MAPPING current decision-making pathways and reimbursement processes for high-risk medical devices in EU/EEA member states and the UK: a scoping review
Bibliographic record
Abstract
OBJECTIVES: The reimbursement of, and subsequent patient access to, high-risk medical devices (MD) and in vitro diagnostics (IVD) across Europe often vary. The Health Technology Assessment Regulation (HTAR) aims to standardize clinical evaluations through Joint Clinical Assessments. Still, national differences in reimbursement frameworks and evidence integration for MD/IVD may impede the realization of HTAR's expected benefits. This review aims to map existing reimbursement frameworks for high-risk MD/IVD, identify key oversight structures, and evaluate the use of comparative effectiveness and safety evidence in reimbursement decisions across the EU/EEA/UK. METHODS: A scoping review was conducted according to the registered protocol (osf.io/65bdk) and was reported following the PRISMA-ScR guidelines. Results were validated through direct engagement with national organizations. RESULTS: Reimbursement frameworks across the EU/EEA/UK for MD/IVD vary significantly. Of the thirty-four countries reviewed, twenty-three incorporate HTA for MD/IVD reimbursement decisions; of these, only eleven countries have a formal HTA process as part of reimbursement pathways. Eight countries have structured mechanisms to address safety and effectiveness evidence uncertainty. Furthermore, twelve countries have primarily centralized processes, while six rely on regional or local decision-making. CONCLUSIONS: This review highlights the variations in how countries integrate HTA into reimbursement frameworks for MD/IVD, how the national decisions are implemented, and how the evidence uncertainty is assessed. Some countries have a well-established reimbursement framework with formal HTA components, whereas others rely on ad hoc HTA processes. Understanding these differences can help optimize the use of HTAR-generated evidence. Further research is needed to capture ongoing reforms in response to the HTAR.
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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 | 0.084 | 0.237 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.002 |
| 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".