特别话题 燃烧:分析医生在篮子负担和效率使用K-意味着集群
Vincent Lattanze1, Xinyue Lan2, Drew Vander Leest2
1Department of Medicine, Albert Einstein College of Medicine, Bronx, United States.
Applied clinical informatics
|March 19, 2025
概括
集群分析确定了不同的医生电子健康记录 (EHR) 使用模式,揭示了经验丰富的初级保健医生更高的文档负担. 这种数据驱动的方法有助于针对性干预,以减少医生工作量.
科学领域:
- 医疗信息学 医疗信息学
- 医疗系统分析 医学系统分析
- 医生工作流程优化工作流程优化
背景情况:
- 电子健康记录 (EHR) 系统至关重要,但增加了医生的文档负担,影响了工作流程和福祉.
- 目前用于识别高负担医生组的现有方法是有限的.
- 本研究使用集群分析来定义针对性干预的不同电子健康记录使用配置文件.
研究的目的:
- 确定集群分析是否可以有效地识别不同的医生EHR使用配置文件.
- 检查这些个人资料与医生人口统计和实践特征的相关性.
- 假设基于工作负载和篮子管理,与经验,性别和专业相关的电子健康记录使用集群将出现.
主要方法:
- 分析了使用Epic Signal的323名医生的门诊EHR使用数据.
- 将k-means集群应用到EHR工作负载和篮子效率的六个指标上.
- 根据医生的性别和专业进行的子组分析.
主要成果:
- 确定了两个不同的集群:高负担组 (经验丰富的初级保健医生) 和低负担组 (年轻专家).
- 高负担的医生花费了显著更多的时间在下班后的文档和篮子管理.
- 亚组分析显示出大量基于性别和专业的差异,特别是在负担较低的集群中.
结论:
- 集群分析成功地确定了不同的EHR使用模式和工作负载差异.
- 这些发现突出了医生经验,性别和专业的差异.
- 这种可扩展,数据驱动的方法使卫生系统能够识别有风险的医生,并设计有针对性的干预措施,以减少EHR文档负担.
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