仪表板监控基于机器学习的临床决策支持干预措施
Daniel J Hekman1, Hanna J Barton1, Apoorva P Maru1
1Berbee-Walsh Department of Emergency Medicine, University of Wisconsin-Madison, School of Medicine and Public Health, Madison, Wisconsin, United States.
Applied clinical informatics
|November 29, 2023
概括
监测基于机器学习的临床决策支持 (ML-CDS) 需要一种超出算法准确性的更广泛的方法. 本研究引入了一个仪表板,以弥合ML-CDS输出和患者结果之间的差距,确保有效地整合到护理系统中.
科学领域:
- 医疗信息学 医疗信息学
- 临床决策支持系统 临床决策支持系统
- 医疗保健中的机器学习
背景情况:
- 目前对ML-CDS的监测主要集中在算法准确性上.
- 有效的患者护理需要监控ML-CDS输出如何转化为护理团队可行的干预措施.
- 需要扩大监测范围,超越模型的表现,覆盖整个护理系统.
研究的目的:
- 描述开发一个仪表板来管理和识别ML-CDS干预中的问题.
- 弥合ML-CDS模型输出和患者结果之间的监测差距.
- 使干预开发团队和运营利益相关者能够管理ML-CDS性能.
主要方法:
- 代开发过程创建一个监控仪表板.
- 纳入最佳实践,代设计和专家咨询.
- 使用浮动图和控制图作为核心仪表板组件.
主要成果:
- 仪表板开发过程突出了改善干预的领域.
- 仪表板本身提供了对更广泛干预的表现的见解.
- 识别需要在护理系统内采取纠正措施的潜在问题.
结论:
- 监控ML-CDS需要一个仪表板方法,提供系统层面和组件层面的视图.
- 使用此类仪表板定期监测ML-CDS对于确保护理系统的适当功能至关重要.
- 这种方法支持有效实施ML-CDS工具,以改善患者护理.
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