Effect on Patient Safety of a Resident Physician Schedule without 24-Hour Shifts

Landrigan, Christopher P.; Rahman, Shadab A.; Sullivan, Jason P.; Vittinghoff, Eric; Barger, Laura K.; Sanderson, Amy; Wright, Kenneth P.; O’Brien, Conor S; Qadri, Salim; Hilaire, Melissa A St; Halbower, Ann C.; Segar, Jeffrey L.; McGuire, John K.; Vitiello, Michael V.; Iglesia, Horacio O. de la; Poynter, Sue E.; Yu, Pearl L.; Zee, Phyllis C.; Lockley, Steven W.; Stone, Katie L.; Czeisler, Charles A.

doi:10.1056/nejmoa1900669

Cited by 62 publications

(52 citation statements)

References 34 publications

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“…We note that adding sleep may not be sufficient for improving patient care, due to residents' overall fatigue and burnout 23,87 . Such changes in training schedules, while improving sleep, might cause deterioration in education opportunities and interruption in continuity of care 56,57 and result in more medical errors 58 …”

Section: Discussionmentioning

confidence: 98%

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Acute and chronic sleep deprivation in residents: Cognition and stress biomarkers

et al. 2020

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Objectives Insufficient sleep affects circadian hormonal profiles and inflammatory markers and may modulate attention, executive functioning and decision‐making. Medical professionals and specifically resident physicians, who are involved in long‐term nightshift schedules during their post‐graduate training, are prone to acute and chronic sleep deprivation and disruption, putting them at risk for making medical errors. The aim of the study was to evaluate the impact of chronic and acute‐on‐chronic sleep deprivation and disruption among residents on selected physiological and cognitive measures. Methods Thirty‐three medical and surgical residents were evaluated twice ‐ at baseline and after a 26‐hour shift. Eighteen young attending physicians who did not engage in nightshift schedules served as controls and were evaluated once. Measures included morning cortisol and high‐sensitivity C‐reactive protein (hs‐CRP), computerised tests of attention and behaviour, the Behaviour Rating Inventory of Executive Function, a risk‐taking questionnaire and the Pittsburgh Sleep Quality Index. Results Residents, but not attendings, reported chronic sleep disruption and deprivation. Residents at baseline exhibited reduced morning cortisol levels and elevated hs‐CRP levels, compared to attendings. Residents at baseline had impaired global executive function compared to attendings. A nightshift with acute sleep deprivation further reduced residents' executive function. Residents at baseline and after a nightshift demonstrated increased impulsivity and slower processing time than attendings. Residents and attendings did not differ in risk‐taking tendencies which were assessed in a separate cohort. Conclusions In a real‐life setting, resident physicians exhibit increased low‐grade systemic inflammation (hs‐CRP) and impaired HPA‐axis function. Their chronic sleep curtailment is associated with greater impulsivity, slower cognitive processing, and impaired executive function. Future research is warranted to understand how improving working schedule by increasing sleep duration may minimise the short‐term and potential long‐term risks to physicians in training.

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Section: Discussionmentioning

confidence: 98%

“…Indeed, some studies have shown that extended 24‐hour shifts increase the rate of medical errors 54 . At the same time, other studies found that extending physicians’ sleep duration did not improve patient‐centred outcomes 55‐58 …”

Section: Discussionmentioning

confidence: 99%

Acute and chronic sleep deprivation in residents: Cognition and stress biomarkers

et al. 2020

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“…In a real-life study, one serious medical error per six to eight patient-days was observed in pediatric intensive care with residents involved in approximately half of the errors [ 14 ]. A diagnostic-scenario based prospective study in physicians reported a high rate of incorrect diagnoses (43–56%) [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

“…In addition, not all errors can be detected retrospectively. Indeed, available data from prospective studies suggest that the incidence of diagnostic errors and their consequences like inappropriate or even dangerous management may be significantly higher than estimated from observational studies [ 6 , 12 , 13 , 14 , 15 ].…”

Section: Introduction/backgroundmentioning

confidence: 99%

Diagnostic Errors Induced by a Wrong a Priori Diagnosis: A Prospective Randomized Simulator-Based Trial

Meyer

Filipovic

Balestra

et al. 2021

JCM

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Preventive strategies against diagnostic errors require the knowledge of underlying mechanisms. We examined the effects of a wrong a priori diagnosis on diagnostic accuracy of a focussed assessment in an acute myocardial infarction scenario. One-hundred-and-fifty-six medical students (cohort 1) were randomized to three study arms differing in the a priori diagnosis revealed: no diagnosis (control group), myocardial infarction (correct diagnosis group), and pulmonary embolism (wrong diagnosis group). Forty-four physicians (cohort 2) were randomized to the control group and the wrong diagnosis group. Primary endpoint was the participants’ final presumptive diagnosis. Among students, the correct diagnosis of an acute myocardial infarction was made by 48/52 (92%) in the control group, 49/52 (94%) in the correct diagnosis group, and 14/52 (27%) in the wrong diagnosis group (p < 0.001 vs. both other groups). Among physicians, the correct diagnosis was made by 20/21 (95%) in the control group and 15/23 (65%) in the wrong diagnosis group (p = 0.023). In the wrong diagnosis group, 31/52 (60%) students and 6/23 (19%) physicians indicated their initially given wrong a priori diagnosis pulmonary embolism as final diagnosis. A wrong a priori diagnosis significantly increases the likelihood of a diagnostic error during a subsequent patient encounter.

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“…These reliable, quantitative data could facilitate real-time policy evaluation and target resources to those with the greatest requirement, even in remote and inaccessible regions. In addition, policy regarding resident physician work hours has shifted frequently in recent years [ 11 , 12 ]; assessing user retention of an app like “Staff Hours” is useful to track the implementation of a work hours policy, as well as the resident physicians’ compliance with work hour limits.…”

Section: Introductionmentioning

confidence: 99%

Assessing User Retention of a Mobile App: Survival Analysis

Lin¹,

Chen²,

Lin³

et al. 2020

JMIR Mhealth Uhealth

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Background A mobile app generates passive data, such as GPS data traces, without any direct involvement from the user. These passive data have transformed the manner of traditional assessments that require active participation from the user. Passive data collection is one of the most important core techniques for mobile health development because it may promote user retention, which is a unique characteristic of a software medical device. Objective The primary aim of this study was to quantify user retention for the “Staff Hours” app using survival analysis. The secondary aim was to compare user retention between passive data and active data, as well as factors associated with the survival rates of user retention. Methods We developed an app called “Staff Hours” to automatically calculate users’ work hours through GPS data (passive data). “Staff Hours” not only continuously collects these passive data but also sends an 11-item mental health survey to users monthly (active data). We applied survival analysis to compare user retention in the collection of passive and active data among 342 office workers from the “Staff Hours” database. We also compared user retention on Android and iOS platforms and examined the moderators of user retention. Results A total of 342 volunteers (224 men; mean age 33.8 years, SD 7.0 years) were included in this study. Passive data had higher user retention than active data (P=.011). In addition, user retention for passive data collected via Android devices was higher than that for iOS devices (P=.015). Trainee physicians had higher user retention for the collection of active data than trainees from other occupations, whereas no significant differences between these two groups were observed for the collection of passive data (P=.700). Conclusions Our findings demonstrated that passive data collected via Android devices had the best user retention for this app that records GPS-based work hours.

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Effect on Patient Safety of a Resident Physician Schedule without 24-Hour Shifts

Cited by 62 publications

References 34 publications

Acute and chronic sleep deprivation in residents: Cognition and stress biomarkers

Acute and chronic sleep deprivation in residents: Cognition and stress biomarkers

Diagnostic Errors Induced by a Wrong a Priori Diagnosis: A Prospective Randomized Simulator-Based Trial

Assessing User Retention of a Mobile App: Survival Analysis

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