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在医疗保健领域建立组织人工智能治理:加拿大的一个案例研究。

Establishing organizational AI governance in healthcare: a case study in Canada.

作者信息

Kim Jee Young, Hasan Alifia, Kueper Jacqueline, Tang Terence, Hayes Chris, Fine Benjamin, Balu Suresh, Sendak Mark

机构信息

Duke Institute for Health Innovation, Durham, NC, USA.

Trillium Health Partners, Mississauga, ON, Canada.

出版信息

NPJ Digit Med. 2025 Aug 15;8(1):522. doi: 10.1038/s41746-025-01909-3.

DOI:10.1038/s41746-025-01909-3
PMID:40817280
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12356831/
Abstract

This research applies the People, Process, Technology, and Operations (PPTO) framework to develop AI governance within a large hospital system in Canada that is early in AI adoption. Stakeholder interviews identified the organization's strengths, gaps, and priorities for AI governance, providing foundational insights into the organization's readiness and needs. Co-design workshops then adapted the PPTO framework to the organization's specific context. Together, these efforts led to the creation of policies and the formation of an AI governance committee within the organization. This work demonstrates that the PPTO framework is a practical and adaptable tool for developing AI governance in real-world healthcare settings. It also addresses a critical gap in the field by generating empirical evidence of how a conceptual AI governance framework can be implemented within healthcare delivery organizations to drive organizational change.

摘要

本研究应用人员、流程、技术与运营(PPTO)框架,在加拿大一家刚开始采用人工智能的大型医院系统内开展人工智能治理工作。通过与利益相关者进行访谈,确定了该组织在人工智能治理方面的优势、差距和优先事项,为了解该组织的准备情况和需求提供了基础见解。随后,通过协同设计研讨会,使PPTO框架适应该组织的具体情况。这些工作共同促成了政策的制定以及该组织内人工智能治理委员会的成立。这项工作表明,PPTO框架是在现实世界的医疗环境中开展人工智能治理的实用且可调整的工具。它还通过提供实证证据,证明了概念性的人工智能治理框架如何在医疗服务组织中实施以推动组织变革,从而填补了该领域的一个关键空白。

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本文引用的文献

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PLOS Glob Public Health. 2025 Jun 4;5(6):e0003862. doi: 10.1371/journal.pgph.0003862. eCollection 2025.
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Exploring the complex nature of implementation of Artificial intelligence in clinical practice: an interview study with healthcare professionals, researchers and Policy and Governance Experts.探索人工智能在临床实践中应用的复杂本质:一项对医疗保健专业人员、研究人员以及政策与治理专家的访谈研究
PLOS Digit Health. 2025 May 7;4(5):e0000847. doi: 10.1371/journal.pdig.0000847. eCollection 2025 May.
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Impact of digital health technologies adoption on healthcare workers' performance and workload: perspective with DOI and TOE models.采用数字健康技术对医护人员绩效和工作量的影响:基于DOI和TOE模型的视角
BMC Health Serv Res. 2025 Feb 18;25(1):271. doi: 10.1186/s12913-025-12414-4.
4
Benefits and Risks of AI in Health Care: Narrative Review.人工智能在医疗保健中的益处与风险:叙述性综述
Interact J Med Res. 2024 Nov 18;13:e53616. doi: 10.2196/53616.
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Healthcare leaders' experiences of implementing artificial intelligence for medical history-taking and triage in Swedish primary care: an interview study.医疗保健领导者在瑞典初级保健中实施人工智能进行病史采集和分诊的经验:一项访谈研究。
BMC Prim Care. 2024 Jul 24;25(1):268. doi: 10.1186/s12875-024-02516-z.
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TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.TRIPOD+AI 声明:报告使用回归或机器学习方法的临床预测模型的更新指南。
BMJ. 2024 Apr 16;385:e078378. doi: 10.1136/bmj-2023-078378.
7
The algorithm journey map: a tangible approach to implementing AI solutions in healthcare.算法历程图:在医疗保健领域实施人工智能解决方案的切实方法。
NPJ Digit Med. 2024 Apr 9;7(1):87. doi: 10.1038/s41746-024-01061-4.
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JAMA Netw Open. 2023 Dec 1;6(12):e2345050. doi: 10.1001/jamanetworkopen.2023.45050.
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