PD81 Applying The Innovation Of Health Technology Assessment Methods Framework To Develop The European Digital Health Technology Assessment (EDiHTA) Framework
Bibliographic record
Abstract
Introduction The EDiHTA project aims to deliver an innovative health technology assessment (HTA) framework for digital health technologies (DHT) that integrates existing methods with new ones to inform decision-making across Europe at different levels. In the development phase of the EDiHTA framework, it has been essential to identify all relevant stakeholders and their roles in the innovation process, while also capturing their needs and requirements. Methods The Innovation of Health Technology Assessment Methods framework was applied to identify relevant stakeholders and their needs and requirements regarding the design of the EDiHTA framework. Once stakeholders and their roles (practitioners or beneficiaries) were identified, their needs were assessed by reviewing existing and new HTA methodologies for DHT as used in 17 countries across Europe. Future scenarios were examined and gaps to be addressed were determined. Various methods were used to elicit the views of different stakeholders (policymakers, HTA agencies and bodies, industry, healthcare professionals, patients). including literature reviews, two online surveys, semi-structured online interviews, and focus groups. Results We engaged policymakers from 15 European countries; 15 HTA agencies from nine European countries; 29 developers (startups and big companies) from 10 different European markets; 14 healthcare professionals from nine European countries plus eight from Brazil, Canada, and the USA; and 15 patient representatives from 10 European countries. The analysis highlighted the heterogeneity among European health systems in assessing, implementing, and reimbursing DHT. All stakeholder groups emphasized the need for a harmonized HTA framework to assess DHT efficiently and accurately, including elements specific to DHT, such as cybersecurity, economic and organizational impact, and patient-centered outcomes. Conclusions There is a clear need among European stakeholders for an agile, flexible, and multidimensional HTA framework that facilitates stepwise assessments across the technology life cycle and involves all relevant stakeholders in the assessment to inform decision-making. The stakeholder interaction also identified new HTA topics and domains essential for effectively addressing the unique challenges of DHT within the EDiHTA framework.
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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 | 0.088 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".