British Columbia's pay-for-performance experiment: Part of the solution to reduce emergency department crowding?
Bibliographic record
Abstract
BACKGROUND: Emergency department (ED) overcrowding continues to be a well-publicized problem in a number of countries. In British Columbia, a province in Canada, an ED pay-for-performance (ED P4P) program was initiated in 2007 to create financial incentives for hospitals to reduce patients' ED length of stay (ED LOS). This study's objectives are to determine if the ED P4P program is associated with decreases in ED LOS, and to address the ED P4P program's limitations. METHODS: We analyze monthly hospital-level ED LOS time data since the inception of the financial incentives. Since the ED P4P program was phased in at different hospitals from different health authorities over time, hospitals' data from only two regional health authorities are included in the study. RESULTS: We find association between the implementation of ED P4P and ED LOS time data. However, due to the lack of control data, the findings cannot demonstrate causality. Furthermore, our findings are from hospitals in the greater Vancouver area only. INTERPRETATION: BC's ED P4P was introduced to create incentives for hospitals to reduce ED LOS by providing incremental incentive funding. Available data indicate that the ED P4P program is associated with mixed successes in reducing ED LOS among participating hospitals.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".