PP50 Facilitating Academic Life Science Innovation With Early Health Technology Assessment: A Survey Of Potential User Needs And Perceptions
Bibliographic record
Abstract
Introduction Academic life scientists often struggle to develop and commercialize concrete medical products based on their discoveries. Early health technology assessment (eHTA) can help innovators to define target product profiles (TPPs) with strong value propositions. To understand how eHTA can best help facilitate the clinical translation of university-based inventions, we conducted a survey of stakeholders in the life science innovation ecosystem. Methods Our 10-minute online survey includes questions on respondents’ location, organizational affiliations, experiences in health technology development, and awareness and perceptions of eHTA. eHTA is broadly defined as the use of tools from health economics, epidemiology, management, and related disciplines to assess the potential value of a medical product candidate for patients, payers, providers, manufacturers, and other stakeholders. The survey is being advertised using social media and email, and it will be followed up with semistructured interviews. Data on 51 complete responses were summarized using frequency tables and cross-tabulations, and the statistical significance of subgroup differences was evaluated using Fisher’s exact test. Results Of 51 respondents, a majority lived in Canada (38/51; 75%) and had an academic affiliation (39/51; 76%). A “lack of commercialization skills among academic life science teams” was identified as a barrier to clinical translation by 41 percent (21/51), though this varied by academic affiliation (33% vs 67%; p=0.051) and industry experience (65% vs 29%; p=0.033). While 31 percent (16/51) reported familiarity with eHTA, this also varied by academic affiliation (23% vs 58%; p=0.033). Only 20 percent (10/51) had previously used eHTA, but a majority expressed an interest in learning more (39/51; 76%) and in using eHTA in the future (31/51; 61%). Conclusions Making eHTA more accessible for academic life scientists who lack commercialization experience may mitigate an important barrier to clinical translation of university-developed health technologies. While awareness of eHTA is relatively low in this group, they are interested in learning more about and using eHTA, and efforts should be made to integrate eHTA with existing product development tools like the TPP.
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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".