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Record W4312491467 · doi:10.52937/hira.22.2.2.e8

A Review of Foreign Pharmaceutical Pricing Structure: Focusing on a Drug Price Formula Based on Ex-factory Prices

2022· review· en· W4312491467 on OpenAlexaboutno aff
Jin Han Ju, Byungsoo Kim, Sang‐Heon Yoon

Bibliographic record

VenueHealth Insurance Review & Assessment Service Research · 2022
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsFactory (object-oriented programming)Drug pricesBusinessEconomicsComputer scienceMonetary economics

Abstract

fetched live from OpenAlex

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specific formula currently used for the conversion of drug prices because the formula used to obtain the percentages is outdated and there is insufficient evidence on how the percentages were derived.In this study, the characteristics of 10 countries that have implemented external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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.014
Science and technology studies0.0000.001
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.367
GPT teacher head0.525
Teacher spread0.158 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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