电子健康记录使用与效率和生产率相关的因素:一个可解释的机器学习分析分析
Huan Li1,2,3, Varada V Khanna2,4, Nate Apathy5
1Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT, 06519, United States.
JAMIA open
|March 2, 2026
概括
医生电子健康记录 (EHR) 的使用,如快速消息响应和有限的超时工作,与更高的效率和生产力有关. 有针对性的EHR策略可以提高医生的表现和患者护理.
科学领域:
- 医疗信息学 医疗信息学
- 医疗实践管理 医疗实践管理
- 医疗保健中的机器学习
背景情况:
- 医生的效率和生产力对于医疗保健的提供至关重要.
- 电子健康记录 (EHR) 显著影响医生的工作流程.
- 了解EHR使用模式可以优化临床操作.
研究的目的:
- 调查特定的门诊医生EHR使用特征和医生效率的代理之间的相关性.
- 确定与提高医生生产力和绩效相关的关键EHR利用指标.
主要方法:
- 一项纵向队列研究分析了来自413个美国组织的医生月EHR元数据 (2019年5月-2022年4月).
- 一个多模型机器学习分类器被开发来预测医生效率.
- 医生的效率是通过同一天的图表完成率来衡量的;生产力是通过每天的患者访问量来衡量.
主要成果:
- 机器学习模型在分类高图表完成效率方面取得了0.74的准确性,与 <1.5天的收件箱周转率和 <25分钟/天的下班记录相关.
- 第二次分析 (0.84准确度) 将高访问量与<4.1分钟/访问EHR时间和<3.2分钟/访问临床审查联系起来.
- 包括218,610名医生,超过519万次观察,显示平均图表完成效率为72.9%和10.8次/天.
结论:
- 特定的EHR使用指标,包括收件箱管理和下班记录,可以作为干预的目标.
- 来自EHR数据的可操作见解可以促进平衡,高效的工作环境.
- 优化EHR使用可以改善患者护理,减少医生在EHR任务上花费的时间.
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