Emerging Use of Early Health Technology Assessment in Medical Product Development: A Scoping Review of the Literature
Bibliographic record
Abstract
Early health technology assessment is increasingly being used to support health economic evidence development during early stages of clinical research. Such early models can be used to inform research and development about the design and management of new medical technologies to mitigate the risks, perceived by industry and the public sector, associated with market access and reimbursement. Over the past 25 years it has been suggested that health economic evaluation in the early stages may benefit the development and diffusion of medical products. Early health technology assessment has been suggested in the context of iterative economic evaluation alongside phase I and II clinical research to inform clinical trial design, market access, and pricing. In addition, performing early health technology assessment was also proposed at an even earlier stage for managing technology portfolios. This scoping review suggests a generally accepted definition of early health technology assessment to be "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". The present review also aimed to identify recent published empirical studies employing an early-stage assessment of a medical product. With most included studies carried out to support a market launch, the dominant methodology was early health economic modeling. Further methodological development is required, in particular, by combining systems engineering and health economics to manage uncertainty in medical product portfolios.
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 | 0.030 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| 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".