Ma Stephen P, Liang April S, Shah Shreya J, Smith Margaret, Jeong Yejin, Devon-Sand Anna, Crowell Trevor, Delahaie Clarissa, Hsia Caroline, Lin Steven, Shanafelt Tait, Pfeffer Michael A, Sharp Christopher, Garcia Patricia
Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, United States.
Stanford Healthcare AI Applied Research Team, Division of Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA 94305, United States.
J Am Med Inform Assoc. 2025 Feb 1;32(2):381-385. doi: 10.1093/jamia/ocae304.
To quantify utilization and impact on documentation time of a large language model-powered ambient artificial intelligence (AI) scribe.
This prospective quality improvement study was conducted at a large academic medical center with 45 physicians from 8 ambulatory disciplines over 3 months. Utilization and documentation times were derived from electronic health record (EHR) use measures.
The ambient AI scribe was utilized in 9629 of 17 428 encounters (55.25%) with significant interuser heterogeneity. Compared to baseline, median time per note reduced significantly by 0.57 minutes. Median daily documentation, afterhours, and total EHR time also decreased significantly by 6.89, 5.17, and 19.95 minutes/day, respectively.
An early pilot of an ambient AI scribe demonstrated robust utilization and reduced time spent on documentation and in the EHR. There was notable individual-level heterogeneity.
Large language model-powered ambient AI scribes may reduce documentation burden. Further studies are needed to identify which users benefit most from current technology and how future iterations can support a broader audience.
量化由大语言模型驱动的环境人工智能（AI）书记员的使用情况及其对记录时间的影响。
这项前瞻性质量改进研究在一家大型学术医疗中心进行，为期3个月，涉及来自8个门诊学科的45名医生。使用情况和记录时间来自电子健康记录（EHR）使用指标。
在17428次会诊中的9629次（55.25%）中使用了环境AI书记员，用户之间存在显著差异。与基线相比，每份记录的中位时间显著减少了0.57分钟。每日中位记录时间、下班后记录时间和EHR总时间也分别显著减少了6.89、5.17和19.95分钟/天。
环境AI书记员的早期试点显示出较高的使用率，并减少了记录和使用EHR所花费的时间。存在显著的个体差异。
由大语言模型驱动的环境AI书记员可能会减轻记录负担。需要进一步研究以确定哪些用户从当前技术中受益最大，以及未来的迭代如何支持更广泛的用户群体。
