Impact of Quality-based Procedures on orthopedic care quantity and quality in Ontario Hospitals
Bibliographic record
Abstract
In 2012 the Ontario Ministry of Health introduced Quality-Based Procedures (QBPs), whereby for a selected set of medical interventions hospitals started to be reimbursed based on the price by volume formula, with the expectation that payments would be subsequently adjusted with respect to hospital performance on quality indicators. From the onset, unilateral hip and knee replacements were included in QBPs, whereas bilateral hip and knee replacements were added in 2014. In complement to QBPs, in 2012 the Health-Based Allocation Model (HBAM) was phased in allowing part of hospital funding to be tied to municipality-level patient and hospital characteristics. Using patient-level data from Canadian Discharge Abstract Database (DAD), we evaluate through a difference-in-difference approach the impact of QBPs/HBAM on the volume and quality of targeted procedures and other types of joint replacements plausibly competing for hospital resources. After controlling for patient, hospital and regional characteristics, we found a significant decrease in acute length of stay associated to QBPs, as well as a marked shift towards patients being discharged home with/without post-operative supporting services. However, evidence with regards to spillover effects and quality improvement across all joint replacement types is weak. Results are robust to various model specifications, and different estimation techniques, including matching methods and synthetic control groups.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".