A Methodological Approach for Measuring the Impact of HTA
Bibliographic record
Abstract
There is a lack of evidence concerning the link between HTA and outcomes in terms of health improvements. This work proposes a framework for assessing the impact of HTA. This impact assessment is a necessary step in then better understanding the value for money of HTA bodies. We emphasis that this is still a work in progress. iDSI has developed a theory of change-based framework in order to evaluate the impact the iDSI has on institutional strengthening – leading to ‘better decisions’ for ‘better health’. This framework recognises that there is a complex translation process between better decisions and better health dependent on many assumptions about local factors and systems, including linkage between decisions and budgets, delivery, implementation, and data accuracy. Work has been undertaken over the last 6 months developing a methodological approach for measuring the impact of health technology assessment (HTA). Two case studies are used to illustrate the approach. At the core of impact assessment is a requirement to link causes and effects, to explain ‘how’ and ‘why’ and to identify – and thus improve or adapt – mechanisms leading to impact. Policy makers also want to know ‘to what extent’ or ‘the magnitude of impact’. The framework developed adopts an economic approach nested in theory of change as a means of both quantifying the magnitude of impact (utilising economic models) as well as explaining why and how impact happens (drawing on theory based approaches) in order to reinforce learning as to how to improve our response and optimise the use of HTA to have the greatest impact in a given context. This should also enable us to capture and explain wider impact – perhaps more intangible aspects which cannot be easily quantified. This may also possibly increase policy-makers’ ‘buy-in’.
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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 | 0.278 | 0.367 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".