使用生成AI/NLP在关键事件报告系统 (CIRS) 中识别和分析关键事件的潜力:一个可行性案例与控制研究
Carlos Ramon Hölzing1, Sebastian Rumpf1, Stephan Huber2
1Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital Würzburg, Oberdürrbacher Str. 6, 97080 Würzburg, Germany.
Healthcare (Basel, Switzerland)
|October 16, 2024
概括
生成型人工智能和自然语言处理 (AI/NLP) 显示出分析关键事件报告系统 (CIRS) 案例的潜力,其性能与人类审查员相比. 这项技术可以通过增强事件报告分析来帮助改善医疗保健患者安全.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 患者安全研究 患者安全研究
背景情况:
- 在关键事件报告系统 (CIRS) 中报告不足阻碍了医疗保健患者的安全.
- 评估分析CIRS数据的新方法对于改进报告系统至关重要.
研究的目的:
- 评估生成AI/NLP在审查CIRS案件中的有效性.
- 在分析CIRS数据时,将AI/NLP性能与人类审查员进行比较.
- 确定AI/NLP在将CIRS案件分类为相关主题方面的能力.
主要方法:
- 一个案例控制可行性研究利用了德国CIRS-麻醉学案例.
- 案件由人类专家和AI/NLP模型 (ChatGPT-3.5) 进行了审查.
- 两位独立的CIRS专家使用利克特等级盲目评估了评论质量.
主要成果:
- 人类专家错误地将45.8%的AI评论归类为由人类生成的.
- 人工智能和人类审查在语言质量,专业知识,逻辑导出性和整体质量方面表现相似 (p > 0.39).
- 人工智能模型成功地将案例自主分类为相关主题.
结论:
- 生成性AI/NLP在分析和分类CIRS案例方面具有显著的潜力.
- 在CIRS分析中AI/NLP的应用可以增强医疗保健事件报告.
- 需要进一步的研究来验证和扩大这些发现.
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