Using External Reference Pricing In Medicare Part D To Reduce Drug Price Differentials With Other Countries
Bibliographic record
Abstract
Many countries use external reference pricing to help determine drug prices. However, external reference pricing has received little attention in the US-perhaps because the US is often the first adopter of drugs. External reference pricing could be used to set prices for drugs that were already established in the market. We compared the price differentials between the US and the UK, Japan, and Ontario (Canada) for single-source brand-name drugs that had been on the market for at least three years. We found that the prices averaged 3.2-4.1 times higher in the US after rebates were considered. The price differential for individual drugs varied from 1.3 to 70.1. The longer a drug remained on the market, the greater the differential. The estimated savings to Medicare Part D of adopting the average price of drugs in the reference countries was $72.9 billion in 2018. Medicare could use external reference pricing in Part D to improve affordability for patients.
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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 | 0.017 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".