A systematic review on current status of health technology reassessment: insights for South Korea
Bibliographic record
Abstract
BACKGROUND: To systematically investigate the current status and methodology of health technology reassessment (HTR) in various countries to draw insights for the healthcare system in South Korea. METHODS: A systematic literature search was conducted on the articles published between January 2000 and February 2015 on Medline, EMBASE, the Cochrane Library, CINAHL, and PubMed. The titles and abstracts of retrieved records were screened and selected by two independent reviewers. Data related to HTR were extracted using a pre-standardised form. The review was conducted using narrative synthesis to understand and summarise the HTR process and policies. RESULTS: Forty five studies, conducted in seven countries, including the United Kingdom, Australia, Canada, Spain, Sweden, Denmark, and the United States of America, fulfilled the inclusion criteria. Informed by the literature review, and complemented by informant interviews, we focused on HTR activities in four jurisdictions: the United Kingdom, Canada, Australia, and Spain. There were similarities in the HTR processes, namely the use of existing health technology assessment agencies, reassessment candidate technology identification and priority setting, stakeholder involvement, support for reimbursement coverage, and implementation strategies. Considering the findings of the systematic review in the context of the domestic healthcare environment in Korea, an appropriate HTR model was developed. This model included four stages, those of identification, prioritisation, reassessment and decision. CONCLUSIONS: Disinvestment and reinvestment through the HTR was used to increase the efficiency and quality of care to help patients receive optimal treatment. Based on the lessons learnt from other countries' experiences, Korea should make efforts to establish an HTR process that optimises the National Healthcare Insurance system through revision of the existing Medical Service Act.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.064 | 0.182 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".