基于人工智能的事件分析和学习系统,以提高患者的安全性和改善治疗质量
Abbas J Jinia1, Katherine Chapman2, Shi Liu2
1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA. jiniaa@mskcc.org.
NPJ digital medicine
|February 16, 2026
概括
这项研究引入了一个基于AI的系统 (AI-ILS),可以快速准确地分析患者安全事件,提高医疗保健质量. AI-ILS系统增强了事件审查流程,并比手动方法更有效地识别安全威胁.
科学领域:
- 改善医疗保健质量 改善医疗保健质量
- 人工智能在医学中的应用
- 患者安全分析分析
背景情况:
- 分析患者安全事件至关重要,但往往效率低下和不一致.
- 目前的手工方法是劳动密集型的,阻碍了及时识别安全威胁.
- 缺乏标准化的分类学和审查流程使事件分析复杂化.
研究的目的:
- 开发和验证基于人工智能的事件分析和学习系统 (AI-ILS).
- 提高分析患者安全事件的准确性和效率.
- 为了应对医疗保健机构内追溯事件分析的挑战.
主要方法:
- 开发了AI-ILS,使用人类因素分析和分类系统 (HFACS) 训练了1548个策划事件.
- 使用AUROC,MCC和整体准确度等指标评估AI-ILS性能.
- 在350个现实临床事件中测试了AI-ILS,将其一致性和速度与专家手动分析进行比较.
- 使用放射瘤学数据部署和验证AI-ILS.
主要成果:
- 人工智能ILS实现了高精度,平均AUROC为0.92,MCC为0.72,整体精度为79%.
- 在真实世界的事件上,与专家审稿人达成88%的一致性.
- 操作速度比手动事件分析快29倍.
- 通过提供基于HFACS的聚合结果,成功地改进了追溯事件分析.
结论:
- AI-ILS为分析患者安全事件提供了高度准确和高效的解决方案.
- 该系统解决了医疗保健事件审查的关键挑战,包括不一致性和缺乏标准化.
- 在临床实践中,AI-ILS显示出改善患者安全和治疗质量的巨大潜力.
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