Assessing Physicians’ Recall Bias of Work Hours With a Mobile App: Interview and App-Recorded Data Comparison

Abstract

Background Previous studies have shown inconsistencies in the accuracy of self-reported work hours. However, accurate documentation of work hours is fundamental for the formation of labor policies. Strict work-hour policies decrease medical errors, improve patient safety, and promote physicians’ well-being. Objective The aim of this study was to estimate physicians’ recall bias of work hours with a mobile app, and to examine the association between the recall bias and physicians’ work hours. Methods We quantified recall bias by calculating the differences between the app-recorded and self-reported work hours of the previous week and the penultimate week. We recruited 18 physicians to install the “Staff Hours” app, which automatically recorded GPS-defined work hours for 2 months, contributing 1068 person-days. We examined the association between work hours and two recall bias indicators: (1) the difference between self-reported and app-recorded work hours and (2) the percentage of days for which work hours were not precisely recalled during interviews. Results App-recorded work hours highly correlated with self-reported counterparts (r=0.86-0.88, P<.001). Self-reported work hours were consistently significantly lower than app-recorded hours by –8.97 (SD 8.60) hours and –6.48 (SD 8.29) hours for the previous week and the penultimate week, respectively (both P<.001). The difference for the previous week was significantly correlated with work hours in the previous week (r=–0.410, P=.01), whereas the correlation of the difference with the hours in the penultimate week was not significant (r=–0.119, P=.48). The percentage of hours not recalled (38.6%) was significantly higher for the penultimate week (38.6%) than for the first week (16.0%), and the former was significantly correlated with work hours of the penultimate week (r=0.489, P=.002) Conclusions Our study identified the existence of recall bias of work hours, the extent to which the recall was biased, and the influence of work hours on recall bias.

A narrative review of the impact of work hours and insufficient rest on job performance

“…95 In addition, studies have demonstrated effects of recall bias associated with distorted time perception resulting from long work hours and inconsistencies in the accuracy of self-reported work hours, with self-reported work hours for the week prior consistently 9 hours (SD = 8.6) lower than those that were automatically app-recorded (p < .001). 96 Improvements in duty hour compliance were seen with programmatic intervention in major domains including (1) improving the accuracy and transparency of work hour reporting, (2) facilitating more timely interventions, and (3) structural schedule changes that included transition to a night float model, providing additional time off for more junior residents, and redesigning the model of clinical coverage. 97 These types of issues should be considered in the structuring of duty hour oversight programs.…”

“…Reported deterrents to accurate duty hour reporting by residents has included fear of programmatic punitive measures, poor peer perception or judgment, desire to retain control of their surgical education, and frustration with the administrative burden following violations 95 . In addition, studies have demonstrated effects of recall bias associated with distorted time perception resulting from long work hours and inconsistencies in the accuracy of self‐reported work hours, with self‐reported work hours for the week prior consistently 9 hours (SD = 8.6) lower than those that were automatically app‐recorded ( p < .001) 96 . Improvements in duty hour compliance were seen with programmatic intervention in major domains including (1) improving the accuracy and transparency of work hour reporting, (2) facilitating more timely interventions, and (3) structural schedule changes that included transition to a night float model, providing additional time off for more junior residents, and redesigning the model of clinical coverage 97 .…”

Development of Digital Biomarkers of Mental Illness via Mobile Apps for Personalized Treatment and Diagnosis

“…A study involving college students, for instance, reported that their average total time of weekly smartphone use was 20.11 ± 12.40 h based on self-reports but nearly 29.39 ± 14.45 h as recorded by mobile apps [ 14 ]. In another study, it was found that, when asked to recall the average weekly work hours within the past month, physicians reported working for an average of 60.24 h per week, despite their mobile apps recording 66.94 h per week [ 15 ]. Figure 1 illustrates that there was, on average, a 11.5–12.0% reduction in work hours reported by the subjects themselves.…”

Measuring Clinical Preparedness After Residency Training: Development of a New Instrument

“…Several survey studies of physicians have revealed that self-reporting or recall bias affects findings, where inflation or deflation of their perceived performance exists. 11,12 What is needed are more objective measures of assessment to assess preparedness more accurately for independent practice.…”