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BackgroundWith 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.ObjectiveTo identify the barriers that patients, providers, and health leaders perceive in relation to implementing AI in primary care and strategies to overcome them.Design12 virtual deliberative dialogues. Dialogue data were thematically analyzed using a combination of rapid ethnographic assessment and interpretive description techniques.SettingVirtual sessions.ParticipantsParticipants from eight provinces in Canada, including 22 primary care service users, 21 interprofessional providers, and 5 health system leaders.ResultsThe 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.LimitationsOnly 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.ConclusionsThese 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.

Introduction: High-quality primary care can reduce avoidable emergency department visits and emergency hospitalizations. The availability of electronic medical record (EMR) data and capacities for data storage and processing have created opportunities for predictive analytics. This systematic review examines studies which predict emergency department visits, hospitalizations, and mortality using EMR data from primary care., Methods: Six databases (Ovid MEDLINE, PubMed, Embase, EBM Reviews (Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, Cochrane Central Register of Controlled Trials, Cochrane Methodology Register, Health Technology Assessment, NHS Economic Evaluation Database), Scopus, CINAHL) were searched to identify primary peer-reviewed studies in English from inception to February 5, 2020. The search was initially conducted on January 18, 2019, and updated on February 5, 2020., Results: A total of 9456 citations were double-reviewed, and 31 studies met the inclusion criteria. The predictive ability measured by C-statistics (ROC) of the best performing models from each study ranged from 0.57 to 0.95. Less than half of the included studies used artificial intelligence methods and only 7 (23%) were externally validated. Age, medical diagnoses, sex, medication use, and prior health service use were the most common predictor variables. Few studies discussed or examined the clinical utility of models., Conclusions: This review helps address critical gaps in the literature regarding the potential of primary care EMR data. Despite further work required to address bias and improve the quality and reporting of prediction models, the use of primary care EMR data for predictive analytics holds promise., Competing Interests: Conflict of interest: The authors have no conflicts of interest to declare., (© Copyright by the American Board of Family Medicine.)