Payers’ experiences with confidential pharmaceutical price discounts: A survey of public and statutory health systems in North America, Europe, and Australasia
Bibliographic record
Abstract
Institutional payers for pharmaceuticals worldwide appear to be increasingly negotiating confidential discounts off of the official list price of pharmaceuticals purchased in the community setting. We conducted an anonymous survey about experiences with and attitudes toward confidential discounts on patented pharmaceuticals in a sample of high-income countries. Confidential price discounts are now common among the ten health systems that participated in our study, though some had only recently begun to use these pricing arrangements on a routine basis. Several health systems had used a wide variety of discounting schemes in the past two years. The most frequent discount received by participating health systems was between 20% and 29% of official list prices; however, six participants reported their health system received one or more discount over the past two years that was valued at 60% or more of the list prices. On average, participants reported that confidential discounts were more common, complex, and significant for specialty pharmaceuticals than for primary care pharmaceuticals. Participants had a more favorable view of the impact of confidential discount schemes on their health systems than on the global marketplace. Overall, the frequency, complexity, and scale of confidential discounts being routinely negotiated suggest that the list prices for medicines bear limited resemblance to what many institutional payers actually pay.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".