The impact of the Ontario quality-based procedures funding model on radical prostatectomy outcomes
Bibliographic record
Abstract
INTRODUCTION: In 2015, radical prostatectomy (RP) in Ontario transitioned to the quality-based procedures (QBP) funding model, which assigns disbursement from surgical quality indicator (QI) outcome performance. The objective of this study was to assess the QBP QI outcomes before and after implementation of the QBP funding model for RP, and to determine whether changes seen were attributable to the QBP model. METHODS: We conducted a population-based, retrospective cohort study including all men who underwent RP for prostate cancer in Ontario from 2010-2019. We used administrative data from Ontario's health databases to gather surgical and QI outcome data. Our primary outcomes were the five measurable QBP QIs outlined by the province. We performed a pre- and post-intervention comparison, in addition to an interrupted-time series (ITS) analysis. RESULTS: Two of the five QIs improved after implementation of the QBP model (complication rate: 11.89% vs. 9.96%, p<0.001; proportion meeting length of stay target: 78.11% vs. 86.84%, p<0.001). ITS analysis revealed that there was no difference in trend in either outcome between pre- and post-implementation periods (p=0.913 and p=0.249, respectively). Two QIs were worse in the post-implementation period (unplanned visit rate: 23.45% vs. 25%, p=0.015; proportion meeting Wait 2 target: 94.39% vs. 92.88%, p<0.001). ITS revealed no significant trend changes post-implementation (p=0.260 and p=0.272, respectively). There was no difference in re-operation rate (2.84% vs. 2.45%, p=0.107). CONCLUSIONS: The QBP model for RP corresponds with mixed QI changes, but further analysis suggests that these changes were pre-existing trends and not attributable to the model.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".