从关于手术护理的投诉中学习:一个大语言模型辅助的序列方法分析分析
Cameron Wells1, Allan Han2, Nejo Joseph3
1Department of Surgery, The University of Auckland, Auckland, New Zealand cameron.wells@auckland.ac.nz.
BMJ quality & safety
|January 28, 2026
概括
患者投诉显示,手术安全问题往往源于术后护理,而不是技术错误. 改善监测,沟通和升级途径对于患者安全和质量改善至关重要.
科学领域:
- 医疗保健的质量和安全.
- 手术患者的治疗结果.
- 医疗信息学 医疗信息学
背景情况:
- 患者投诉提供了超越传统指标的外科安全洞察力.
- 之前对手术投诉的分析范围有限,并且是专业特定的.
- 需要使用大型语言模型 (LLM) 的可扩展方法来分析手术投诉.
研究的目的:
- 描述主题,外科手术期间的过程,系统因素和外科手术投诉的结果.
- 使用大型语言模型 (LLM) 辅助方法进行可扩展的手术投诉分析.
- 根据投诉数据,确定改善手术患者安全的关键领域.
主要方法:
- 对来自新西兰的650份手术投诉报告进行了回顾性,横截面的研究.
- 采用固定参数的LLM工作流来提取有关人口统计,临床背景,并发症和系统因素的数据.
- 利用LLM支持的主题综合来识别投诉主题,并进行人类验证 (科恩 κ>0.85).
主要成果:
- 手术后并发症很常见 (84.2%),通常导致永久性残疾 (33.2%) 或死亡 (24.2%).
- 认知恶化 (76.1%),升级 (58.3%) 和管理 (49.5%) 的延迟是常见的,并且与死亡率的增加有关.
- 关键主题包括术后管理,沟通/知情同意和专业行为,技术错误较少发生.
结论:
- 外科护理投诉主要强调术后监测,升级和通信方面的问题,而不是手术内技术错误.
- 系统改进应侧重于早期识别恶化,可靠的移交/升级,以及加强同意流程.
- 在LLM辅助分析提供了一个可扩展的方法来分析投诉,以推动组织学习和改善外科手术的质量.
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