Hassan Hadeel, Zipursky Amy Rebecca, Rabbani Naveed, You Jacqueline Guan-Ting, Tse Gabriel, Orenstein Evan, Parsons Chase Richard, Jessa Karim, Lawton Greg, Shin H Stella, Sung Lillian, Yan Adam Paul
Department of Paediatrics, Division of Hematology/Oncology, The Hospital for Sick Children, Toronto, Canada.
Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada.
Appl Clin Inform. 2025 Apr 30. doi: 10.1055/a-2597-2017.
Artificial intelligence (AI) scribes use advanced speech recognition and natural language processing to automate clinical documentation and ease administrative burden. However, little is known about the impact of AI scribes on clinicians, patients and organizations.
(1) To propose an evaluation framework to guide future AI scribe implementations, (2) to describe the impact of AI scribes along the domains proposed in the developed evaluation framework, and (3) to identify gaps in the AI scribe implementation literature to be evaluated in future studies.
Databases including Embase, Embase Classic and Ovid Medline were searched, and a manual review was conducted of the New England Journal of Medicine AI. Studies published after 2021 that reported on the implementation of AI scribes in healthcare were included. Descriptive analysis was undertaken. Quality assessment was undertaken using the Newcastle-Ottawa Scale. The nominal group technique was used to develop an evaluation framework.
Eleven studies met the inclusion criteria, with ten published in 2024. The most frequently used AI scribe was Dragon Ambient eXperience (DAX) (n=7, 64%). While clinicians often reported improved documentation quality, AI scribe accuracy varied, frequently requiring manual edits and raising occasional concerns about errors. Ten studies reported improvements in at least one efficiency metric, and ten studies highlighted positive impacts on clinician wellness and burnout. Patient experience was assessed in three studies, all reporting favorable outcomes.
AI scribes represent a promising tool for improving clinical efficiency and alleviating documentation burden. This systematic review highlights the potential benefits of AI scribes, including reduced documentation time and enhanced clinician satisfaction, while also identifying critical challenges such as variable adoption, performance limitations, and gaps in evaluation.
人工智能（AI）抄写员利用先进的语音识别和自然语言处理技术实现临床文档自动化，并减轻行政负担。然而，关于AI抄写员对临床医生、患者和组织的影响，我们知之甚少。
（1）提出一个评估框架，以指导未来AI抄写员的实施；（2）描述AI抄写员在所制定评估框架提出的各个领域的影响；（3）找出AI抄写员实施文献中的空白，以便在未来研究中进行评估。
检索了包括Embase、Embase Classic和Ovid Medline在内的数据库，并对《新英格兰医学杂志》人工智能进行了人工审查。纳入2021年后发表的报告医疗保健领域AI抄写员实施情况的研究。进行描述性分析。使用纽卡斯尔-渥太华量表进行质量评估。采用名义群体技术制定评估框架。
11项研究符合纳入标准，其中10项于2024年发表。使用最频繁的AI抄写员是Dragon Ambient eXperience（DAX）（n = 7，64%）。虽然临床医生经常报告文档质量有所提高，但AI抄写员的准确性各不相同，经常需要人工编辑，偶尔还会引发对错误的担忧。10项研究报告至少有一项效率指标有所改善，10项研究强调了对临床医生健康和职业倦怠的积极影响。三项研究评估了患者体验，均报告了良好结果。
AI抄写员是提高临床效率和减轻文档负担的一个有前途的工具。这项系统评价突出了AI抄写员的潜在益处，包括减少文档时间和提高临床医生满意度，同时也识别出了一些关键挑战，如采用率不一、性能限制和评估方面的差距。
