紧急医生对基于大型语言模型的文档的态度的转变:实施前和后的研究研究
Seongwon Lee1, Ji Woo Song2, Seng Chan You3,4
1Yonsei Institute for Digital Health, Yonsei University, Seoul, Korea.
Scientific reports
|November 25, 2025
概括
大型语言模型 (LLM) 显著减少医生的工作负担和担忧,当用于急诊部出院笔记. 医生很容易接受并从这些AI助理在临床环境中受益.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 医生倦怠是医疗保健中的一个重大问题.
- 有效的文档对于患者护理和运营效率至关重要.
- 人工智能 (AI) 整合到临床工作流程中既带来了机遇,也带来了挑战.
研究的目的:
- 评估大型语言模型 (LLM) 对急诊室医生工作量的影响.
- 评估医生对医疗文档中人工智能采用的态度和担忧.
- 提供真实世界的证据,证明AI助理在临床环境中的接受和好处.
主要方法:
- 一项涉及八名经验丰富的急诊医生的研究.
- 在使用LLM之前,在使用LLM期间 (3天) 和在使用LLM后 (5周) 进行的调查.
- 对感知的工作量和AI相关问题的定量和定性评估.
主要成果:
- 医生对使用LLM的担忧显著下降,并随着时间的推移保持在低水平.
- 使用LLM可将编写放电笔记所需的时间减少约三分之一.
- 医生们表现出快速接受和从LLM协助中感知到的好处.
结论:
- 法学士助理很容易被医生接受,并有效地减少了急诊室的工作量.
- 人工智能文档工具显示出提高效率和解决医生倦怠问题的前景.
- 这项研究为AI在医疗保健文档中的长期整合提供了有价值的见解.
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