a more expansive role for a mental health professional at the federal level. The reason is because the COVID-19 fallout is requiring us to offer a greater array of mental health and substance use disorder services than ever before.
 

Processing the current crisis

Americans managed to recover emotionally from the ravages of death and dying from World War II; we lived through the “atomic age” of mutual destruction, sometimes calling it the age of anxiety. But nothing has come close to the overwhelming devastation that COVID-19 has brought to the world – and to this country.

A recent Government Accountability Office report shows 38% of U.S. adults reported symptoms of anxiety or depression from April 2020 through February 2021. That was up from 11% from January to June 2019, the report said, citing data from the Centers for Disease Control and Prevention. Meanwhile, the report cites data from the Substance Abuse and Mental Health Services Administration showing that opioid deaths were 25%-50% higher during the pandemic than a year earlier.

My sense is that people generally have opened up regarding their emotional problems in a freer manner, thus allowing us to speak about and accept mental health problems as part of our human reality – just as we accept physical disorders and search for treatment and care.

In terms of talk therapy, I still believe that the “thinking” therapies, that is, cognitive therapies that involved getting a new perspective on problems, are most effective in dealing with the myriad of emotional issues people experience as well as those that have arisen because of COVID-19, and the tremendous fear of severe illness and death that the virus can bring. Besides anxiety, depression, and fear, the psychological toll of a fractured lifestyle, coupled with social isolation, will lead many into a variety of PTSD-related conditions. Many of those conditions, including PTSD, might lift when COVID-19 is controlled, but the time frame for resolution is far from clear and will vary, depending on each person. National leadership, as well as therapists, need to be ready to work with the many mental health problems COVID-19 will leave in its wake.

Therapeutically, as we develop our cognitive approaches to the problems this pandemic has brought, whether affecting people with no past psychiatric history or those with a previous or ongoing problems, we are in a unique position ourselves to offer even more support based on our own experiences during the pandemic. Our patients have seen us wear masks and work remotely, and just as we know about their suffering, they know we have been affected as well. These shared experiences with patients can allow us to express even greater empathy and offer even greater support – which I believe enhances the cognitive process and adds more humanism to the therapeutic process.

The therapists I’ve talked with believe that sharing coping skills – even generally sharing anxieties – can be very therapeutic. They compared these exchanges to what is done in support or educational groups.

As a psychiatrist who has been treating patients using cognitive-behavioral therapy – the thinking therapy – for more than 40 years, I agree that sharing our experiences in this worldwide pandemic with those we are helping can be extremely beneficial. Using this approach would not distract from other cognitive work. CBT, after all, is a far cry from dynamic or psychoanalytic talking or listening.

Change is in the air. More and more Americans are getting vaccinated, and the CDC is constantly updating its guidance on COVID-19. That guidance should have a mental health component.

I urge the president to put mental health at the forefront by nominating an expert who could offer mental health solutions on a daily basis. This person should be on equal footing with the surgeon general. Taking this step would help destigmatize mental suffering and despair – and create greater awareness about how to address those conditions.
 

Dr. London has been a practicing psychiatrist for 4 decades and a newspaper columnist for almost as long. He has a private practice in New York and is author of “Find Freedom Fast: Short-Term Therapy That Works” (New York: Kettlehole Publishing, 2019). Dr. London has no conflicts of interest.

In refractory gastroparesis, gastric peroral endoscopic myotomy (G-POEM) led to improvements in some patients, but the benefits were modest overall, according to results from a multicenter prospective study.

The clinical success rate was 56% at 12 months, defined as a 1 unit or greater decrease in the Gastroparesis Cardinal Symptom Index (GCSI) score accompanied by a 25% or greater decrease in two subscales detailing specific symptoms. Though the results fell short of expectations, they represent progress. In a previous large, multicenter, prospective study of existing therapies, just 28% experienced an improvement of 1 or more in the GCSI after 48 weeks of standard of care treatment.

This recent study, led by Kia Vosoughi and senior author Mouen Khashab, MD, of Johns Hopkins Medicine, Baltimore, was published online March 19, 2021, in Gut.

Pylorospasm has been linked to the symptoms of gastroparesis, prompting pyloric-directed interventions such as botulinum toxin injection, transpyloric stent placement, and pneumatic dilation of the pylorus. However, none have proven to have long-term benefit. G-POEM was introduced in 2013 as a minimally invasive pyloric-directed procedure. Some small, retrospective studies showed encouraging results, but this was the first prospective study.

In refractory gastroparesis, gastric peroral endoscopic myotomy (G-POEM) led to improvements in some patients, but the benefits were modest overall, according to results from a multicenter prospective study.

The clinical success rate was 56% at 12 months, defined as a 1 unit or greater decrease in the Gastroparesis Cardinal Symptom Index (GCSI) score accompanied by a 25% or greater decrease in two subscales detailing specific symptoms. Though the results fell short of expectations, they represent progress. In a previous large, multicenter, prospective study of existing therapies, just 28% experienced an improvement of 1 or more in the GCSI after 48 weeks of standard of care treatment.

This recent study, led by Kia Vosoughi and senior author Mouen Khashab, MD, of Johns Hopkins Medicine, Baltimore, was published online March 19, 2021, in Gut.

Pylorospasm has been linked to the symptoms of gastroparesis, prompting pyloric-directed interventions such as botulinum toxin injection, transpyloric stent placement, and pneumatic dilation of the pylorus. However, none have proven to have long-term benefit. G-POEM was introduced in 2013 as a minimally invasive pyloric-directed procedure. Some small, retrospective studies showed encouraging results, but this was the first prospective study.

In refractory gastroparesis, gastric peroral endoscopic myotomy (G-POEM) led to improvements in some patients, but the benefits were modest overall, according to results from a multicenter prospective study.

The clinical success rate was 56% at 12 months, defined as a 1 unit or greater decrease in the Gastroparesis Cardinal Symptom Index (GCSI) score accompanied by a 25% or greater decrease in two subscales detailing specific symptoms. Though the results fell short of expectations, they represent progress. In a previous large, multicenter, prospective study of existing therapies, just 28% experienced an improvement of 1 or more in the GCSI after 48 weeks of standard of care treatment.

This recent study, led by Kia Vosoughi and senior author Mouen Khashab, MD, of Johns Hopkins Medicine, Baltimore, was published online March 19, 2021, in Gut.

Pylorospasm has been linked to the symptoms of gastroparesis, prompting pyloric-directed interventions such as botulinum toxin injection, transpyloric stent placement, and pneumatic dilation of the pylorus. However, none have proven to have long-term benefit. G-POEM was introduced in 2013 as a minimally invasive pyloric-directed procedure. Some small, retrospective studies showed encouraging results, but this was the first prospective study.

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.