电子健康记录活动在新的决策支持实施方面发生了变化:使用审计日志和主题建模进行监测
Jinying Chen1,2,3,4, Sarah L Cutrona4,5, Ajay Dharod3,6,7,8
1Department of Medicine/Section of Preventive Medicine and Epidemiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, United States.
JAMIA open
|October 22, 2025
概括
本研究引入了一种机器学习方法,用于监测计算机化临床决策支持 (CDS) 工具对电子健康记录 (EHR) 活动的影响. 识别的方法改变了EHR的使用,有助于CDS工具的评估和改进.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 临床决策支持系统 临床决策支持系统
背景情况:
- 计算机化临床决策支持 (CDS) 工具被整合到电子健康记录 (EHR) 中,以帮助临床医生.
- 监测这些工具对临床医生的电子健康记录活动的实际影响对于有效实施和改进至关重要.
- 现有的方法可能无法充分捕捉CDS实施后临床医生的行为细微变化.
研究的目的:
- 开发和验证一种新的机器学习方法,用于监测CDS工具对临床医生的电子健康记录活动的影响.
- 评估烟草戒断CDS工具实施后电子健康记录使用模式的变化.
主要方法:
- 利用主题建模,一种机器学习技术,分析EHR审计日志并推断临床医生的活动.
- 在5个癌症诊所的3445次接触数据上训练了模型,比较了实施前后的时间.
- 领域专家标记了临床相关性的确定EHR活动主题.
主要成果:
- 主题模型确定了14个不同的EHR活动,包括以CDS为重点的,与CDS相关的,数据审查和EHR修改任务.
- 烟草戒断CDS实施后,CDS专注和CDS相关活动显著增加.
- 相反,EHR修改和患者数据审查活动在实施后显示下降.
结论:
- 开发的基于主题模型的方法有效地识别了在CDS实施后EHR活动的转变.
- 这种可扩展,数据驱动的框架为评估和改进嵌入EHR的CDS干预提供了有价值的见解.
- 该方法可适应评估使用CDS的其他医疗保健质量改善策略.
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