患者代表人群对GPT产生的看法与标准急诊室出院说明:随机盲人调查评估
Thomas Huang1,2, Conrad Safranek1,2, Vimig Socrates2,3
1Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, United States.
Journal of medical Internet research
|August 2, 2024
概括
像ChatGPT这样的大型语言模型 (LLM) 可以生成对患者友好的出院说明,显示出改善急诊室 (ED) 通信和减少医生文档负担的潜力.
科学领域:
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
- 患者沟通 患者沟通
背景情况:
- 出院指示对于患者从急诊室 (ED) 过渡至关重要,但往往耗时且不个人化.
- 生成型人工智能 (AI) 和大型语言模型 (LLM) 提供了一个创建高质量,个性化的释放指令的机会.
- 在了解患者对LLM产生的出院说明的看法方面存在差距.
研究的目的:
- 评估LLM,如ChatGPT在为ED创建准确和患者可访问的出院说明方面的有效性.
- 与标准指令相比,评估患者对LLM生成的指令的看法.
主要方法:
- 五个虚构的ED遭遇被用来生成基于LLM (GPT-4) 和标准放电指令.
- 指示被格式化为访问后摘要,并分发给亚马逊MTurk的受访者.
- 根据可解释性,可理解性和满意度来评估患者的观点.
主要成果:
- 调查受访者更喜欢LLM生成的放电说明,特别是返回预防措施 (P=.01).
- 通过LLM生成的指令被发现与其他所有部分的标准指令相比并没有劣势.
- 患者在几个关键领域对LLM生成的指令的理解性和满意度评价更为有利,包括程序,药物和随访.
结论:
- LLM显示出增加ED文档工作流程和减少医生负担的潜力.
- 通过提供更易于阅读和适用的出院说明,LLM可以增强患者的沟通.
- 这项技术可以显著改善医疗机构当前的患者沟通方法.
关键词:
聊天GPT 聊天GPT 聊天人工智能的人工智能是人工智能.释放指令的使用说明.紧急情况部门的急救部门.紧急医疗 紧急医疗大型语言模型.机器学习是机器学习.自然语言处理自然语言处理.调查和问卷调查和问卷调查.更多相关视频
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