Using Population Segmentation to Identify Opportunities for Integrated Care in Ontario, Canada.
Bibliographic record
Abstract
Ontario, Canada has introduced Ontario Health Teams (OHTs) to advance integrated care and population health management. There are currently 54 OHTs covering a population of nearly 14 Million individuals. The teams are expected to plan and implement new care models that improve quadruple aim goals of patient and provider experience, health outcomes and cost efficiency. The Health System Performance Network (HSPN) is funded by the Ontario Ministry of Health to provide evaluation and research to support OHTs. The HSPN is using population segmentation alongside performance measurement to support OHTs to identify populations for improvement. OHTs are using the data provided by HSPN to target opportunities for improvement. This presentation summarizes the performance measures and population segmentation approach used in Ontario to advance integrated care and population health. HSPN is using the British Columbia Health System Matrix (BCHSM), based on the Bridges to Health model to segment the population for each OHT. HSPN has identified 10 overall indicators of performance for OHTs ranging from premature mortality to continuity of physician care. There are also 5 measures respectively in several specific populations including older adults, mental health, and end of life. Indicators are also examined according to socioeconomic strata. This presentation will overview the population segmentation methodology, the ways that performance indicators vary according to population segments, and the ways that OHTs receive the data and are coached in the use of data to target and advance integrated care initiatives. The BCHSM provides 14 different population segments ranging from non-users to low-users to frail older adults in the community to people at the end of life. Increasing frailty and clinical complexity is strongly related to the population segments/groups and premature mortality and total system costs correspond as well to increasing patient complexity. The BCHSM has high face-validity. Improvement indicators for mental health and health system priorities such as emergency presentations for mental health reasons and delayed discharge from acute hospital are also more common in specific population groups which helps to prioritize and target interventions. These data are supporting the development of specific integrated care programs to address health needs in underserved populations.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".