Owoyemi Ayomide, Osuchukwu Joanne, Salwei Megan E, Boyd Andrew
Department of Biomedical and Health Informatics, University of Illinois Chicago, 1919 W Taylor, Chicago, IL, 60612, United States, 1 3129782703.
College of Medicine, University of Cincinnati, Cincinnati, OH, United States.
JMIRx Med. 2025 Feb 20;6:e65565. doi: 10.2196/65565.
The integration of artificial intelligence (AI) in health care settings demands a nuanced approach that considers both technical performance and sociotechnical factors.
This study aimed to develop a checklist that addresses the sociotechnical aspects of AI deployment in health care and provides a structured, holistic guide for teams involved in the life cycle of AI systems.
A literature synthesis identified 20 relevant studies, forming the foundation for the Clinical AI Sociotechnical Framework checklist. A modified Delphi study was then conducted with 35 global health care professionals. Participants assessed the checklist's relevance across 4 stages: "Planning," "Design," "Development," and "Proposed Implementation." A consensus threshold of 80% was established for each item. IQRs and Cronbach α were calculated to assess agreement and reliability.
The initial checklist had 45 questions. Following participant feedback, the checklist was refined to 34 items, and a final round saw 100% consensus on all items (mean score >0.8, IQR 0). Based on the outcome of the Delphi study, a final checklist was outlined, with 1 more question added to make 35 questions in total.
The Clinical AI Sociotechnical Framework checklist provides a comprehensive, structured approach to developing and implementing AI in clinical settings, addressing technical and social factors critical for adoption and success. This checklist is a practical tool that aligns AI development with real-world clinical needs, aiming to enhance patient outcomes and integrate smoothly into health care workflows.
在医疗环境中整合人工智能（AI）需要一种细致入微的方法，既要考虑技术性能，也要考虑社会技术因素。
本研究旨在制定一份清单，以解决医疗保健中AI部署的社会技术方面问题，并为参与AI系统生命周期的团队提供一份结构化的整体指南。
通过文献综合确定了20项相关研究，为临床AI社会技术框架清单奠定了基础。然后对35名全球医疗保健专业人员进行了一项改良的德尔菲研究。参与者在“规划”“设计”“开发”和“拟议实施”4个阶段评估了该清单的相关性。为每个项目设定了80%的共识阈值。计算四分位距（IQR）和克朗巴哈α系数以评估一致性和可靠性。
初始清单有45个问题。根据参与者的反馈，该清单被精简为34项，最后一轮所有项目达成了100%的共识（平均分>0.8，IQR为0）。根据德尔菲研究的结果，列出了最终清单，又增加了1个问题，使问题总数达到35个。
临床AI社会技术框架清单为在临床环境中开发和实施AI提供了一种全面、结构化的方法，解决了对于AI应用和成功至关重要的技术和社会因素。该清单是一种实用工具，使AI开发与现实世界的临床需求相匹配，旨在改善患者治疗效果并顺利融入医疗工作流程。
