Demonstrating the influence of HTA: INAHTA member stories of HTA impact
Bibliographic record
Abstract
A central function of health technology assessment (HTA) agencies is the production of HTA reports to support evidence-informed policy and decision making. HTA agencies are interested in understanding the mechanisms of HTA impact, which can be understood as the influence or impact of HTA report findings on decision making at various levels of the health system. The members of the International Network of Agencies for HTA (INAHTA) meet at their annual Congress where impact story sharing is one important activity. This paper summarizes four stories of HTA impact that were finalists for the David Hailey Award for Best Impact Story.The methods to measure impact include: document review; claims analysis and review of reimbursement status; citation analysis; qualitative evaluation of stakeholders' views; and review of media response. HTA agency staff also observed changes in government activities and priorities based on the HTA. Impact assessment can provide information to improve the HTA process, for example, the value of patient and clinician engagement in the HTA process to better define the assessment question and literature reviews in a more holistic and balanced way.HTA reports produced by publicly funded HTA agencies are valued by health systems around the globe as they support decision making regarding the appropriate use, pricing, reimbursement, and disinvestment of health technologies. HTAs can also have a positive impact on information sharing between different levels of government and across stakeholder groups. These stories show how HTA can have a significant impact, irrespective of the health system and health technology being assessed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.090 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.022 | 0.019 |
| Scholarly communication | 0.027 | 0.021 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.008 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 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".