A Review of Foreign Pharmaceutical Pricing Structure: Focusing on a Drug Price Formula Based on Ex-factory Prices
Bibliographic record
Abstract
Upon coverage of a new drug by National Health Insurance in the Republic of Korea, the A-7 pricing is used as a reference for pharmaceutical benefit assessment.However, improvements need to be made regarding a specific formula currently used for the conversion of drug prices because the formula used to obtain the percentages is outdated and there is insufficient evidence on how the percentages were derived.In this study, the characteristics of 10 countries that have implemented external reference pricing and factors that affect drug prices such as profit margins for wholesalers and pharmacies, value-added tax (VAT), and rebates were examined with a focus on ex-factory price (EFP).In addition, we calculated EFP through conversion from each country's drug prices, compared EFP with the pharmacy purchase prices, and based on the above, drew implications for improving the foreign drug price reference values.Our results showed EFP to be publicly available in six countries (United States, France, Italy, Switzerland, Canada, and Australia) and to fall between 58% (United States) and 92% (Canada) of the pharmacy sales prices.Conversion to EFP was possible for drug prices in Germany, the United Kingdom, and Japan, as information such as pharmacy sales prices, profit margins for wholesalers and pharmacies, and VAT is made public.However, only the pharmacy sales prices, not the profit margins for wholesalers, were available in Taiwan.Foreign drug prices referenced are not adopted as-is but serve as meaningful reference values based on which reasonable drug prices can be decided.Therefore, reference values should be precise and transparent, and from such a perspective, EFP may be more suitable as data resources than pharmacy purchase prices.Furthermore, consideration should be given to adding Canada and Australia to the current list of seven reference countries, and information on the drug pricing structure of other countries should be updated periodically.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".