Describing a complex primary health care population in a learning health system to support future decision support and artificial intelligence initiatives
Bibliographic record
Abstract
ABSTRACT Introduction 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, including development of decision support tools. To properly characterize complex populations, both simple statistical and artificial intelligence techniques can be useful. We present the first large-scale description of the population served by one of the first primary care LHS in North America. Objectives Our objective is to describe sociodemographic, clinical, and health care use characteristics of adult primary care clients served by the Alliance for Healthier Communities, which provides team-based primary health care through Community Health Centres (CHCs) across Ontario, Canada. Methods Using electronic health record data from 2009-2019 for all CHCs, we perform table-based summaries for each characteristic; and apply unsupervised leaning techniques to explore patterns of common condition co-occurrence, care provider teams, and care frequency. Results Of the 221,047 eligible clients, those at CHCs that primarily serve those most at risk (homeless, mental health, addictions) tend to have more chronic conditions and social determinants of health, which are also prominent in clients with multimorbidity. 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 within- and between-client variability in care frequency. Example methodological considerations learned for future LHS initiatives include the need to carefully consider the level of analysis and associated implications for data quality and target population, heterogeneity in conditions and care characteristics, and non-uniform risk profiles across the care history. Conclusions We demonstrate the use of methods from statistics and artificial intelligence, applied with an epidemiological lens, to provide an overview of a complex primary care population. In addition to substantive findings, we discuss implications for future LHS initiatives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".