Electronic Health Record Usage Patterns Across Surgical Subspecialties

Kevin Tang; Kevin Labagnara; Mustufa Babar; Justin Loloi; Kara L. Watts; Sunit Jariwala; Nitya Abraham

doi:10.1055/a-2194-1061

Applied Clinical Informatics, Table of Contents

Appl Clin Inform 2024; 15(01): 034-044
DOI: 10.1055/a-2194-1061

Research Article

Electronic Health Record Usage Patterns Across Surgical Subspecialties

Kevin Tang‡^*

¹Albert Einstein College of Medicine, Bronx, New York, United States

,

Kevin Labagnara‡^*

¹Albert Einstein College of Medicine, Bronx, New York, United States

,

Mustufa Babar

¹Albert Einstein College of Medicine, Bronx, New York, United States

,

Justin Loloi

²Department of Urology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, United States

,

Kara L. Watts

²Department of Urology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, United States

,

Sunit Jariwala

³Department of Medicine, Division of Allergy/Immunology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, United States

,

Nitya Abraham

²Department of Urology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, United States

› Author Affiliations

Abstract

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.

Keywords

electronic health record - surgeon - burnout - documentation

Full Text

References

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