Estimation of Surgical Resident Duty Hours and Workload in Real Time Using Electronic Health Record Data.

OBJECTIVE

To explore the use of electronic health record (EHR) data to estimate surgery resident duty hours and monitor real time workload.

DESIGN

Retrospective analysis of resident duty hours logged using a voluntary global positioning system (GPS)-based smartphone application compared to duty hour estimates by an EHR-based algorithm. The algorithm estimated duty hours using EHR activity data and operating room logs. A dashboard was developed through Plan-Do-Study-Act cycles for real-time monitoring of workload.

SETTING

Single tertiary/quaternary medical center general surgery residency program with approximately 90 categorical, preliminary, and integrated residents at eight clinical sites.

PARTICIPANTS

Categorical, preliminary, and integrated surgery residents of all clinical years who volunteered to pilot a GPS application to track duty hours.

RESULTS

Of 2,623 work periods by 59 residents were logged with both methods. EHR-estimated work periods started later than GPS logs (median 0.3 hours, interquartile range [IQR] -0.1 - 0.3); EHR-estimated work periods ended earlier than GPS logs (median 0.1 hours, IQR -0.7 - 0.3); and EHR-estimated duty hour totals were less than totals logged by GPS (median -0.3 hours, IQR -0.8 - +0.1). Overall correlation between weekly duty hours logged by EHR and GPS was 0.79. Correlations between the 2 systems stratified from PGY-1 through PGY-5 were 0.76, 0.64, 0.82, 0.87, and 0.83, respectively. The algorithm identified six 80-hour workweek violations (averaged over 4 weeks), while GPS logs identified 8. EHR-based duty hours and operational data were integrated into a dashboard to enable real time monitoring of resident workloads.

CONCLUSIONS

EHR-based estimation of surgical resident duty hours has good correlation with GPS-based assessment of duty hours and identifies most workweek duty hour violations. This approach allows for dynamic workload monitoring and may be combined with operational data to anticipate and prevent duty hour violations, thereby optimizing learning.