International Best Practices For Negotiating ‘Reimbursement Contracts’ With Price Rebates From Pharmaceutical Companies
Bibliographic record
Abstract
Reimbursement contracts, in which health insurers receive rebates from drug manufacturers instead of paying the transparent list price, are becoming increasingly common worldwide. Through interviews with policy makers in nine high-income countries, we describe the use of these contracts around the globe and identify related policy challenges and best practices. Of the nine countries surveyed, the majority routinely use confidential reimbursement contracts. This alternative to drug coverage at list prices offers benefits but is not without challenges. Payers face increased administrative costs, difficulties enforcing contracts, and reduced information about prices paid by others. Among the best practices identified, policy makers recommend establishing clear and consistent processes for negotiating contracts with relatively simple rebate structures and transparency to the public about the existence, purpose, and type of reimbursement contracts in place. Policy makers should also work to address undesirable price disparities within their countries and internationally, which may occur as a result of this new pricing paradigm.
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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 | 0.250 | 0.261 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.026 | 0.019 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".