Editorial

“If your time to you is worth savin’

Then you better start swimmin’ or you’ll sink like a stone

For the times they are a-changin’...”

–Bob Dylan

The Accreditation Council for Graduate Medical Education requires residency programs to limit and track trainee work hours to reduce the risk of fatigue, burnout, and medical errors. These hours are documented most often by self-report, at the cost of additional administrative burden for trainees and programs, dubious accuracy, and potentially incentivizing misrepresentation.¹

Thus, the study by Soleimani and colleagues² in this issue is a welcome addition to the literature on duty-hours tracking. Using timestamp data from the electronic health record (EHR), the authors developed and collected validity evidence for an automated computerized algorithm to measure how much time trainees were spending on clinical work. The study was conducted at a large academic internal medicine residency program and tracked 203 trainees working 14,610 days. The authors compared their results to trainee self-report data. Though the approach centered on EHR access logs, it accommodated common scenarios of time away from the computer while at the hospital (eg, during patient rounds). Crucially, the algorithm included EHR access while at home. The absolute discrepancy between the algorithm and self-report averaged 1.38 hours per day. Notably, EHR work at home accounted for about an extra hour per day. When considering in-hospital work alone, the authors found 3% to 13% of trainees exceeding 80-hour workweek limits, but when adding out-of-hospital work, this percentage rose to 10% to 21%.

The authors used inventive methods to improve accuracy. They prespecified EHR functions that constituted active clinical work, classifying reading without editing notes or placing orders simply as “educational study,” which they excluded from duty hours. They ensured that time spent off-site was included and that logins from personal devices while in-hospital were not double-counted. Caveats to the study include the limited generalizability for institutions without the computational resources to replicate the model. The authors acknowledged the inherent flaw in using trainee self-report as the “gold standard,” and potentially some subset of the results could have been corroborated with time-motion observation studies.³ The decision to exclude passive medical record review at home as work arguably discounts the integral value that the “chart biopsy” has on direct patient care; it probably led to systematic underestimation of duty hours for junior and senior residents, who may be most likely to contribute in this way. Similarly, not counting time spent with patients at the end of the day after sign-out risks undercounting hours as well. Nonetheless, this study represents a rigorously designed and scalable approach to meeting regulatory requirements that can potentially lighten the administrative task load for trainees, improve reporting accuracy, and facilitate research comparing work hours to other variables of interest (eg, efficiency). The model can be generalized to other specialties and could document workload for staff physicians as well.

Merits of the study aside, the algorithm underscores troubling realities about the practice of medicine in the 21st century. Do we now equate clinical work with time on the computer? Is our contribution as physicians defined primarily by our presence at the keyboard, rather than the bedside?⁴ Future research facilitated by automated hours tracking is likely to further elucidate a connection between time spent in the EHR with burnout⁴ and job dissatisfaction, and the premise of this study is emblematic of the erosion of clinical work-life boundaries that began even before the pandemic.⁵ While the “times they are a-changin’,” in this respect, it may not be for the better.

“If your time to you is worth savin’

Then you better start swimmin’ or you’ll sink like a stone

For the times they are a-changin’...”

–Bob Dylan

The Accreditation Council for Graduate Medical Education requires residency programs to limit and track trainee work hours to reduce the risk of fatigue, burnout, and medical errors. These hours are documented most often by self-report, at the cost of additional administrative burden for trainees and programs, dubious accuracy, and potentially incentivizing misrepresentation.¹

Thus, the study by Soleimani and colleagues² in this issue is a welcome addition to the literature on duty-hours tracking. Using timestamp data from the electronic health record (EHR), the authors developed and collected validity evidence for an automated computerized algorithm to measure how much time trainees were spending on clinical work. The study was conducted at a large academic internal medicine residency program and tracked 203 trainees working 14,610 days. The authors compared their results to trainee self-report data. Though the approach centered on EHR access logs, it accommodated common scenarios of time away from the computer while at the hospital (eg, during patient rounds). Crucially, the algorithm included EHR access while at home. The absolute discrepancy between the algorithm and self-report averaged 1.38 hours per day. Notably, EHR work at home accounted for about an extra hour per day. When considering in-hospital work alone, the authors found 3% to 13% of trainees exceeding 80-hour workweek limits, but when adding out-of-hospital work, this percentage rose to 10% to 21%.

The authors used inventive methods to improve accuracy. They prespecified EHR functions that constituted active clinical work, classifying reading without editing notes or placing orders simply as “educational study,” which they excluded from duty hours. They ensured that time spent off-site was included and that logins from personal devices while in-hospital were not double-counted. Caveats to the study include the limited generalizability for institutions without the computational resources to replicate the model. The authors acknowledged the inherent flaw in using trainee self-report as the “gold standard,” and potentially some subset of the results could have been corroborated with time-motion observation studies.³ The decision to exclude passive medical record review at home as work arguably discounts the integral value that the “chart biopsy” has on direct patient care; it probably led to systematic underestimation of duty hours for junior and senior residents, who may be most likely to contribute in this way. Similarly, not counting time spent with patients at the end of the day after sign-out risks undercounting hours as well. Nonetheless, this study represents a rigorously designed and scalable approach to meeting regulatory requirements that can potentially lighten the administrative task load for trainees, improve reporting accuracy, and facilitate research comparing work hours to other variables of interest (eg, efficiency). The model can be generalized to other specialties and could document workload for staff physicians as well.

Merits of the study aside, the algorithm underscores troubling realities about the practice of medicine in the 21st century. Do we now equate clinical work with time on the computer? Is our contribution as physicians defined primarily by our presence at the keyboard, rather than the bedside?⁴ Future research facilitated by automated hours tracking is likely to further elucidate a connection between time spent in the EHR with burnout⁴ and job dissatisfaction, and the premise of this study is emblematic of the erosion of clinical work-life boundaries that began even before the pandemic.⁵ While the “times they are a-changin’,” in this respect, it may not be for the better.

“If your time to you is worth savin’

Then you better start swimmin’ or you’ll sink like a stone

For the times they are a-changin’...”

–Bob Dylan

The Accreditation Council for Graduate Medical Education requires residency programs to limit and track trainee work hours to reduce the risk of fatigue, burnout, and medical errors. These hours are documented most often by self-report, at the cost of additional administrative burden for trainees and programs, dubious accuracy, and potentially incentivizing misrepresentation.¹

Thus, the study by Soleimani and colleagues² in this issue is a welcome addition to the literature on duty-hours tracking. Using timestamp data from the electronic health record (EHR), the authors developed and collected validity evidence for an automated computerized algorithm to measure how much time trainees were spending on clinical work. The study was conducted at a large academic internal medicine residency program and tracked 203 trainees working 14,610 days. The authors compared their results to trainee self-report data. Though the approach centered on EHR access logs, it accommodated common scenarios of time away from the computer while at the hospital (eg, during patient rounds). Crucially, the algorithm included EHR access while at home. The absolute discrepancy between the algorithm and self-report averaged 1.38 hours per day. Notably, EHR work at home accounted for about an extra hour per day. When considering in-hospital work alone, the authors found 3% to 13% of trainees exceeding 80-hour workweek limits, but when adding out-of-hospital work, this percentage rose to 10% to 21%.

The authors used inventive methods to improve accuracy. They prespecified EHR functions that constituted active clinical work, classifying reading without editing notes or placing orders simply as “educational study,” which they excluded from duty hours. They ensured that time spent off-site was included and that logins from personal devices while in-hospital were not double-counted. Caveats to the study include the limited generalizability for institutions without the computational resources to replicate the model. The authors acknowledged the inherent flaw in using trainee self-report as the “gold standard,” and potentially some subset of the results could have been corroborated with time-motion observation studies.³ The decision to exclude passive medical record review at home as work arguably discounts the integral value that the “chart biopsy” has on direct patient care; it probably led to systematic underestimation of duty hours for junior and senior residents, who may be most likely to contribute in this way. Similarly, not counting time spent with patients at the end of the day after sign-out risks undercounting hours as well. Nonetheless, this study represents a rigorously designed and scalable approach to meeting regulatory requirements that can potentially lighten the administrative task load for trainees, improve reporting accuracy, and facilitate research comparing work hours to other variables of interest (eg, efficiency). The model can be generalized to other specialties and could document workload for staff physicians as well.

Merits of the study aside, the algorithm underscores troubling realities about the practice of medicine in the 21st century. Do we now equate clinical work with time on the computer? Is our contribution as physicians defined primarily by our presence at the keyboard, rather than the bedside?⁴ Future research facilitated by automated hours tracking is likely to further elucidate a connection between time spent in the EHR with burnout⁴ and job dissatisfaction, and the premise of this study is emblematic of the erosion of clinical work-life boundaries that began even before the pandemic.⁵ While the “times they are a-changin’,” in this respect, it may not be for the better.

Although malpractice “crises” come and go, liability fears persist near top of mind for most physicians.¹ Liability insurance premiums have plateaued in recent years, but remain at high levels, and the prospect of being reported to the National Practitioner Data Bank (NPDB) or listed on a state medical board’s website for a paid liability claim is unsettling. The high-acuity setting and the absence of longitudinal patient relationships in hospital medicine may theoretically raise malpractice risk, yet hospitalists’ liability risk remains understudied.²

The contribution by Schaffer and colleagues³ in this issue of the Journal of Hospital Medicine is thus welcome and illuminating. The researchers examine the liability risk of hospitalists compared to that of other specialties by utilizing a large database of malpractice claims compiled from multiple insurers across a decade.³ In a field of research plagued by inadequate data, the Comparative Benchmarking System (CBS) built by CRICO/RMF is a treasure. Unlike the primary national database of malpractice claims, the NPDB, the CBS contains information on claims that did not result in a payment, as well as physicians’ specialty and detailed information on the allegations, injuries, and their causes. The CBS contains almost a third of all medical liability claims made in the United States during the study period, supporting generalizability.

Schaffer and colleagues¹ found that hospitalists had a lower claims rate than physicians in emergency medicine or neurosurgery. The rate was on par with that for non-hospital general internists, even though hospitalists often care for higher-acuity patients. Although claims rates dropped over the study period for physicians in neurosurgery, emergency medicine, psychiatry, and internal medicine subspecialties, the rate for hospitalists did not change significantly. Further, the median payout on claims against hospitalists was the highest of all the specialties examined, except neurosurgery. This reflects higher injury severity in hospitalist cases: half the claims against hospitalists involved death and three-quarters were high severity.

The study is not without limitations. Due to missing data, only a fraction of the claims (8.2% to 11%) in the full dataset are used in the claims rate analysis. Regression models predicting a payment are based on a small number of payments for hospitalists (n = 363). Further, the authors advance, as a potential explanation for hospitalists’ higher liability risk, that hospitalists are disproportionately young compared to other specialists, but the dataset lacks age data. These limitations suggest caution in the authors’ overall conclusion that “the malpractice environment for hospitalists is becoming less favorable.”

Nevertheless, several important insights emerge from their analysis. The very existence of claims demonstrates that patient harm continues. The contributing factors and judgment errors found in these claims demonstrate that much of this harm is potentially preventable and a risk to patient safety. Whether or not the authors’ young-hospitalist hypothesis is ultimately proven, it is difficult to argue with more mentorship as a means to improve safety. Also, preventing or intercepting judgment errors remains a vexing challenge in medicine that undoubtedly calls for creative clinical decision support solutions. Schaffer and colleagues¹ also note that hospitalists are increasingly co-managing patients with other specialties, such as orthopedic surgery. Whether this new practice model drives hospitalist liability risk because hospitalists are practicing in areas in which they have less experience (as the authors posit) or whether hospitalists are simply more likely to be named in a suit as part of a specialty team with higher liability risk remains unknown and merits further investigation.

Ultimately, regardless of whether the liability environment is worsening for hospitalists, the need to improve our liability system is clear. There is room to improve the system on a number of metrics, including properly compensating negligently harmed patients without unduly burdening providers. The system also induces defensive medicine and has not driven safety improvements as expected. The liability environment, as a result, remains challenging not just for hospitalists, but for all patients and physicians as well.

Although malpractice “crises” come and go, liability fears persist near top of mind for most physicians.¹ Liability insurance premiums have plateaued in recent years, but remain at high levels, and the prospect of being reported to the National Practitioner Data Bank (NPDB) or listed on a state medical board’s website for a paid liability claim is unsettling. The high-acuity setting and the absence of longitudinal patient relationships in hospital medicine may theoretically raise malpractice risk, yet hospitalists’ liability risk remains understudied.²

The contribution by Schaffer and colleagues³ in this issue of the Journal of Hospital Medicine is thus welcome and illuminating. The researchers examine the liability risk of hospitalists compared to that of other specialties by utilizing a large database of malpractice claims compiled from multiple insurers across a decade.³ In a field of research plagued by inadequate data, the Comparative Benchmarking System (CBS) built by CRICO/RMF is a treasure. Unlike the primary national database of malpractice claims, the NPDB, the CBS contains information on claims that did not result in a payment, as well as physicians’ specialty and detailed information on the allegations, injuries, and their causes. The CBS contains almost a third of all medical liability claims made in the United States during the study period, supporting generalizability.

Schaffer and colleagues¹ found that hospitalists had a lower claims rate than physicians in emergency medicine or neurosurgery. The rate was on par with that for non-hospital general internists, even though hospitalists often care for higher-acuity patients. Although claims rates dropped over the study period for physicians in neurosurgery, emergency medicine, psychiatry, and internal medicine subspecialties, the rate for hospitalists did not change significantly. Further, the median payout on claims against hospitalists was the highest of all the specialties examined, except neurosurgery. This reflects higher injury severity in hospitalist cases: half the claims against hospitalists involved death and three-quarters were high severity.

The study is not without limitations. Due to missing data, only a fraction of the claims (8.2% to 11%) in the full dataset are used in the claims rate analysis. Regression models predicting a payment are based on a small number of payments for hospitalists (n = 363). Further, the authors advance, as a potential explanation for hospitalists’ higher liability risk, that hospitalists are disproportionately young compared to other specialists, but the dataset lacks age data. These limitations suggest caution in the authors’ overall conclusion that “the malpractice environment for hospitalists is becoming less favorable.”

Nevertheless, several important insights emerge from their analysis. The very existence of claims demonstrates that patient harm continues. The contributing factors and judgment errors found in these claims demonstrate that much of this harm is potentially preventable and a risk to patient safety. Whether or not the authors’ young-hospitalist hypothesis is ultimately proven, it is difficult to argue with more mentorship as a means to improve safety. Also, preventing or intercepting judgment errors remains a vexing challenge in medicine that undoubtedly calls for creative clinical decision support solutions. Schaffer and colleagues¹ also note that hospitalists are increasingly co-managing patients with other specialties, such as orthopedic surgery. Whether this new practice model drives hospitalist liability risk because hospitalists are practicing in areas in which they have less experience (as the authors posit) or whether hospitalists are simply more likely to be named in a suit as part of a specialty team with higher liability risk remains unknown and merits further investigation.

Ultimately, regardless of whether the liability environment is worsening for hospitalists, the need to improve our liability system is clear. There is room to improve the system on a number of metrics, including properly compensating negligently harmed patients without unduly burdening providers. The system also induces defensive medicine and has not driven safety improvements as expected. The liability environment, as a result, remains challenging not just for hospitalists, but for all patients and physicians as well.

Although malpractice “crises” come and go, liability fears persist near top of mind for most physicians.¹ Liability insurance premiums have plateaued in recent years, but remain at high levels, and the prospect of being reported to the National Practitioner Data Bank (NPDB) or listed on a state medical board’s website for a paid liability claim is unsettling. The high-acuity setting and the absence of longitudinal patient relationships in hospital medicine may theoretically raise malpractice risk, yet hospitalists’ liability risk remains understudied.²

The contribution by Schaffer and colleagues³ in this issue of the Journal of Hospital Medicine is thus welcome and illuminating. The researchers examine the liability risk of hospitalists compared to that of other specialties by utilizing a large database of malpractice claims compiled from multiple insurers across a decade.³ In a field of research plagued by inadequate data, the Comparative Benchmarking System (CBS) built by CRICO/RMF is a treasure. Unlike the primary national database of malpractice claims, the NPDB, the CBS contains information on claims that did not result in a payment, as well as physicians’ specialty and detailed information on the allegations, injuries, and their causes. The CBS contains almost a third of all medical liability claims made in the United States during the study period, supporting generalizability.

Schaffer and colleagues¹ found that hospitalists had a lower claims rate than physicians in emergency medicine or neurosurgery. The rate was on par with that for non-hospital general internists, even though hospitalists often care for higher-acuity patients. Although claims rates dropped over the study period for physicians in neurosurgery, emergency medicine, psychiatry, and internal medicine subspecialties, the rate for hospitalists did not change significantly. Further, the median payout on claims against hospitalists was the highest of all the specialties examined, except neurosurgery. This reflects higher injury severity in hospitalist cases: half the claims against hospitalists involved death and three-quarters were high severity.

The study is not without limitations. Due to missing data, only a fraction of the claims (8.2% to 11%) in the full dataset are used in the claims rate analysis. Regression models predicting a payment are based on a small number of payments for hospitalists (n = 363). Further, the authors advance, as a potential explanation for hospitalists’ higher liability risk, that hospitalists are disproportionately young compared to other specialists, but the dataset lacks age data. These limitations suggest caution in the authors’ overall conclusion that “the malpractice environment for hospitalists is becoming less favorable.”

Nevertheless, several important insights emerge from their analysis. The very existence of claims demonstrates that patient harm continues. The contributing factors and judgment errors found in these claims demonstrate that much of this harm is potentially preventable and a risk to patient safety. Whether or not the authors’ young-hospitalist hypothesis is ultimately proven, it is difficult to argue with more mentorship as a means to improve safety. Also, preventing or intercepting judgment errors remains a vexing challenge in medicine that undoubtedly calls for creative clinical decision support solutions. Schaffer and colleagues¹ also note that hospitalists are increasingly co-managing patients with other specialties, such as orthopedic surgery. Whether this new practice model drives hospitalist liability risk because hospitalists are practicing in areas in which they have less experience (as the authors posit) or whether hospitalists are simply more likely to be named in a suit as part of a specialty team with higher liability risk remains unknown and merits further investigation.

Ultimately, regardless of whether the liability environment is worsening for hospitalists, the need to improve our liability system is clear. There is room to improve the system on a number of metrics, including properly compensating negligently harmed patients without unduly burdening providers. The system also induces defensive medicine and has not driven safety improvements as expected. The liability environment, as a result, remains challenging not just for hospitalists, but for all patients and physicians as well.

With another long winter officially in the rearview mirror and spring sunshine displaying new signs of life outdoors, I am excited to share some of the changes happening inside the offices of the Journal of Clinical Outcomes Management (JCOM). It is my pleasure to introduce Ebrahim Barkoudah, MD, MPH, as the journal’s new physician Editor in Chief. Dr. Barkoudah’s extensive experience in education and his work to improve patient outcomes will be assets to JCOM.

Specializing in both internal medicine and hospital medicine, Dr. Barkoudah is the Associate Director of the Hospital Medicine Unit and a Medical Director in the Department of Medicine at Brigham and Women’s Hospital in Boston. He is also Assistant Professor of Medicine at Harvard Medical School, where he led the school’s international education efforts.

Dr. Barkoudah serves patients with a range of complex clinical disorders, managing their care and seeking innovative treatment options. His research interest is in health care outcomes as well as clinical trials of therapeutic interventions. Dr. Barkoudah also serves on numerous clinical innovation committees at Brigham Health and national task forces.

Dr. Barkoudah is an active member of several professional societies including the American College of Physicians, Society of Hospital Medicine, American Heart Association, Massachusetts Medical Society, among others. He was the Institutional Administration Fellow of the Safety and Quality Fellowship Program at the Institution for Healthcare Improvement.

On behalf of the JCOM Editorial Review Board, I want to extend a special thank you to outgoing editor Lori Tishler, MD, MPH. Dr. Tishler’s impact on the journal cannot be overstated, and we are indebted to the time and expertise she shared with the journal during her tenure.

—Eric Seger

With another long winter officially in the rearview mirror and spring sunshine displaying new signs of life outdoors, I am excited to share some of the changes happening inside the offices of the Journal of Clinical Outcomes Management (JCOM). It is my pleasure to introduce Ebrahim Barkoudah, MD, MPH, as the journal’s new physician Editor in Chief. Dr. Barkoudah’s extensive experience in education and his work to improve patient outcomes will be assets to JCOM.

Specializing in both internal medicine and hospital medicine, Dr. Barkoudah is the Associate Director of the Hospital Medicine Unit and a Medical Director in the Department of Medicine at Brigham and Women’s Hospital in Boston. He is also Assistant Professor of Medicine at Harvard Medical School, where he led the school’s international education efforts.

Dr. Barkoudah serves patients with a range of complex clinical disorders, managing their care and seeking innovative treatment options. His research interest is in health care outcomes as well as clinical trials of therapeutic interventions. Dr. Barkoudah also serves on numerous clinical innovation committees at Brigham Health and national task forces.

Dr. Barkoudah is an active member of several professional societies including the American College of Physicians, Society of Hospital Medicine, American Heart Association, Massachusetts Medical Society, among others. He was the Institutional Administration Fellow of the Safety and Quality Fellowship Program at the Institution for Healthcare Improvement.

On behalf of the JCOM Editorial Review Board, I want to extend a special thank you to outgoing editor Lori Tishler, MD, MPH. Dr. Tishler’s impact on the journal cannot be overstated, and we are indebted to the time and expertise she shared with the journal during her tenure.

—Eric Seger

With another long winter officially in the rearview mirror and spring sunshine displaying new signs of life outdoors, I am excited to share some of the changes happening inside the offices of the Journal of Clinical Outcomes Management (JCOM). It is my pleasure to introduce Ebrahim Barkoudah, MD, MPH, as the journal’s new physician Editor in Chief. Dr. Barkoudah’s extensive experience in education and his work to improve patient outcomes will be assets to JCOM.

Specializing in both internal medicine and hospital medicine, Dr. Barkoudah is the Associate Director of the Hospital Medicine Unit and a Medical Director in the Department of Medicine at Brigham and Women’s Hospital in Boston. He is also Assistant Professor of Medicine at Harvard Medical School, where he led the school’s international education efforts.

Dr. Barkoudah serves patients with a range of complex clinical disorders, managing their care and seeking innovative treatment options. His research interest is in health care outcomes as well as clinical trials of therapeutic interventions. Dr. Barkoudah also serves on numerous clinical innovation committees at Brigham Health and national task forces.

Dr. Barkoudah is an active member of several professional societies including the American College of Physicians, Society of Hospital Medicine, American Heart Association, Massachusetts Medical Society, among others. He was the Institutional Administration Fellow of the Safety and Quality Fellowship Program at the Institution for Healthcare Improvement.

On behalf of the JCOM Editorial Review Board, I want to extend a special thank you to outgoing editor Lori Tishler, MD, MPH. Dr. Tishler’s impact on the journal cannot be overstated, and we are indebted to the time and expertise she shared with the journal during her tenure.

—Eric Seger

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

SARS-CoV-2 seroprevalence studies of healthcare workers (HCWs) provide valuable insights into the excess risk of infection in this population and indirect evidence supporting the value of personal protective equipment (PPE) use. Seroprevalence estimates are composite measures of exposure risk and transmission mitigation both in the healthcare and community environments. The challenge of interpreting these studies arises from the diversity of HCW vocational roles and work settings in juxtaposition to heterogeneous community exposure risks. In this issue, two studies untangle some of these competing factors.

Investigators from Kashmir, India, assessed the relationship between seropositivity and specific HCW roles and work sites.¹ They found a lower seroprevalence among HCWs at hospitals dedicated to COVID patients, relative to non-COVID hospitals. This seemingly paradoxical finding likely results from a combination of vigilant PPE adherence enforced through a buddy system, restrictive visitation policies, HCW residential dormitories reducing community exposure, and a spillover effect of careful in-hospital exposure avoidance practices on out-of-hospital behavior. A similar spillover effect has been hypothesized for low HCW seroprevalence relative to the surrounding community in California.²

In complement, researchers at a large New York City (NYC) hospital found higher overall HCW seropositivity rates compared with the community, though estimates were strikingly variable after detailed stratification by job function and location.³ The gradient of seroprevalence showed the highest risk among nurses and those in nonclinical, low-wage jobs (eg, patient transport, housekeeping), a finding also seen in another US study prior to adjustment for demographic and community factors.⁴ This finding highlights the association between socioeconomic status, structural community exposure risk factors such as multiple essential workers living within multigenerational households, and the challenges of sickness absenteeism. High seroprevalence among nurses and emergency department HCWs (who expeditiously evaluate many undifferentiated patients) may reflect both greater aggregate duration of exposure to infected patients and increased frequency of PPE donning and doffing, resulting in fatigue and diminished vigilance.⁵

A NYC-based study similarly showed high HCW seroprevalence, although no consistent associations with job function (albeit measured with less granularity) or community-based exposures were identified.⁶ Several studies comparing HCW to local community seropositivity rates have reached disparate conclusions.^2,7 These contrasting data may result from variability in vigilance of PPE use, mask use in work rooms or during meals/breaktimes, sick leave policies driven by staffing demands, and neighborhood factors. In addition, selection biases and timing of blood sampling relative to viral transmission peaks (with differing degrees of temporal antibody waning) may contribute to the apparent discordance. In particular, comparative community-based samples vary greatly in their inclusion of asymptomatic patients, which can substantially affect such estimates by changing the denominator population.

We draw three conclusions: (1) Evidence for HCW exposure often tracks with community infection rates, suggesting that nonworkplace exposures are a dominant source of HCW seropositivity; (2) vigilant PPE use and assertively implemented protective measures unrelated to patient encounters can dramatically reduce infection risk, even among those with frequent exposures; and (3) HCW infection risk during future peaks can be effectively restrained with adequate resources and support, even in the presence of variants for which no effective vaccination or preventive pharmacotherapy exists. Given the divergent seroprevalence rates found in these studies after detailed stratification by job function and location, it is important for future studies to evaluate their relationship with infectious risk. Accurately quantifying the excess risks borne by HCWs may remain an elusive objective, but experiential knowledge offers numerous strategies worthy of proactive implementation to preserve HCW safety and well-being.

SARS-CoV-2 seroprevalence studies of healthcare workers (HCWs) provide valuable insights into the excess risk of infection in this population and indirect evidence supporting the value of personal protective equipment (PPE) use. Seroprevalence estimates are composite measures of exposure risk and transmission mitigation both in the healthcare and community environments. The challenge of interpreting these studies arises from the diversity of HCW vocational roles and work settings in juxtaposition to heterogeneous community exposure risks. In this issue, two studies untangle some of these competing factors.

Investigators from Kashmir, India, assessed the relationship between seropositivity and specific HCW roles and work sites.¹ They found a lower seroprevalence among HCWs at hospitals dedicated to COVID patients, relative to non-COVID hospitals. This seemingly paradoxical finding likely results from a combination of vigilant PPE adherence enforced through a buddy system, restrictive visitation policies, HCW residential dormitories reducing community exposure, and a spillover effect of careful in-hospital exposure avoidance practices on out-of-hospital behavior. A similar spillover effect has been hypothesized for low HCW seroprevalence relative to the surrounding community in California.²

In complement, researchers at a large New York City (NYC) hospital found higher overall HCW seropositivity rates compared with the community, though estimates were strikingly variable after detailed stratification by job function and location.³ The gradient of seroprevalence showed the highest risk among nurses and those in nonclinical, low-wage jobs (eg, patient transport, housekeeping), a finding also seen in another US study prior to adjustment for demographic and community factors.⁴ This finding highlights the association between socioeconomic status, structural community exposure risk factors such as multiple essential workers living within multigenerational households, and the challenges of sickness absenteeism. High seroprevalence among nurses and emergency department HCWs (who expeditiously evaluate many undifferentiated patients) may reflect both greater aggregate duration of exposure to infected patients and increased frequency of PPE donning and doffing, resulting in fatigue and diminished vigilance.⁵

A NYC-based study similarly showed high HCW seroprevalence, although no consistent associations with job function (albeit measured with less granularity) or community-based exposures were identified.⁶ Several studies comparing HCW to local community seropositivity rates have reached disparate conclusions.^2,7 These contrasting data may result from variability in vigilance of PPE use, mask use in work rooms or during meals/breaktimes, sick leave policies driven by staffing demands, and neighborhood factors. In addition, selection biases and timing of blood sampling relative to viral transmission peaks (with differing degrees of temporal antibody waning) may contribute to the apparent discordance. In particular, comparative community-based samples vary greatly in their inclusion of asymptomatic patients, which can substantially affect such estimates by changing the denominator population.

We draw three conclusions: (1) Evidence for HCW exposure often tracks with community infection rates, suggesting that nonworkplace exposures are a dominant source of HCW seropositivity; (2) vigilant PPE use and assertively implemented protective measures unrelated to patient encounters can dramatically reduce infection risk, even among those with frequent exposures; and (3) HCW infection risk during future peaks can be effectively restrained with adequate resources and support, even in the presence of variants for which no effective vaccination or preventive pharmacotherapy exists. Given the divergent seroprevalence rates found in these studies after detailed stratification by job function and location, it is important for future studies to evaluate their relationship with infectious risk. Accurately quantifying the excess risks borne by HCWs may remain an elusive objective, but experiential knowledge offers numerous strategies worthy of proactive implementation to preserve HCW safety and well-being.

SARS-CoV-2 seroprevalence studies of healthcare workers (HCWs) provide valuable insights into the excess risk of infection in this population and indirect evidence supporting the value of personal protective equipment (PPE) use. Seroprevalence estimates are composite measures of exposure risk and transmission mitigation both in the healthcare and community environments. The challenge of interpreting these studies arises from the diversity of HCW vocational roles and work settings in juxtaposition to heterogeneous community exposure risks. In this issue, two studies untangle some of these competing factors.

Investigators from Kashmir, India, assessed the relationship between seropositivity and specific HCW roles and work sites.¹ They found a lower seroprevalence among HCWs at hospitals dedicated to COVID patients, relative to non-COVID hospitals. This seemingly paradoxical finding likely results from a combination of vigilant PPE adherence enforced through a buddy system, restrictive visitation policies, HCW residential dormitories reducing community exposure, and a spillover effect of careful in-hospital exposure avoidance practices on out-of-hospital behavior. A similar spillover effect has been hypothesized for low HCW seroprevalence relative to the surrounding community in California.²

In complement, researchers at a large New York City (NYC) hospital found higher overall HCW seropositivity rates compared with the community, though estimates were strikingly variable after detailed stratification by job function and location.³ The gradient of seroprevalence showed the highest risk among nurses and those in nonclinical, low-wage jobs (eg, patient transport, housekeeping), a finding also seen in another US study prior to adjustment for demographic and community factors.⁴ This finding highlights the association between socioeconomic status, structural community exposure risk factors such as multiple essential workers living within multigenerational households, and the challenges of sickness absenteeism. High seroprevalence among nurses and emergency department HCWs (who expeditiously evaluate many undifferentiated patients) may reflect both greater aggregate duration of exposure to infected patients and increased frequency of PPE donning and doffing, resulting in fatigue and diminished vigilance.⁵

A NYC-based study similarly showed high HCW seroprevalence, although no consistent associations with job function (albeit measured with less granularity) or community-based exposures were identified.⁶ Several studies comparing HCW to local community seropositivity rates have reached disparate conclusions.^2,7 These contrasting data may result from variability in vigilance of PPE use, mask use in work rooms or during meals/breaktimes, sick leave policies driven by staffing demands, and neighborhood factors. In addition, selection biases and timing of blood sampling relative to viral transmission peaks (with differing degrees of temporal antibody waning) may contribute to the apparent discordance. In particular, comparative community-based samples vary greatly in their inclusion of asymptomatic patients, which can substantially affect such estimates by changing the denominator population.

We draw three conclusions: (1) Evidence for HCW exposure often tracks with community infection rates, suggesting that nonworkplace exposures are a dominant source of HCW seropositivity; (2) vigilant PPE use and assertively implemented protective measures unrelated to patient encounters can dramatically reduce infection risk, even among those with frequent exposures; and (3) HCW infection risk during future peaks can be effectively restrained with adequate resources and support, even in the presence of variants for which no effective vaccination or preventive pharmacotherapy exists. Given the divergent seroprevalence rates found in these studies after detailed stratification by job function and location, it is important for future studies to evaluate their relationship with infectious risk. Accurately quantifying the excess risks borne by HCWs may remain an elusive objective, but experiential knowledge offers numerous strategies worthy of proactive implementation to preserve HCW safety and well-being.