On the integration of early health technology assessment in the innovation process: reflections from five stakeholders
Bibliographic record
Abstract
Early health technology assessment (HTA), which includes all methods used to inform industry and other stakeholders about the potential value of new medical products in development, including methods to quantify and manage uncertainty, has seen many applications in recent years. However, it is still unclear how such early value assessments can be integrated into the technology innovation process. This commentary contributes to the discussion on the purposes early HTA can serve. Similarities and differences in the perspectives of five stakeholders (i.e., the hospital, the patient, the assessor, the medical device industry, and the policy maker) on the purpose, value, and potential challenges of early HTA are described. All five stakeholders agreed that integrating early HTA in the innovation process has the possibility to shape and refine an innovation, and inform research and development decisions. The early assessment, using a variety of methodologies, can provide insights that are relevant for all stakeholders but several challenges, for example, feasibility and responsibility, need to be addressed before early HTA can become standard practice. For early evaluations to be successful, all relevant stakeholders including patients need to be involved. Also, nimble, flexible assessment methods are needed that fit the dynamics of medical technology. Best practices should be shared to optimize both the innovation process and the methods to perform an early value assessment.
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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 | 0.206 | 0.238 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.039 |
| Scholarly communication | 0.024 | 0.030 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.031 | 0.046 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".