Analyzing Physician In Basket Burden and Efficiency Using K-Means Clustering

Vincent Lattanze; Xinyue Lan; Drew Vander Leest; Jasper Sim; Melissa Fazzari; Xianhong Xie; Sunit P. Jariwala

doi:10.1055/a-2562-1100

Applied Clinical Informatics, Table of Contents

Appl Clin Inform 2025; 16(03): 640-651
DOI: 10.1055/a-2562-1100

Research Article

Analyzing Physician In Basket Burden and Efficiency Using K-Means Clustering

Vincent Lattanze

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

,

Xinyue Lan

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

,

Drew Vander Leest

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

,

Jasper Sim

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

,

Melissa Fazzari

²Division of Biostatistics, Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, United States

,

Xianhong Xie

²Division of Biostatistics, Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, United States

,

Sunit P. Jariwala

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

› Author Affiliations

Abstract

Background

Electronic health record (EHR) systems are essential for modern healthcare but contribute to a significant documentation burden, affecting physician workflow and well-being. While previous studies have identified differences in EHR usage across demographics, systematic methods for identifying high-burden physician groups remain limited. This study applies cluster analysis to uncover distinct EHR usage profiles and provide a framework to inform the development of targeted interventions.

Objectives

This study investigated two research questions: (1) Can cluster analysis effectively identify distinct physician EHR usage profiles? (2) How do these profiles vary across physician demographics and practice characteristics? We hypothesized that (1) EHR usage clusters would emerge based on workload intensity, after-hours documentation, and In Basket management patterns, and (2) would be significantly associated with physician experience, sex, and specialty.

Methods

We analyzed outpatient EHR usage data from 323 physicians at an academic health system using Epic Signal, an analytical tool for Epic EHR. Using k-means clustering, we examined six metrics representing EHR workload (after-hours and extended-day activities) and In Basket efficiency (message handling and management patterns). We assessed cluster differences and conducted subgroup analyses by physician sex and specialty.

Results

Two distinct physician clusters emerged: one high-burden cluster, predominantly comprising experienced primary care physicians, and another lower-burden cluster, consisting mostly of younger specialists. Physicians in the high-burden cluster spent nearly three times as much time on after-hours documentation and In Basket management. While message response times remained similar, subgroup analyses revealed significant sex and specialty-based differences, particularly in the lower-burden cluster.

Conclusion

Cluster analysis effectively identified distinct EHR usage patterns, highlighting disparities in workload by experience, sex, and specialty. This approach provides a scalable, data-driven method for health systems to identify at-risk groups and design targeted interventions to mitigate documentation burden and enhance EHR efficiency.

Keywords

electronic health records and systems - documentation burden - cluster analysis - machine learning - COVID-19

Full Text

References

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Supplementary Material

Supplementary Material