Measuring alignment with evidence-informed practice in Ontario’s systemic treatment funding model.
Bibliographic record
Abstract
15 Background: A new systemic treatment funding model (STFM) was implemented in Ontario on April 1, 2014, transitioning from life-time per case funding to reimbursement based on evidence-informed episodes of care. The effectiveness of the model will be evaluated against key indicators including the percent of patients on evidence-informed regimens (PPEIR). Methods: Provincial Disease Site Group (DSG) experts reviewed all chemotherapy regimens administered in Ontario over the two years prior to implementation. Each DSG identified the treatment regimens to be STFM reimbursed, based on evidence of clinical benefit according to treatment intent (curative/adjuvant vs. palliative or both). A year of pre-implementation data will serve as a baseline to assess the impact of transition to the new funding model. Clinical and administrative stakeholders have received their baseline facility-level data and will receive monthly reports, including the PPEIR, to aid in identifying and resolving clinical practice and/or data quality issues post-implementation. Results: Of approximately 1,000 regimens reviewed by the DSGs, ~100 were deemed to be evidence informed for adjuvant/curative intent, ~325 for palliative intent, and ~90 for both intents. Overall, the 2013/14 baseline provincial PPEIR was 91.6% for 16,200 treatment courses given with adjuvant/curative intent while 93.2% of 56,800 patient-months of treatment with palliative intent were aligned with the proposed evidence informed definition. Significant variation in baseline PPEIR was seen for the 29 level 1-3 provincial treatment facilities (range = 71-99%) and for the 10 different disease sites. Conclusions: Knowledge of the PPEIR utilized increases understanding of practice at the system (provincial), regional, facility and disease site level and will provide opportunities for benchmarking and ongoing improvement in quality of care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".