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Automating Measurement of Trainee Work Hours.
Hossein Soleimani1, Julia Adler-Milstein2,3, Russell J Cucina1,3
1Health Informatics, University of California, San Francisco, San Francisco, California.
Journal of Hospital Medicine
|April 30, 2021
Summary
A new computational method accurately measures medical resident work hours using electronic health records (EHR), reducing clerical burden and improving data accuracy for training programs.
Area of Science:
- Medical Education
- Health Informatics
- Workforce Management
Background:
- Medical training programs face work hour regulations.
- Current monitoring relies on self-reported data, which can be inconsistent.
Purpose of the Study:
- To develop and validate a computational method for automating the measurement of intern and resident work hours.
- To compare automated work hour measurements against traditional self-reporting.
Main Methods:
- Utilized electronic health record (EHR) access log data for internal medicine trainees.
- Inferred work hours by linking EHR sessions and accounting for out-of-hospital work.
- Validated the computational method against self-reported work hours.
Main Results:
- The computational method demonstrated a mean absolute error of 1.27-1.51 hours compared to self-reports.
- Estimated average weekly work hours varied by postgraduate year (PGY).
- The method accounted for both in-hospital and out-of-hospital work.
Conclusions:
- Electronic health record (EHR) log data accurately approximates self-reported work hours.
- Automation reduces trainee clerical work and enhances data consistency and timeliness.
- This method provides valuable data for medical training program oversight.
