Implementing artificial intelligence in Canadian primary care: Barriers and strategies identified through a national deliberative dialogue

Background: With large volumes of longitudinal data in electronic medical records from diverse patients, primary care is primed for disruption by artificial intelligence (AI) technology. With AI applications in primary care still at an early stage in Canada and most countries, there is a unique opportunity to engage key stakeholders in exploring how AI would be used and what implementation would look like. Objective: To identify the barriers that patients, providers, and health leaders perceive in relation to implementing AI in primary care and strategies to overcome them. Design: 12 virtual deliberative dialogues. Dialogue data were thematically analyzed using a combination of rapid ethnographic assessment and interpretive description techniques. Setting: Virtual sessions. Participants: Participants from eight provinces in Canada, including 22 primary care service users, 21 interprofessional providers, and 5 health system leaders Results: The barriers that emerged from the deliberative dialogue sessions were grouped into four themes: (1) system and data readiness, (2) the potential for bias and inequity, (3) the regulation of AI and big data, and (4) the importance of people as technology enablers. Strategies to overcome the barriers in each of these themes were highlighted, where participatory co-design and iterative implementation were voiced most strongly by participants. Limitations: Only five health system leaders were included in the study and no self-identifying Indigenous people. This is a limitation as both groups may have provided unique perspectives to the study objective. Conclusions: These findings provide insight into the barriers and facilitators associated with implementing AI in primary care settings from different perspectives. This will be vital as decisions regarding the future of AI in this space is shaped.