Similarities and Differences in Health Technology Assessment Systems and Implications for Coverage Decisions: Evidence from 32 Countries
Bibliographic record
Abstract
Health technology assessment (HTA) systems across countries vary in the way they are set up, according to their role and based on how funding decisions are reached. Our objective was to study the characteristics of these systems and their likely impact on the funding of technologies undergoing HTA. Based on a literature review, we created a conceptual framework that captures key operating features of HTA systems. We used this framework to map current HTA activities across 32 countries in the European Union, the UK, Canada and Australia. Evidence was collected through a systematic search of competent authority websites and grey literature sources. Primary data collection through expert consultation validated our findings and further complemented the analysis. Sixty-three HTA bodies were identified. Most have a national scope (76%), are independent (73%), have an advisory role (52%), evaluate pharmaceuticals predominantly or exclusively (76%), assess health technologies based on their clinical and cost-effectiveness (73%) and involve various stakeholders as members of the HTA committee (94%) and/or through external consultation (76%). The majority of HTA outcomes are not legally binding (81%). Although all study countries implement HTA, the way it fits into decision-making, negotiation processes, and coverage and funding decisions differs significantly across countries. HTA is a dynamic and transformative process and there is a need for transparency to investigate whether evidence-based information influences coverage decisions.
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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 | 0.078 | 0.311 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.029 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".