预测从电子健康记录使用措施中预测初级保健医生的倦怠情况
Daniel Tawfik1, Mohsen Bayati2, Jessica Liu1
1Stanford University School of Medicine, Stanford, CA.
Mayo Clinic proceedings
|April 4, 2024
概括
电子健康记录 (EHR) 使用措施显示,预测个体医生倦怠的能力有限. 然而,这些措施可以适度地确定患有高倦怠风险的诊所,有助于针对性干预.
科学领域:
- 医疗信息学 医疗信息学
- 医疗信息学 医疗信息学
- 医生福利 医生福利
背景情况:
- 医生倦怠是影响医疗保健质量的重要问题.
- 电子健康记录 (EHR) 产生了大量的使用数据.
- 预测倦怠风险对于及时干预至关重要.
研究的目的:
- 评估常规EHR使用指标是否可以预测医生倦怠.
- 为了确定临床工作单位在高风险的燃烧.
- 为了评估针对性干预策略的EHR数据.
主要方法:
- 对初级保健医生的观察性研究.
- 收集的临床工作量和EHR效率指标.
- 将EHR数据与2年的幸福调查 (斯坦福职业成就指数) 联系起来.
- 利用梯度提升分类器和其他预测模型.
- 使用接收器操作特征曲线 (AUC) 下面的面积来评估模型性能.
主要成果:
- 233名医生完成了396项调查;28%的医生报告高燃烧.
- 梯度增强模型实现了0.59的AUC,用于预测倦怠.
- 预测功能包括医生年龄和笔记/订单定制.
- 临床层面的测量显示,识别高风险诊所的敏感度为56%,特异性为85%.
结论:
- 常规收集的EHR使用措施对个人医生倦怠的预测能力有限.
- 这些措施提供了适度的能力,以确定诊所的高燃烧风险.
- 进一步的研究可能会完善用于预测和预防倦怠的电子健康记录数据的利用.
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