Upshaw Tara L, Craig-Neil Amy, Macklin Jillian, Gray Carolyn Steele, Chan Timothy C Y, Gibson Jennifer, Pinto Andrew D
From the Upstream Lab, MAP/Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, Ontario, Canada (TLU, CAN, JM, ADP); Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada (TLU); Undergraduate Medical Education, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada (TLU, JM); Bridgepoint Collaboratory for Research and Innovation, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada (CSG); Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada (CSG, ADP); Department of Mechanical and Industrial Engineering, Faculty of Applied Science and Engineering, University of Toronto, Toronto, Ontario, Canada (TCYC); Joint Centre for Bioethics, University of Toronto, Toronto, Ontario, Canada (JG); Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada (JG); Department of Family and Community Medicine, St. Michael's Hospital, Toronto, Ontario, Canada (ADP); Department of Family and Community Medicine, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada (ADP).
J Am Board Fam Med. 2023 Apr 3;36(2):210-220. doi: 10.3122/jabfm.2022.220171R1. Epub 2023 Mar 22.
Artificial intelligence (AI) implementation in primary care is limited. Those set to be most impacted by AI technology in this setting should guide it's application. We organized a national deliberative dialogue with primary care stakeholders from across Canada to explore how they thought AI should be applied in primary care.
We conducted 12 virtual deliberative dialogues with participants from 8 Canadian provinces to identify shared priorities for applying AI in primary care. Dialogue data were thematically analyzed using interpretive description approaches.
Participants thought that AI should first be applied to documentation, practice operations, and triage tasks, in hopes of improving efficiency while maintaining person-centered delivery, relationships, and access. They viewed complex AI-driven clinical decision support and proactive care tools as impactful but recognized potential risks. Appropriate training and implementation support were the most important external enablers of safe, effective, and patient-centered use of AI in primary care settings.
Our findings offer an agenda for the future application of AI in primary care grounded in the shared values of patients and providers. We propose that, from conception, AI developers work with primary care stakeholders as codesign partners, developing tools that respond to shared priorities.
人工智能(AI)在初级保健中的应用有限。在此背景下,那些将受到AI技术最大影响的人应指导其应用。我们与来自加拿大各地的初级保健利益相关者组织了一次全国性的审议对话,以探讨他们认为AI应如何应用于初级保健。
我们与来自加拿大8个省份的参与者进行了12次虚拟审议对话,以确定在初级保健中应用AI的共同优先事项。使用解释性描述方法对对话数据进行主题分析。
参与者认为,AI应首先应用于文档记录、实践操作和分诊任务,以期在保持以人为本的服务、关系和可及性的同时提高效率。他们认为复杂的AI驱动的临床决策支持和主动护理工具很有影响力,但也认识到潜在风险。适当的培训和实施支持是以安全、有效和以患者为中心的方式在初级保健环境中使用AI的最重要外部推动因素。
我们的研究结果为基于患者和提供者共同价值观的AI在初级保健中的未来应用提供了一个议程。我们建议,从概念阶段开始,AI开发者就应与初级保健利益相关者作为共同设计伙伴合作,开发符合共同优先事项的工具。