Describing a complex primary health care population in a learning health system to support future decision support and artificial intelligence initiatives

Abstract

Background: Learning health systems (LHS) use data to improve care. Descriptive epidemiology to reveal health states and needs of the LHS population is essential for informing LHS initiatives. To properly characterize complex populations, both simple statistical and artificial intelligence techniques, applied with an epidemiological lens, can be useful. We present the first large-scale description of the population served by one of the first primary care LHS in Canada. Methods: We use electronic health record data from 2009-2019 to describe sociodemographic, clinical, and health care use characteristics of adult primary care clients served by the Alliance for Healthier Communities. In addition to simple summary statistics, we apply unsupervised leaning techniques to explore patterns of common condition co-occurrence, care provider teams, and care frequency. Results: There are 221 047 eligible clients. Clients at community health centres that primarily serve those most at risk (homeless, mental health, addictions) and clients with multimorbidity tended to have more social determinants of health and chronic conditions. Most care is provided by physician and nursing providers, with heterogeneous combinations of other provider types. A subset of clients have many issues addressed within single-visits and there is notable within- and between-client variability in care frequency. Conclusions: This population-level overview of clients served by the Alliance provides a foundation for future LHS initiatives. In addition to substantive findings, we demonstrate the use of methods from statistics and artificial intelligence to describe a complex primary care population. We discuss implications for future initiatives, including development of decision support tools.