• Rebecca Johnson, Thomas Chang, Rahim Moineddin, Tara Upshaw, Noah Crampton, Emma Wallace, and Andrew D Pinto.
• From the Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada (RJ, ADP); Institute for Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada (RJ, RM, ADP); Undergraduate Medical Education, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada (TC); Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada (TU); Department of Family and Community Medicine, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada (NC); Department of Family and Community Medicine, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada (NC, ADP); Department of General Practice, University College Cork, Cork, Ireland (EW); Department of Family and Community Medicine, St. Michael's Hospital, Toronto, Ontario, Canada (ADP).
• J Am Board Fam Med. 2024 Jul 1; 37 (4): 583606583-606.

IntroductionHigh-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.MethodsSix 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.ResultsA 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.ConclusionsThis 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.© Copyright by the American Board of Family Medicine.

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