The Effect of Pay for Performance in the Emergency Department on Patient Waiting Times and Quality of Care in Ontario, Canada: A Difference-in-Differences Analysis
Bibliographic record
Abstract
STUDY OBJECTIVE: In 2008, a pay-for-performance program was implemented in sequential waves in Ontario emergency departments (EDs), with the aim of reducing length of stay. We seek to evaluate its effects on ED length of stay and quality of care. METHODS: This was a retrospective observational study of ED visits in Ontario from April 1, 2007, to March 31, 2011, using multivariable difference-in-differences analysis. Pay-for-performance hospitals and matched control sites were selected for each of 3 waves of the program. The primary outcome was 90th percentile ED length of stay; we also examined quality-of-care indicators. RESULTS: Pay-for-performance hospitals had a modest reduction in overall adjusted 90th percentile ED length of stay in wave 1 (-36 minutes; 95% confidence interval [CI] -50 to -21 minutes), but not in wave 2 (-14 minutes; 95% CI -30 to 2 minutes) or wave 3 (-7 minutes; 95% CI -23 to 8 minutes). ED admitted patients had a pronounced reduction in adjusted 90th percentile length of stay in wave 1 (-225 minutes; 95% CI -263 to -188 minutes) and wave 2 (-133 minutes; 95% CI -175 to -91 minutes). Nonadmitted low-acuity patients had reductions in adjusted 90th percentile ED length of stay in wave 1 (-24 minutes; 95% CI -29 to -18 minutes) and wave 3 (-19 minutes; 95% CI -24 to -14 minutes). The program did not negatively affect ED quality-of-care measures, such as 30-day mortality or readmission rates. CONCLUSION: Pay-for-performance was associated with modest overall benefits for ED length of stay without adversely affecting quality of care.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".