Identifying High Users of Healthcare in British Columbia, Alberta and Manitoba
Bibliographic record
Abstract
This paper describes efforts to identify high users of healthcare in British Columbia, Alberta and Manitoba. British Columbia's Ministry of Health adopted a population segmentation approach that segments the province's population into 14 matrix populations. The approach has been used to inform strategic planning, especially for populations with frailty or chronic conditions. Alberta has pursued a similar but different population segmentation approach. A model capable of allocating healthcare costs to the individual level revealed that 5% of the population consumed approximately 66% of the allocated costs. Additional analysis of this 5% produced seven population profiles that have been used in planning. Manitoba's approach has focused on hospital inpatients and users of emergency departments (EDs). High users of hospitals were defined as the top 5% of users of days of inpatient care. Despite representing only 0.33% of the Manitoba population, this group received 45% of all days of hospital care. Using a threshold of seven or more ED visits annually, 2.2% of all ED patients were defined as frequent users, representing 13.5% of all visits. The paper demonstrates that common themes emerge from the approaches taken in each jurisdiction.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".