Navigating change: a comparative analysis of health technology assessment reforms across agencies – processes, drivers, and interdependencies
Bibliographic record
Abstract
OBJECTIVES: Health technology assessment (HTA) is a critical part of healthcare decision making in many countries. Changes in Methods and Processes (M&P) of HTA agencies can affect the time and degree of patient access to treatments. Published literature focuses on the different M&P adopted by HTA agencies, rather than on how these have come about over time. Our study investigates key HTA reforms and explores their drivers and interdependencies in a set of HTA agencies in Europe, Asia-Pacific, and North America. METHODS: We conducted a targeted literature review on M&P guidelines and subsequent changes to those, for 14 HTA agencies. We supplemented and validated initial findings with 29 semi-structured interviews with country-specific experts. We used analytical tools to create process maps, proactivity and influence networks, and clusters of HTA agencies. RESULTS: We found that processes leading to M&P reforms follow similar steps across HTA agencies. The three most important drivers to reforms were HTA practice and guidelines in other countries; the healthcare policy, legal, and political context within the agency's country; and experience of challenges in the assessment by the HTA body itself. International collaborations have the potential to accelerate the evolution of HTA systems and the implementation of reforms. CONCLUSION: We identified PBAC (Australia), CDA-AMC (Canada), NICE (England), IQWiG (Germany), and ZIN (the Netherlands) as HTA agencies that are catalysts of HTA reforms as well as internationally influential. International collaborations may represent a useful route to accelerate changes as long as they ensure wide stakeholder engagement at an early stage.
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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 | 0.057 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 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".