The value and impact of health technology assessment: discussions and recommendations from the 2023 Health Technology Assessment International Global Policy Forum
Bibliographic record
Abstract
Health technology assessment (HTA) programs inform decision making about the value and reimbursement of new and existing health technologies; however, they are under increasing pressure to demonstrate that they are a cost-effective use of finite healthcare resources themselves. The 2023 HTAi Global Policy Forum (GPF) discussed the value and impact of HTA, including how it is assessed and communicated, and how it could be enhanced in the future. This article summarizes the discussions held at the 2023 HTAi GPF, where the challenges and opportunities related to the value and impact of HTA were debated. Core themes and recommendations identified that defining the purpose of value and impact assessment is an essential first step prior to undertaking it, and that it can be done through the use and expansion of existing tools. Further work around aligning HTA programs with underlying societal values is needed to ensure the long-term value and impact of HTA. HTA could also have a role in assessing the efficiency of the wider health system by applying HTA methods or concepts to broader budgetary allocations and organizational aspects of health care. Stakeholders (particularly patients, industry, and clinicians but also payers, wider society, and the media) should ideally be actively engaged when undertaking the value and impact assessment of HTA. More concerted efforts in communicating the role and remit of HTA bodies would also help stakeholders to better understand the value and impact of HTA, which in turn could improve the implementation of HTA recommendations and application to future actions in the lifecycle of technologies.
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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 | 0.197 | 0.156 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.028 | 0.039 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.068 | 0.059 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".