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CC BY 4.0 · Appl Clin Inform 2025; 16(04): 1031-1040
DOI: 10.1055/a-2595-0415
Special Topic on Reducing Technology-Related Stress and Burnout

Qualitative Verification of Machine Learning-Based Burnout Predictors in Primary Care Physicians: An Exploratory Study

Daniel Tawfik
1   Department of Pediatrics, Stanford University School of Medicine, Palo Alto, California, United States
,
Stefanie S. Sebok-Syer
2   Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, California, United States
,
Cassandra Bragdon
3   Department of Medicine, Stanford University School of Medicine, Palo Alto, California, United States
,
Cati Brown-Johnson
3   Department of Medicine, Stanford University School of Medicine, Palo Alto, California, United States
,
Marcy Winget
3   Department of Medicine, Stanford University School of Medicine, Palo Alto, California, United States
,
Mohsen Bayati
4   Operations, Information & Technology, Stanford Graduate School of Business, Palo Alto, California, United States
,
Tait Shanafelt
3   Department of Medicine, Stanford University School of Medicine, Palo Alto, California, United States
,
Jochen Profit
1   Department of Pediatrics, Stanford University School of Medicine, Palo Alto, California, United States
› Institutsangaben
Funding This research was supported by research grants from the Agency for Healthcare Research and Quality (K08 HS027837, PI: D.T.), the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01 HD084679, PI: J.P.), and the American Medical Association's Practice Transformation Initiative (PI: T.S.).
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Abstract

Background

Electronic health record (EHR) usage measures may quantify physician activity at scale and predict practice settings with a high risk for physician burnout, but their relation to experiences is poorly understood.

Objectives

This study aimed to explore the EHR-related experiences and well-being of primary care physicians in comparison to EHR usage measures identified as important for predicting burnout from a machine learning model.

Methods

Exploratory qualitative study with semi-structured interviews of primary care physicians and clinic managers from a large academic health system and its community physician partners. We included primary care clinics with high burnout scores, low burnout scores, or large changes in burnout scores between 2020 and 2022, relative to all primary care clinics in the health system. We conducted inductive and deductive coding of interview responses using a priori themes related to the machine learning model categories of patient load, documentation burden, messaging burden, orders, and physician distress and fulfillment.

Results

Interviews with 16 physicians and 4 clinic managers identified burdens related to three dominant themes: (1) messaging and documentation burdens are high and require more time than most physicians have available during standard working hours. (2) While EHR-related burdens are high they also provide patient-care benefits. (3) Turnover and insufficient staffing exacerbate time demands associated with patient load. Dimensions that are difficult to quantify, such as a perceived imbalance between job demands and individual resources, also contribute to burnout and were consistent across all themes.

Conclusion

EHR-related work burden, largely quantifiable through EHR usage measures, are major source of distress among primary care physicians. Organizational recognition of this work as well as staffing and support to predict associated work burden may increase professional fulfillment and reduce burnout among primary care physicians.

Keywords

physician burnout - machine learning - experiences - electronic health record usage measures

Protection of Human and Animal Subjects

This study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects and was approved by the Institutional Review Board of Stanford University (protocol #49374).


Supplementary Material



Publikationsverlauf

Eingereicht: 11. Dezember 2024

Angenommen: 21. April 2025

Accepted Manuscript online:
28. April 2025

Artikel online veröffentlicht:
05. September 2025

© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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