Fraser Health Uses Mathematical Programming to Plan Its Inpatient Hospital Network
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Bibliographic record
Abstract
Fraser Health (FH), a British Columbia health authority that serves more than 1.5 million people, must increase its acute care capacity significantly over the next 15 years because of anticipated population growth and aging. The distribution of the projected capacity over each of FH's 12 hospitals depends on the mix of clinical services to be provided at each site, a decision guided by population needs and clinical practices. We present a multiperiod mathematical programming model that we developed to provide options for configuring the system, specifically the location of clinical services and allocation of bed capacity across the hospitals. The decisions in the model are based on population access, critical mass standards, and clinical adjacencies. We describe its application in a long-term planning initiative that FH undertook. Extensive scenario analyses allowed administrators, clinicians, and planners to test multiple system configurations, gain a robust understanding of the trade-offs between these configurations, and formalize the planning process for acute care services.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it