基于紧急严重性指数原则的ChatGPT使用紧急部门分类:一个横截面研究
Cansu Colakca1, Mehmet Ergın1,2, Habibe Selmin Ozensoy3
1Department of Emergency Medicine, Ankara Bilkent City Hospital, Ankara, Turkey.
Scientific reports
|September 27, 2024
概括
聊天GPT在评估急诊室患者紧急情况方面表现出适度的一致性,显示出人工智能在分拣中的前景. 人工智能模型实现了76.6%的准确性,特别擅长识别高急性病例.
科学领域:
- 紧急医疗 紧急医疗
- 人工智能的人工智能
- 临床分类是临床分类.
背景情况:
- 紧急诊所 (ED) 面临的挑战是错误和延迟的患者分拣.
- 像ChatGPT这样的人工智能 (AI) 模型为医疗保健中的自然语言处理任务提供了潜在的解决方案.
研究的目的:
- 通过使用紧急严重性指数 (ESI) 标准,评估ChatGPT在患者分组中的准确性.
- 将人工智能驱动的选评估与ED环境中的专家医生评估进行比较.
主要方法:
- 一项横截面研究包括了在24小时内提交ED的成年患者.
- 患者数据 (人口统计,主要投诉,生命体征) 被收集并标准化.
- 一个专家委员会 (EC) 和ChatGPT同时根据ESI标准评估了患者的紧急情况.
- 欧盟委员会中位数的决定作为比较的黄金标准.
主要成果:
- 在EC和ChatGPT之间发现了一个统计学上显著的中度协议 (科恩的卡帕=0.659,P<0.001).
- 聊天GPT选的整体准确率为76.6%.
- 观察到对预测高精度ESI水平 (1和2) 的高一致性 (科恩卡帕=0.828).
- 在高敏度类别中,ChatGPT显示了高诊断特异性 (95.63%),负预测值 (98.17%) 和准确性 (94.90%).
结论:
- 聊天GPT可以有效地区分需要紧急护理的患者在ED.
- 由人工智能驱动的工具显示出将其整合到ED分拣过程中的希望.
- 需要进一步的研究来探索AI在优化紧急护理方面的全部潜力.
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