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环境人工智能抄写员对实习医生文档负担的影响。

The Effect of Ambient Artificial Intelligence Scribes on Trainee Documentation Burden.

作者信息

Wright Donald S, Kanaparthy Naga, Melnick Edward R, Levy Deborah R, Huot Stephen J, Hsiao Allen, Schwamm Lee, Ong Shawn Y

机构信息

Emergency Medicine, Yale School of Medicine, New Haven, United States.

Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, United States.

出版信息

Appl Clin Inform. 2025 Jul 2. doi: 10.1055/a-2647-1142.


DOI:10.1055/a-2647-1142
PMID:40602775
Abstract

BACKGROUND: Ambient artificial intelligence scribes have become widespread commercial products in the era of generative artificial intelligence. While studies have examined the effect of these tools on the experience of attending physicians, little evidence is available regarding their use by resident physician trainees. OBJECTIVES: To assess trainee experience with an ambient artificial intelligence scribe using measures of usability, acceptability, and documentation burden. METHODS: This prospective observational study enrolled 47 trainees in a 2-month pilot. Pre/post surveys were conducted with the NASA Task Load Index (NASA-TLX, raw unweighted form, pre/post, for cognitive load during the documentation), the System Usability Scale (post, general usability), the Net Promoter Score (post, acceptability), and the AMIA TrendBurden Survey (pre/post, documentation burden). EHR utilization metrics were obtained from Epic Signal for both the pilot period and a 6-month baseline. RESULTS: In total, 43/47 (91.5%) of participants adopted the intervention in practice. NASA-TLX scores improved from 56.3 to 43.3 (p<.001) and multiple items on the TrendBurden survey improved with high measures of acceptability. No significant difference in time spent in notes activity per note written was observed, with a median increase of 0.4 minutes (p=.568). CONCLUSION: Trainee use of an ambient artificial intelligence scribe was associated with improvements in documentation burden. Additional research on the effect of this technology on trainee learning and expertise development is needed.

摘要

背景:在生成式人工智能时代,环境人工智能抄写员已成为广泛使用的商业产品。虽然已有研究考察了这些工具对主治医生工作体验的影响,但关于住院医师培训学员使用它们的证据却很少。 目的:使用可用性、可接受性和记录负担等指标评估住院医师培训学员使用环境人工智能抄写员的体验。 方法:这项前瞻性观察性研究招募了47名学员参与为期2个月的试点。使用美国国家航空航天局任务负荷指数(NASA-TLX,原始未加权形式,用于记录过程中的认知负荷,试点前后各测一次)、系统可用性量表(试点后,评估总体可用性)、净推荐值(试点后,评估可接受性)以及美国医学信息学会趋势负担调查(试点前后各测一次,评估记录负担)进行前后调查。从Epic Signal获取了试点期间和6个月基线期的电子健康记录使用指标。 结果:总共有43/47(91.5%)的参与者在实践中采用了该干预措施。NASA-TLX评分从56.3提高到了43.3(p<.001),趋势负担调查中的多个项目也因高可接受性评分而有所改善。每书写一份记录所花费的时间没有显著差异,中位数增加了0.秒(p = .568)。 结论:住院医师培训学员使用环境人工智能抄写员与记录负担的改善有关。需要进一步研究这项技术对学员学习和专业技能发展的影响。

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[2]
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引用本文的文献

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AI Scribes in Health Care: Balancing Transformative Potential With Responsible Integration.

JMIR Med Inform. 2025-8-1

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