Health technology assessment 2025 and beyond: lifecycle approaches to promote engagement and efficiency in health technology assessment
Bibliographic record
Abstract
Lifecycle considerations have always been part of health technology assessment (HTA). However, the concept of taking a fuller, more holistic "lifecycle approach" is gaining interest in the HTA community. The 2022 HTAi Global Policy Forum (GPF) discussed how adopting a lifecycle approach could promote stakeholder engagement and robust evidence generation, and whether it could enhance information sharing and transparency across stakeholder groups. This article summarizes the discussions held at the 2022 HTAi GPF and subsequent HTAi Annual Meeting panel session that debated some of the key challenges and opportunities, with particular focus on the pre- and postmarket and disinvestment phase activities. Core themes and recommendations identified that collaboration and patient involvement are happening but still needs to be strengthened, and moving to disease-based approaches may help, although individual contexts still need to be considered. Appropriately developed and mandated core outcome sets may help with information sharing and efficiency in all lifecycle activities. Further, methods for the appropriate use of big data and digital data collection should be developed and driven by the HTA community. The value of lifecycle activities should be reviewed; in particular, scientific advice appears valuable, but the magnitude of effect is somewhat unknown due to the challenges around the confidential nature of these activities. Not all lifecycle activities can be conducted for every technology, and while there is a move away from disinvestment phase activities, more structured prioritization criteria are required. This article ends with suggested next steps to bring forward some of the priority recommendations.
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 | 0.269 | 0.175 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.012 | 0.031 |
| Scholarly communication | 0.039 | 0.047 |
| Open science | 0.005 | 0.037 |
| Research integrity | 0.013 | 0.021 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".