Hossein Soleimani1, Julia Adler-Milstein2,3, Russell J Cucina1,3, Sara G Murray1. 1. Health Informatics, University of California, San Francisco, San Francisco, California. 2. Center for Clinical Informatics and Improvement Research, University of California, San Francisco, San Francisco, California. 3. Department of Medicine, University of California, San Francisco, San Francisco, California.
Abstract
BACKGROUND: Medical training programs across the country are bound to a set of work hour regulations, generally monitored via self-report. OBJECTIVE: We developed a computational method to automate measurement of intern and resident work hours, which we validated against self-report. DESIGN, SETTING, AND PARTICIPANTS: We included all electronic health record (EHR) access log data between July 1, 2018, and June 30, 2019, for trainees enrolled in the internal medicine training program. We inferred the duration of continuous in-hospital work hours by linking EHR sessions that occurred within 5 hours as "on-campus" work and further accounted for "out-of-hospital" work which might be taking place at home. MAIN OUTCOMES AND MEASURES: We compared daily work hours estimated through the computational method with self-report and calculated the mean absolute error between the two groups. We used the computational method to estimate average weekly work hours across the rotation and the percentage of rotations where average work hours exceed the 80-hour workweek. RESULTS: The mean absolute error between self-reported and EHR-derived daily work hours for first- (PGY-1), second- (PGY-2), and third- (PGY-3) year trainees were 1.27, 1.51, and 1.51 hours, respectively. Using this computational method, we estimated average (SD) weekly work hours of 57.0 (21.7), 69.9 (12.2), and 64.1 (16.3) for PGY-1, PGY-2, and PGY-3 residents. CONCLUSION: EHR log data can be used to accurately approximate self-report of work hours, accounting for both in-hospital and out-of-hospital work. Automation will reduce trainees' clerical work, improve consistency and comparability of data, and provide more complete and timely data that training programs need.
BACKGROUND: Medical training programs across the country are bound to a set of work hour regulations, generally monitored via self-report. OBJECTIVE: We developed a computational method to automate measurement of intern and resident work hours, which we validated against self-report. DESIGN, SETTING, AND PARTICIPANTS: We included all electronic health record (EHR) access log data between July 1, 2018, and June 30, 2019, for trainees enrolled in the internal medicine training program. We inferred the duration of continuous in-hospital work hours by linking EHR sessions that occurred within 5 hours as "on-campus" work and further accounted for "out-of-hospital" work which might be taking place at home. MAIN OUTCOMES AND MEASURES: We compared daily work hours estimated through the computational method with self-report and calculated the mean absolute error between the two groups. We used the computational method to estimate average weekly work hours across the rotation and the percentage of rotations where average work hours exceed the 80-hour workweek. RESULTS: The mean absolute error between self-reported and EHR-derived daily work hours for first- (PGY-1), second- (PGY-2), and third- (PGY-3) year trainees were 1.27, 1.51, and 1.51 hours, respectively. Using this computational method, we estimated average (SD) weekly work hours of 57.0 (21.7), 69.9 (12.2), and 64.1 (16.3) for PGY-1, PGY-2, and PGY-3 residents. CONCLUSION: EHR log data can be used to accurately approximate self-report of work hours, accounting for both in-hospital and out-of-hospital work. Automation will reduce trainees' clerical work, improve consistency and comparability of data, and provide more complete and timely data that training programs need.
Authors: Joseph A Lin; Logan Pierce; Sara G Murray; Hossein Soleimani; Elizabeth C Wick; Julie Ann Sosa; Kenzo Hirose Journal: J Surg Educ Date: 2021-09-08 Impact factor: 3.524