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

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.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.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.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.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.

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Publication Details

Journal
Applied Clinical Informatics
Published
2025-04-28
DOI
https://doi.org/10.1055/a-2595-0415
Citations
2
Primary Topic
Healthcare professionals’ stress and burnout
Type
article
Field-Weighted Citation Impact
5.63

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article

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

Marcy Winget, Cati Brown‐Johnson, Tait D. Shanafelt, Stefanie S. Sebok‐Syer et al.
2 citations
Applied Clinical Informatics
Healthcare professionals’ stress and burnout
5.63
article

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

Marcy Winget, Cati Brown‐Johnson, Tait D. Shanafelt, Stefanie S. Sebok‐Syer, Daniel Tawfik, Jochen Profit, Mohsen Bayati, Cassandra Bragdon
article en
2 citations

Abstract

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.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.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.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.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.

Applied Clinical InformaticsVol. 16(04)
Palo Alto University (US), Stanford Medicine (US), Stanford University (US)
American Medical Association, Agency for Healthcare Research and Quality, National Institute of Child Health and Human Development, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Openalex Percentile: Top 5%
Healthcare professionals’ stress and burnout
5.63
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