Assessing the performance of health technology assessment (HTA) agencies: developing a multi-country, multi-stakeholder, and multi-dimensional framework to explore mechanisms of impact
Bibliographic record
Abstract
BACKGROUND: Health technology assessment (HTA) agencies have an important role to play in managing the rising demands on health systems. However, creating and running such agencies potentially diverts resources from frontline services. A large number of studies address the question of 'what is the impact of HTA?'. Several points of heterogeneity in this literature include: purpose of the study, definition of HTA, definition of impact, and scope and rigour of evaluations. Our study seeks to address several limitations in this literature. This study aims to explore the mechanisms of impact of an HTA agency. In doing so, we consider HTA as an institution rather than a knowledge product to build an impact evaluation framework from an international, multi-stakeholder and multi-dimensional perspective. METHODS: We conducted 9 key informant interviews with experts from the international HTA community. We addressed several questions, informed by existing frameworks of impact within the literature, to understand their perspectives on the mechanisms of impact of an HTA agency. We analyse data using logic modelling and impact mapping, as tools to understand and visualise mechanisms of change. FINDINGS: Our impact mapping highlights several distinct, but not necessarily mutually exclusive, mechanisms through which the overall impact of an HTA agency is achieved. These are: the effective conduct of HTA studies; effective use of HTA in agenda-setting and policy formulation processes; effective engagement and external communications; good institutional reputation and fit within the healthcare and policy-making system; effective use of HTA as a tool for the negotiation of health technology prices; and the effective implementation of policy change regarding health technologies. We also identify indicators of these effects. CONCLUSIONS: Our findings and resulting evaluation framework complement and add to existing literature by offering a new perspective on the mechanisms by which HTA agencies generate impact. This new perspective considers HTA as an institution rather than a knowledge product, is international, multi-dimensional, and includes multi-stakeholder views. We hope the analysis will be useful to countries interested in managing HTA performance.
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 | 0.216 | 0.190 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.028 | 0.021 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.036 | 0.033 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".