CHANGING HEALTH TECHNOLOGY ASSESSMENT PARADIGMS?
Bibliographic record
Abstract
OBJECTIVES: Health technology assessment (HTA) has to innovate to best support changing health system environments and to help provide access to valuable innovation under fiscal constraint. METHODS: Issues associated with changing HTA paradigms were identified through scoping and explored through deliberation at a meeting of industry and HTA leaders. RESULTS: Five broad areas of change (engagement, scientific dialogue, research prioritization, adaptive approaches, and real world data) were identified. The meeting focused on two themes derived from these: re-thinking scientific dialogue and multi-stakeholder engagement, and re-thinking value, affordability, and access. Earlier and ongoing engagement to steer the innovation process and help achieve appropriate use across the technology lifecycle was perceived as important but would be resource intensive and would require priority setting. Patients need to be involved throughout, and particularly at the early stages. Further discussion is needed on the type of body best suited to convening the dialogue required. There was agreement that HTA must continue to assess value, but views differed on the role that HTA should play in assessing affordability and on appropriate responses to challenges around affordability. Enhanced horizon scanning could play an important role in preparing for significant future investments. CONCLUSIONS: Early and ongoing multi-stakeholder engagement and revisiting approaches to valuing innovation are required. Questions remain as to the most appropriate role for HTA bodies. Changing HTA paradigms extend HTA's traditional remit of being responsive to decision-makers demands to being more proactive and considering whole system value.
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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 | 0.191 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.026 | 0.044 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.013 | 0.027 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".