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基层医疗医生中基于机器学习的职业倦怠预测指标的定性验证:一项探索性研究

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

作者信息

Tawfik Daniel, Sebok-Syer Stefanie S, Bragdon Cassandra, Brown-Johnson Cati, Winget Marcy, Bayati Mohsen, Shanafelt Tait, Profit Jochen

机构信息

Department of Pediatrics, Stanford University School of Medicine, Palo Alto, California, United States.

Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, California, United States.

出版信息

Appl Clin Inform. 2025 Aug;16(4):1031-1040. doi: 10.1055/a-2595-0415. Epub 2025 Apr 28.

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.

摘要

电子健康记录(EHR)使用指标可以大规模量化医生的活动,并预测医生职业倦怠风险较高的执业环境,但人们对其与医生体验之间的关系了解甚少。本研究旨在探讨初级保健医生与电子健康记录相关的体验和幸福感,并与从机器学习模型中确定的对预测职业倦怠很重要的电子健康记录使用指标进行比较。

对来自一个大型学术健康系统及其社区医生合作伙伴的初级保健医生和诊所经理进行半结构化访谈的探索性定性研究。我们纳入了相对于该健康系统中所有初级保健诊所而言,倦怠得分高、倦怠得分低或在2020年至2022年期间倦怠得分有大幅变化的初级保健诊所。我们使用与患者负荷、文档负担、信息负担、医嘱以及医生困扰与成就感等机器学习模型类别相关的先验主题,对访谈回复进行归纳和演绎编码。

对16名医生和4名诊所经理的访谈确定了与三个主要主题相关的负担:(1)信息和文档负担很重,所需时间超过大多数医生在标准工作时间内所能提供的时间。(2)虽然与电子健康记录相关的负担很重,但它们也带来了患者护理方面的益处。(3)人员流动和人员不足加剧了与患者负荷相关的时间需求。难以量化的维度,如工作需求与个人资源之间的感知失衡,也会导致职业倦怠,并且在所有主题中都是一致的。

通过电子健康记录使用指标在很大程度上可以量化的与电子健康记录相关的工作负担,是初级保健医生困扰的主要来源。组织对这项工作的认可以及预测相关工作负担的人员配备和支持,可能会增加初级保健医生的职业成就感,并减少他们的职业倦怠。

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