The determinants of efficiency in the Canadian health care system
Bibliographic record
Abstract
In spite of the vast number of studies measuring economic efficiency in health care, there has been little take-up of this evidence by policy-makers to date. This study provides an illustration of how a system-level study drawing on best practice in empirical measurement of efficiency may be of practical use to health system decision makers and managers. We make use of the rich data available in Canada to undertake a robust two-stage data envelopment analysis to calculate efficiency at the regional (sub-provincial) level. Decisions about what the health system produces (the outcome to measure efficiency against) and what are the resources it has to produce that outcome were based on interviews and consultation with health system decision makers. Overall, we find large inefficiencies in the Canadian health care system, which could improve outcomes (here, measured as a reduction in treatable causes of death) by between 18 and 35% across our analyses. Also, we find that inefficiencies are the result of three main sets of factors that policy makers could pay attention to: management factors, such as hospital re-admissions; public health factors, such as obesity and smoking rates; and environmental factors such as the population's average income.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".