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Record W2414147142 · doi:10.5124/jkma.2014.57.11.906

The current status and future direction of Korean health technology assessment system

2014· article· en· W2414147142 on OpenAlexaboutno aff
Min Young Lee, Jeonghoon Ahn

Bibliographic record

VenueJournal of Korean Medical Association · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsHealth technologyBusinessGatekeepingHealth carePaymentAgency (philosophy)MedicaidHealth policyMedicineEconomic growthPublic healthFinanceNursingEconomics

Abstract

fetched live from OpenAlex

신의료기술평가제도가 도입되기 전 의료기술의 안전성• 유효성은 2000년 7월 제정•고시된 미결정행위등의결정및 조정기준에 의해 의료기관 관련단체나 중앙의료심사조정위 원회의 의견으로 확인하였다.당시에는 의료기술을 과학적Health technology assessment was first introduced to the Republic of Korea in 2006 by amending the Medical Services Act.The Committee of New Health Technology Assessment (CNHTA) is the ministerial committee that has the responsibility of reviewing the safety and effectiveness of new health technology.CNHTA review plays a gatekeeping role for new health technology in Korea, which can increase the burden on patients in Korea, either by out-of pocket payments or co-pays for National Health Insurance covered service.This kind of gatekeeping is a function of the healthcare system in many countries where no financial cap such as a fixed budget or diagnosis-related group payment is applied.However, it has been argued that gatekeeping works against industrial promotion policy.The one-stop service introduced in 2014 is a system similar to US parallel review between the US Food and Drug Administration and Centers for Medicare and Medicaid Services.This service provides a simultaneous process of regulatory review by the Ministry of Food and Drug Safety, identification of existing technology by the Health Insurance Review and Assessment Services, and new health technology assessment by the National Evidence-based Healthcare Collaborating Agency and the Ministry of Health and Welfare.This service is expected to reduce the total review process by 3 to12 months.A limited health technology appointment service was introduced in April 2014.This service designates orphan health technologies and health technologies for rare and incurable diseases and supports evidence development at designated hospitals.Several countries have similar systems: US Coverage with Evidence Development, Canadian Conditionally Funded Field Evaluation, UK Only in Research, and many others.The future direction of Health technology assessment should focus on the life cycle management of health technology.A consistent, continuous, and transformative mechanism to manage from the research and development of health technology to delisting obsolete technology to make room for new innovative technology is warranted.

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)0.101
metaresearch head score (Gemma)0.128
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.899
Threshold uncertainty score0.532

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1010.128
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0100.015
Science and technology studies0.0020.004
Scholarly communication0.0130.017
Open science0.0070.006
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0160.005

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 head0.071
GPT teacher head0.402
Teacher spread0.331 · 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.

Study designObservational
DomainEvaluation
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

Citations6
Published2014
Admission routes1
Has abstractyes

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