使用K-Means集群来通过电子健康记录负担和效率来识别医生集群
Jasper Sim1, Kyle Mani1, Melissa Fazzari2
1Department of Medicine, Albert Einstein College of Medicine, Bronx, New York, USA.
概括
电子健康记录 (EHR) 给医生带来了繁重的文档负担. 对EHR数据的集群分析确定了不同的医生效率组,有助于针对医生健康的有针对性的干预.
科学领域:
- 医疗信息学 医疗信息学
- 医疗文件 医疗文件
- 医生工作流分析分析
背景情况:
- 电子健康记录 (EHR) 是现代医学不可或缺的组成部分,但对医生的文档负担做出了重大贡献.
- 评估和减轻这种负担对于医生福祉和医疗保健系统效率至关重要.
研究的目的:
- 在蒙特菲奥雷医疗系统内分析Epic EHR信号工具中的医生元数据.
- 利用集群分析来量化医生之间的电子健康记录负担和效率.
主要方法:
- 分析了2020年7月的数据,反映了远程医疗后的操作期.
- 使用了六个信号指标:非计划日的时间,睡衣时间,计划外的时间,周转时间,熟练度得分和当天的访问结束.
- 基于这些指标,对小组医生进行了k-Means聚类.
主要成果:
- 医生被分为不同的组:一个具有更高的效率和更少的超时 (TOSH),另一个具有更低的效率和更高的TOSH.
- 这些集群甚至在按医生的性别和专业分层时也存在.
- 集群内部分析显示了医生指标的同质性,在按性别分层时,在高效集群中发现了一些变化.
结论:
- 确定的集群有效地代表了蒙特菲奥雷医疗系统的EHR效率负担格局.
- 电子病历数据的k-Means集群为评估医生电子病历负担提供了一种可扩展和适应的方法.
- 这种方法使卫生系统能够识别文档趋势,并为医生实施有针对性的健康干预措施.
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