使用大型语言模型对患者安全报告进行人工智能驱动的分析:一个探索性的多种方法研究.
Kevin Chen1,2, Kiley Rogers3, William Haberkorn3
1Division of Pediatric Hospital Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA kevinychen90@gmail.com.
BMJ quality & safety
|January 30, 2026
概括
大型语言模型 (LLM) 可以准确分析患者安全报告,识别趋势并加快审查. 这种人工智能方法有助于揭示隐藏的风险,并改善医疗保健中的质量干预措施.
科学领域:
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
- 患者安全信息学
背景情况:
- 医疗机构难以分析大量的患者安全事件报告.
- 手动审查叙事数据是耗时和资源密集的,使大多数低危害事件未经检查.
- 大型语言模型 (LLM) 为此数据分析挑战提供了一种新的技术解决方案.
研究的目的:
- 开发和评估一种基于人工智能的方法,使用LLMs进行患者安全报告分析.
- 确定患者安全问题,并揭示美国医疗保健系统内的系统级趋势.
- 评估在医疗保健环境中实施LLM技术的准备情况.
主要方法:
- 对OpenAI的GPT-4o模型进行定量评估,以分析9357份患者安全报告的准确性.
- LLM提取安全问题,生成分类学,并标记报告;患者安全专家验证了准确性.
- 通过10次利益相关者采访,对仪表板的可接受性,适当性和采用性的定性评估.
主要成果:
- 该LLM获得了高的同意分数:问题识别94%,家长类别91.5%,儿童类别83.3%.
- 以前隐藏的患者安全问题模式被LLM识别出来.
- 利益相关者发现LLM产生的见解是有价值的,适当的,并认为采用LLM的障碍很少,预计会加快审查和提高质量.
结论:
- 在患者安全报告中,LLM可以有效地捕捉和分析复杂的概念.
- 通过总结和分类问题,LLMs揭示了未知的趋势和系统级风险.
- 劳动力质量管理系统可以增强手动审查,加快安全事件分析,并指导质量改进干预.
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