Using Data From Ontario’s Episode-Based Funding Model to Assess Quality of Chemotherapy
Bibliographic record
Abstract
INTRODUCTION: A new episode-based funding model for ambulatory systemic therapy was implemented in Ontario, Canada on April 1, 2014, after a comprehensive knowledge transfer and exchange strategy with providers and administrators. An analysis of the data from the first year of the new funding model provided an opportunity to assess the quality of chemotherapy, which was not possible under the old funding model. MATERIALS AND METHODS: Options for chemotherapy regimens given with adjuvant/curative intent or palliative intent were informed by input from disease site groups. Bundles were developed and priced to enable evidence-informed best practice. Analysis of systemic therapy utilization after model implementation was performed to assess the concordance rate of the treatments chosen with recommended practice. The actual number of cycles of treatment delivered was also compared with expert recommendations. RESULTS: Significant improvement compared with baseline was seen in the proportion of adjuvant/curative regimens that aligned with disease site group-recommended options (98% v 90%). Similar improvement was seen for palliative regimens (94% v 89%). However, overall, the number of cycles of adjuvant/curative therapy delivered was lower than recommended best practice in 57.5% of patients. There was significant variation by disease site and between facilities. CONCLUSION: Linking funding to quality, supported by knowledge transfer and exchange, resulted in a rapid improvement in the quality of systemic treatment in Ontario. This analysis has also identified further opportunities for improvement and the need for model refinement.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".