HTA responsiveness to today’s challenges to health systems: a responsible innovation in health perspective
Bibliographic record
Abstract
Abstract Introduction Though Health Technology Assessment (HTA) has steadily grown over the past decades, less attention has been paid to the way HTA may prove more responsive to the broader economic, social, and environmental challenges that health systems are facing today. In view of climate change, chronic diseases, an aging population, inequalities, and workforce issues, the HTA community’s unique set of skills nonetheless holds great potential to help decision-makers strengthen many publicly funded health systems around the world. Methods This article adopts an integrated system-wide perspective guided by the Responsible Innovation in Health (RIH) framework to explore how the HTA community may not only adapt to the speed of innovation but also consider its direction. Results Because RIH aims to steer innovation toward a more sustainable pathway, it can help HTA agencies anticipate decision-makers’ informational needs regarding four systemic challenges: (1) equitable access; (2) workforce issues; (3) accountable policy trade-offs; and (4) environmental sustainability. We clarify how key elements of the RIH framework may be used by HTA agencies to: (1) supplement their evaluation process; (2) align their priority-setting or strategic planning activities with their health system challenges; or (3) inform the production of early HTAs, horizon scans, or reports that are broader in scope than a single technology review. Conclusions The article concludes with three practical implications that were identified by the Institut National d’Excellence en Santé et Services Sociaux (INESSS) (Québec, Canada) and may inspire other HTA agencies.
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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 | 0.089 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.053 |
| Scholarly communication | 0.026 | 0.017 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".