从护理记录中提取药品安全信息:使用ChatGPT进行数据分类
Susumu Wakabayashi1,2, Ryoma Seto2
1Department of Pharmacy, Kyorin University Hospital.
Studies in health technology and informatics
|August 8, 2025
概括
护理记录可以通过人工智能识别不良影响 (AE) 来提高药物安全. 然而,存在重大文档缺口,需要标准化数据收集以提高患者安全.
科学领域:
- 医疗信息学 医疗信息学
- 患者安全 患者安全
- 医疗保健中的人工智能
背景情况:
- 护理记录是患者安全的宝贵数据来源.
- 提高药品安全性需要准确检测不良影响 (AE).
- 当前的文档实践在AE识别方面存在挑战.
研究的目的:
- 探索使用ChatGPT-4进行AE分析的护理记录的有用性.
- 为了确定日本一家医院内AE报告的文档漏洞.
- 分析与AE相关的术语和患者状况之间的关系.
主要方法:
- 来自日本社区医院的927个匿名护理记录的分析.
- 使用ChatGPT-4进行AE识别和数据分类.
- 构建了一个共发生网络,以分析术语关系,并使用网络指标识别中心模式.
主要成果:
- 对于AE状态 (674例) 和患者状况 (865例) 的"未知"分类的高率表明存在重大文档缺口.
- 与药物 (6例) 和设备 (2例) 相关的副作用的有限文档.
- 同时发生网络强调了知情同意 (IC) 在沟通风险方面的重要性,特别是在化疗和抗生素方面.
结论:
- 护理记录在AE管理中发挥着至关重要的作用,尽管目前的文档限制.
- 标准化的文档和护理笔记的整合对于提高药品安全至关重要.
- 未来的研究应该专注于将人工智能与人类反相结合,以从不完整的护理笔记中提取更好的AE.
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