评估紧急部门的人工智能决策支持系统:回顾性研究
Yvette Van Der Haas1,2, Wiesje Roskamp2, Lidwina Elisabeth Maria Chang-Willems2
1Eindhoven University of Technology, Eindhoven, The Netherlands.
JMIR AI
|January 26, 2026
概括
一个人工智能 (AI) 模型显著减少了急诊室 (ED) 入院决策时间,平均为111分钟. 这种人工智能工具在缓解ED过度拥挤和改善患者护理方面表现有前途.
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
- 医疗保健中的人工智能
背景情况:
- 紧急诊所 (ED) 过度拥挤是一个关键问题,与医疗错误的增加,患者长期逗留和较高的死亡率有关.
- 人工智能 (AI) 决策支持工具为优化ED患者流动和决策提供了潜在的解决方案.
- 现有的研究往往忽视了人工智能在医疗保健环境中的临床相关性和实际实施.
研究的目的:
- 评估AI模型在预测ED患者入院的临床实用性.
- 评估人工智能的潜力,以减少做入学决策所需的时间.
- 调查人工智能对缓解ED过度拥挤和改善患者护理的影响.
主要方法:
- 从圣安东尼乌斯医院 (2018年1月至2023年9月) 进行了154,347次匿名患者访问的回顾性分析.
- 开发和测试一个极端梯度增强人工智能模型来预测医院入院决策.
- 使用分成10分钟间隔的数据对AI模型进行评估,以模拟现实世界的适用性并测量决策时间的减少.
主要成果:
- 人工智能模型实现了0.78的精度和0.73.7的回忆.
- 在AI正确预测入院的患者中,观察到平均节省时间为111分钟 (IQR 59-169).
- 亚组分析表明,老年患者和肺病学等专业的患者节省了更多的时间,在某些情况下,每名患者节省了高达90分钟的时间.
结论:
- 开发的AI模型显示了减少ED入学决策时间的巨大潜力,从而减轻过度拥挤.
- 人工智能工具提供一致的,加权的入学建议,即使在高ED压力时期.
- 建议进行进一步的前性研究,以验证现实世界的影响,并在各种临床环境中优化AI模型.
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