Electronic Health Record Usage Patterns Across Surgical Subspecialties.

Objectives: This study aimed to utilize metrics from physician action logs to analyze surgeon clinical, volume, electronic health record (EHR) efficiency, EHR proficiency, and workload outside scheduled time as impacted by physician characteristics such as years of experience, gender, subspecialty, academic title, and administrative title.

Methods: We selected 30 metrics from Epic Signal, an analytic tool in Epic that extracts metrics related to clinician documentation. Metrics measuring appointments, messages, and scheduled hours per day were used as a correlate for volume. EHR efficiency, and proficiency were measured by scores built into Epic Signal. Metrics measuring time spent in the EHR outside working hours were used as a correlate for documentation burden. We analyzed these metrics among surgeons at our institution across 4 months and correlated them with physician characteristics.

Results: Analysis of 133 surgeons showed that, when stratified by gender, female surgeons had significantly higher EHR metrics for time per day, time per appointment, and documentation burden, and significantly lower EHR metrics for efficiency when compared to male surgeons. When stratified by experience, surgeons with 0 to 5 years of experience had significantly lower EHR metrics for volume, time per day, efficiency, and proficiency when compared to surgeons with 6 to 10 and more than 10 years of experience. On multivariate analysis, having over 10 years of experience was an independent predictor of more appointments per day, greater proficiency, and spending less time per completed message. Female gender was an independent predictor of spending more time in notes per appointment and time spent in the EHR outside working hours.

Conclusion: The burden associated with volume, proficiency, efficiency, and workload outside scheduled time related to EHR use varies by gender and years of experience in our cohort of surgeons. Evaluation of physician action logs could help identify those at higher risk of burnout due to burdensome medical documentation.