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Record W4415356846 · doi:10.1017/s026646232510319x

MAPPING current decision-making pathways and reimbursement processes for high-risk medical devices in EU/EEA member states and the UK: a scoping review

2025· review· en· W4415356846 on OpenAlexaff
Rasha A. Alshaikh, Kieran Walsh, Fatma El-Komy, Susan Spillane, Marie Carrigan, Louise Larkin, Patricia Harrington, Michelle O’Neill, Conor Teljeur, Máirín Ryan, Caitríona M. O’Driscoll

Bibliographic record

VenueInternational Journal of Technology Assessment in Health Care · 2025
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsTrinity College
FundersHelse Sør-Øst RHFHelse VestHelse Midt-NorgeUniversity College CorkHelse Nord RHFUniversity of Cambridge
KeywordsReimbursementHealth technologyMember statesMEDLINECurrent (fluid)

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.084
metaresearch head score (Gemma)0.237
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.084
Threshold uncertainty score0.445

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0840.237
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0210.020
Science and technology studies0.0020.003
Scholarly communication0.0100.008
Open science0.0030.005
Research integrity0.0050.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.195
GPT teacher head0.541
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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