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.
Date:
2021-12-24
Relation:
Journal of Medical Internet Research. 2021 Dec 24;23(12):Article number e26763.
