Applying the British Columbia Health System Matrix (BCHSM) population segmentation framework to support integrated care in Ontario, Canada.
Bibliographic record
Abstract
ObjectiveTo adapt the BCHSM population segmentation methodology to Ontario’s health administrative data to identify mutually exclusive segments with similar health care needs to support integrated care efforts and population health management in Ontario, Canada. To compare health system related costs across derived segments to identify opportunities for better integrated care. ApproachWe identified Ontarians alive with valid health card numbers as of April 1, 2020 (n =14,358,565) and created a matrix of prior utilization, cost and diagnoses using linked health administrative databases. Using a hierarchical technique, we assigned individuals into one of 14 BCHSM segments based on the greatest health care needs. Segments of need range from non-users (low need) to end-of-life patients (greatest need). We report the distribution of individual characteristics, average monthly costs across segments and further stratified health care costs by quintile of material deprivation within segments. ResultsThe largest segment was the healthy (low) users (43%) followed by low chronic conditions (28%) and non-users (10%). Five segments comprised <1% of the total population: end-of-life, frail in care, cancer, frail in the community and child and youth major. Average costs per month alive increased from $28 for the non-user segment to $5,100 for the end-of-life segment (0.5% of the population). Costs in the Frail with high chronic conditions segment ($2,740/mo) were 3-times higher than costs in the high chronic conditions segment ($930/mo), 6-times higher than costs in the medium chronic conditions segment ($450/mo), and 14-times higher than costs in the low chronic conditions segment ($193/mo). Results were generally more favourable in areas of low (vs high) material deprivation overall and within population segments. ConclusionUsing Ontario’s linkable health administrative data we have created an Ontario adaptation of the BCHSM needs-based population segmentation approach. Segmentation supports population health management as well as helping identify opportunities for improvement to strengthen integrated care and potential cost savings.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".