Understanding Clinician Perceptions of GenAI: A Mixed Methods Analysis of Clinical Documentation Tasks.

IF 5.7 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
David Fraile Navarro, A Baki Kocaballi, Shlomo Berkovsky
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Abstract

This mixed-methods study evaluated clinicians' user experience (UX) with Generative AI (GenAI) in Electronic Health Record (EHR) systems across three clinical documentation tasks (Information Extraction, Summarization, and Speech-to-Text) at varying levels of user supervision (low, medium, high), focusing on workflow improvements, safety, and acceptable automation levels. Using conceptual prototyping in a usability study framework, we evaluated how incorporating GenAI into EHR could support the three documentation tasks at varying automation levels. A total of 38 clinicians interacted with the prototype and completed a questionnaire on task relevance, perceived importance, desired automation level, and EHR satisfaction. Both quantitative (descriptive statistics, Kruskal-Wallis tests, Spearman correlations) and qualitative (thematic) analyses were conducted with equal priority to explore preferences, perceived safety, and practical requirements. Clinicians showed positive reception to GenAI integration, particularly for streamlining documentation. While task relevance and importance were strongly correlated, EHR satisfaction did not significantly predict automation acceptance. Medium automation emerged as the preferred level, considered "safe with caution". Five key themes emerged from qualitative analysis: efficiency and quality benefits; system reliability concerns; safety and medico-legal considerations; automation bias and loss of nuance; and deployment requirements including adjustable settings and oversight. While clinicians welcome GenAI-driven documentation, they prefer moderate automation to balance efficiency with clinical control. Successful integration requires addressing safety concerns, conducting real-world trials, and mitigating potential biases and medico-legal challenges. These findings suggest a cautious but optimistic path forward for AI integration in EHR systems, emphasizing the importance of maintaining clinician oversight while leveraging automation benefits.

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了解临床医生对基因ai的看法:临床文献任务的混合方法分析。
这项混合方法研究评估了临床医生在电子健康记录(EHR)系统中使用生成式人工智能(GenAI)的用户体验(UX),涉及三个临床文档任务(信息提取、摘要和语音到文本),在不同级别的用户监督(低、中、高)下,重点关注工作流程改进、安全性和可接受的自动化水平。在可用性研究框架中使用概念原型,我们评估了将GenAI纳入EHR如何支持不同自动化级别的三个文档任务。共有38名临床医生与原型进行了互动,并完成了任务相关性、感知重要性、期望自动化水平和电子病历满意度的问卷调查。定量分析(描述性统计、Kruskal-Wallis测试、Spearman相关性)和定性分析(专题分析)均以同等优先级进行,以探索偏好、感知安全性和实际需求。临床医生对GenAI的整合表现出积极的接受,特别是在简化文档方面。虽然任务相关性和重要性强相关,但电子病历满意度不显著预测自动化接受度。中等自动化程度被认为是“谨慎安全”的首选水平。定性分析产生了五个关键主题:效率和质量效益;系统可靠性问题;安全和医疗法律方面的考虑;自动化偏差和细微差别的丧失;部署要求包括可调整的设置和监督。虽然临床医生欢迎基因驱动的文档,但他们更喜欢适度的自动化,以平衡效率和临床控制。成功的整合需要解决安全问题,进行真实世界的试验,减轻潜在的偏见和医学法律挑战。这些发现表明,将人工智能整合到电子病历系统中是一条谨慎但乐观的道路,强调了在利用自动化优势的同时保持临床医生监督的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Medical Systems
Journal of Medical Systems 医学-卫生保健
CiteScore
11.60
自引率
1.90%
发文量
83
审稿时长
4.8 months
期刊介绍: Journal of Medical Systems provides a forum for the presentation and discussion of the increasingly extensive applications of new systems techniques and methods in hospital clinic and physician''s office administration; pathology radiology and pharmaceutical delivery systems; medical records storage and retrieval; and ancillary patient-support systems. The journal publishes informative articles essays and studies across the entire scale of medical systems from large hospital programs to novel small-scale medical services. Education is an integral part of this amalgamation of sciences and selected articles are published in this area. Since existing medical systems are constantly being modified to fit particular circumstances and to solve specific problems the journal includes a special section devoted to status reports on current installations.
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