在住院成年人中,基于人工智能的查阿片类药物使用障碍风险的临床实施
Majid Afshar1, Felice Resnik2, Cara Joyce3
1Department of Medicine, University of Wisconsin-Madison, Madison, WI, USA. majid.afshar@wisc.edu.
Nature medicine
|April 3, 2025
概括
在EHR中,人工智能驱动的阿片类药物使用障碍 (OUD) 查器成功地识别了需要成护理的患者,而不减少咨询. 这种人工智能工具还减少了30天的住院再诊,为OUD管理提供了可扩展的解决方案.
科学领域:
- 数字健康数字健康
- 医学中的人工智能
- 公共卫生 公共卫生
背景情况:
- 患有阿片类药物使用障碍 (OUD) 的成年人面临重大风险和医院再入院.
- 目前的OUD查不是标准的,导致错过干预.
- 电子健康记录 (EHR) 和人工智能为系统的OUD识别提供了潜力.
研究的目的:
- 为了评估嵌入在EHR中的AI驱动的OUD选器.
- 为了确定AI选器是否与通常的护理在识别患者的成医学咨询中不逊色.
- 评估AI查器对医院再接收和成本效益的影响.
主要方法:
- 一个前后准实验性研究设计.
- 使用卷积神经网络实现AI选器,以实时分析EHR笔记.
- 人工智能实施前后的患者咨询和30天再入院的比较.
主要成果:
- 咨询率保持相似 (1.35%对1.51%),符合非劣等标准.
- 人工智能查与30天再入院的显著减少有关 (OR:0.53,P=0.02).
- 干预措施证明了成本效益,避免了每次再接收6,801美元.
结论:
- 人工智能驱动的EHR查是识别OUD患者的可行和可扩展的替代方案.
- 这种方法可以通过减少医院再入院来改善患者的治疗结果.
- 人工智能工具可以提高成医学服务的效率和有效性.
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