使用大型语言模型分析伤口护理护理记录.
Yeonju Kim1, Jiin Kim2, Mona Choi2
1College of Nursing and Brain Korea 21 FOUR Project, Yonsei University, Seoul, South Korea.
Studies in health technology and informatics
|August 8, 2025
概括
一个大型语言模型 (LLM) 准确地总结了癌症伤口护理记录,显示了高的事实一致性. 这项技术有望通过组织临床文档来改善癌症伤病患者的护理.
科学领域:
- 瘤学 护理 护理专业
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 不结构化的护理记录在提取关键患者信息方面带来了挑战.
- 对癌症伤口管理文件的有效总结对于优质护理至关重要.
- 大型语言模型 (LLM) 为处理临床文本提供了潜在的解决方案.
研究的目的:
- 评估大型语言模型 (LLM) 在总结癌症伤口管理的非结构化护理记录中的有效性.
- 根据既定的临床指南评估LLM生成摘要的质量和准确性.
- 识别低质量的摘要中的错误类型,以用于未来的模型改进.
主要方法:
- 从专业癌症伤口护理护士的80个非结构化护理记录的回顾性分析.
- 根据不列颠哥伦比亚省癌症局的指导方针提取21个关键变量.
- 申请一个LLM记录总结,然后进行定量和定性质量评估.
主要成果:
- 该LLM证明了护理干预变量的完全准确性和高准确性 (约. 80%) 对于护理评估变量.
- 在定量和定性评估中,事实一致性获得了最高分.
- 大约50%的低质量的摘要归因于推理错误.
结论:
- 在癌症伤口管理方面,LLM可以有效地总结非结构化的护理记录.
- 这项技术显示出支持临床决策和改善患者护理的巨大潜力.
- 对LLM的进一步改进可能会解决推理错误,以提高摘要质量.
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