Reforming Drug Pricing: Institutional Lessons from Most Favored Nation (MFN) and Value‐Based Care (VBC) Across Three Health Systems
Bibliographic record
Abstract
Context: Global efforts to reform prescription drug pricing must navigate the trade-offs between four core objectives: cost containment, innovation incentives, equitable access, and administrative feasibility. In the United States, the Most Favored Nation (MFN) pricing rule and emerging value-based care (VBC) models reflect divergent strategies for aligning prices with therapeutic value. However, comparative analyses across different health systems are limited. Methods: A structured trade-off matrix was employed to compare pharmaceutical pricing strategies in the U.S., Canada, and the UK across four dimensions: cost containment, innovation incentives, equity in access, and implementation feasibility. The comparison is drawn from peer-reviewed scholarly literature, policy studies, and regulatory publications spanning the period 2007 to 2025. Findings: The MFN model offers short-term savings but can also hinder innovation and outcome-based reimbursement in fragmented systems such as the U.S. The UK achieves a significant value, as well as pricing alignment, from centralized authorities such as NICE and the VPAG scheme. Canada, through the PMPRB, can enforce adequate price controls and international referencing; however, it fails to incorporate VBC principles and risk-sharing mechanisms. Conclusions: Global models offer guiding principles, whereas sustainable pricing reforms must be designed to fit local political, institutional, and market conditions. Value-based incentives combined with cross-country yardsticks in hybrid models, though flexible and transparent, can create a more feasible reform approach for the U.S..
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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 | 0.024 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".