信息显示器用于医院患者病情恶化的自动监测算法:范围审查
Yik-Ki Jacob Wan1, Melanie C Wright2, Mary M McFarland3
1Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84108, United States.
Journal of the American Medical Informatics Association : JAMIA
|October 17, 2023
概括
患者病情恶化监测算法正在得到改进,但大多数显示器仍然使用基于得分的基本系统. 采用先进人工智能的关键挑战包括用户信任,透明度和工作流集成.
科学领域:
- 医疗信息学 医疗信息学
- 临床决策支持 临床决策支持
- 医疗保健中的人工智能
背景情况:
- 自动监控算法越来越多地用于预测患者的脱补偿和住院恶化.
- 这些系统旨在通过早期识别有风险的个体来改善患者的治疗结果.
研究的目的:
- 对患者病情恶化监测信息显示器的设计进行审查.
- 为了识别这些显示器中使用的算法的类型.
- 评估这些显示器对临床过程和患者结果的影响.
主要方法:
- 在Arksey和O'Malley的框架下进行了范围审查.
- 在五个数据库中对2009年1月1日至2022年1月26日之间发表的文章进行了搜索.
- 包括标准侧重于住院医生的临床参与者,利用AI算法显示恶化信息,以及临床,工作流程或可用性结果.
主要成果:
- 从最初的14575篇文章中,包括了64篇描述61个独特展览的文章.
- 41个显示器针对特定的恶化,24个提供简单的警报,48个使用基于分数的算法.
- 护士是47个显示的主要目标用户,只有一个随机对照试验报告了显著的初级结局效应.
结论:
- 尽管人工智能取得了进展,但大多数监控显示依赖于已建立的基于分数的算法.
- 采用新算法的障碍包括用户信任,算法透明度和无工作流集成.
- 有效的决策支持工具需要解决这些采用障碍.
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