Financial Characteristics of Outcomes-Based Agreements: What Do Canadian Public Payers and Pharmaceutical Manufacturers Prefer?
Bibliographic record
Abstract
OBJECTIVES: This study sought to gain insight into the financial characteristics of outcomes-based agreements (OBAs) considered most suitable to Canadian public payers and pharmaceutical manufacturers, and the rationale for their preferences. METHODS: A total of 17 public payers and pharmaceutical manufacturers participated in semistructured qualitative interviews, which assessed their knowledge of OBAs and their preferred financial characteristics. RESULTS: Payers identified 5 OBA financial models that they considered both acceptable and feasible, in no preferential order: (1) discontinuation of therapy, (2) rebates for nonresponders, (3) free trial period, (4) adjustable pricing, and (5) blended rebate. Payers had a clear preference for short-term OBAs (<1 year), whereas both payers and manufacturers agreed OBAs with longer durations (up to 5 years) would be manageable if appropriately designed. Six key success factors to design suitable and acceptable OBA financial models were identified, including the areas of interim reporting, easily measurable health outcomes, trusted data sources, engaging unbiased third-party data experts, harmonizing OBA billing methods, and the inclusion of budget caps. CONCLUSIONS: Manufacturers and payers showed high level of interest in OBAs and a robust understanding of their potential role in supporting timely market access for patients in need, with the caveat that they need to be carefully designed to provide value. Further opportunities for discussion and engagement between public payers and manufacturers are needed to establish how to implement OBAs at a pan-Canadian level and how individual provinces and territories can incorporate them within their existing governance infrastructures.
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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 | 0.026 | 0.150 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".