A Comparative Analysis of International Drug Price Negotiation Frameworks: An Interview Study of Key Stakeholders
Bibliographic record
Abstract
Policy Points Health care systems around the world rely on a range of methods to ensure the affordability of prescription drugs, including negotiating prices soon after drug approval and relying on formal clinical assessments that compare newly approved therapies with existing alternatives. The negotiation framework established under the Inflation Reduction Act is far more limited than other frameworks explored in this study. Adding elements from these frameworks could lead to more effective price negotiation in the United States. CONTEXT: In 2022, Congress passed the Inflation Reduction Act, which allowed Medicare, for the first time, to begin negotiating the prices for certain high-cost brand-name prescription drugs. Many other industrialized countries negotiate drug prices, and we sought to compare and contrast key features of the negotiation process across several health systems. We focused, in particular, on the criteria for selecting drugs for price negotiation, procedures for negotiation, factors that influence negotiated prices, and how prices are implemented. METHODS: We included four G7 countries in our analysis (Canada, France, Germany, and the United Kingdom [England]), two Benelux countries (Belgium and the Netherlands), and one Scandinavian country (Norway) with long-established frameworks for drug price negotiation. We also analyzed the Veterans Affairs Health System in the United States. For each system, we gathered relevant legislation, government publications, and guidelines to understand negotiation frameworks, and we reached out to key drug price negotiators in each system to conduct semistructured interviews. All interviews were recorded, transcribed, and coded, and data were analyzed based on an internal assessment tool that we developed. FINDINGS: All eight systems negotiate the prices of brand-name prescription drugs soon after approval and rely on formal clinical assessments that compare newly approved drugs with existing therapies. Systems in our study differed on characteristics such as whether the body performing clinical assessments is separate from the negotiating authority, how added health benefit is assessed, whether explicit willingness-to-pay thresholds are employed, and how specific approaches for priority disease areas are taken. CONCLUSIONS: High-income countries around the world adopt different approaches to conducting price negotiations on brand-name drugs but coalesce around a set of practices that will largely be absent from the current Medicare negotiation framework. US policymakers might consider adding some of these characteristics in the future to improve negotiation outcomes.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".