CC BY 4.0 · Appl Clin Inform
DOI: 10.1055/a-2595-0415
Research Article

Special Topic Burnout: Qualitative verification of machine learning-based burnout predictors in primary care physicians: An exploratory study

Daniel Tawfik
1   Pediatrics, Stanford University, Stanford, United States (Ringgold ID: RIN6429)
,
Stefanie S. Sebok-Syer
2   Emergency Medicine, Stanford University, Stanford, United States (Ringgold ID: RIN6429)
,
Cassandra Bragdon
3   Medicine, Stanford University, Stanford, United States (Ringgold ID: RIN6429)
,
Cati Brown-Johnson
3   Medicine, Stanford University, Stanford, United States (Ringgold ID: RIN6429)
,
Marcy Winget
3   Medicine, Stanford University, Stanford, United States (Ringgold ID: RIN6429)
,
Mohsen Bayati
4   Graduate School of Business, Stanford University, Stanford, United States (Ringgold ID: RIN6429)
,
Tait Shanafelt
3   Medicine, Stanford University, Stanford, United States (Ringgold ID: RIN6429)
,
Jochen Profit
1   Pediatrics, Stanford University, Stanford, United States (Ringgold ID: RIN6429)
› Author Affiliations
Supported by: Agency for Healthcare Research and Quality K08 HS027837
Supported by: American Medical Association Practice Transformation Initiative
Supported by: Eunice Kennedy Shriver National Institute of Child Health and Human Development R01 HD084679

Background: Electronic health record (EHR) usage measures may quantify physician activity at scale and predict practice settings with high risk for physician burnout, but their relation to experiences are poorly understood. Objective: 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 3 dominant themes: 1) Messaging and Documentation Burden 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; and 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. Conclusions and Relevance: EHR-related work burden, largely quantifiable through EHR usage measures, are major sources 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.



Publication History

Received: 11 December 2024

Accepted after revision: 21 April 2025

Accepted Manuscript online:
28 April 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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