• Tara L Upshaw, Amy Craig-Neil, Jillian Macklin, GrayCarolyn SteeleCSFrom 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, Can, ChanTimothy C YTCYFrom 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, Canad, Jennifer Gibson, and Andrew D Pinto.
• 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): 210220210-220.

BackgroundArtificial 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.MethodsWe 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.ResultsParticipants 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.InterpretationOur 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.© Copyright by the American Board of Family Medicine.

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