Evidence requirements for the authorization and reimbursement of high-risk medical devices in the USA, Europe, Australia and Canada: An analysis of seven high-risk medical devices
Bibliographic record
Abstract
In the last decade, public awareness of unsafe and ineffective high-risk devices entering the market has been raised. Consequently, evidence requirements for the market authorisation process of medical devices may not be enough to ensure high-quality and safe provision of care. This report first explores the authorisation systems for high-risk medical devices in four selected regions (USA, Canada, Australia and Europe). Secondly, it analyses the clinical evidence accessible at the time of both market approval and assessment (HTA) for the reimbursement of seven selected high-risk medical devices. Methods: A literature search in PubMed, complimented by a worldwide web-search, was conducted for authorisation systems and their evidence requirements in the four selected regions, with a focus on seven exemplary high-risk devices. Results: All seven medical devices have been approved in the European Union through an appointed Notified Body, only four by the Australian Therapeutic Goods Administration (TGA), one each by the US-American Food and Drug Administration (FDA) and the Canadian Therapeutics Products Directorate (TPD) respectively. In comparison to the other three regulatory systems, the number of approved devices in Europe is high, especially when taking into additional consideration that four further devices were also assessed by the FDA, but were either rejected or not approved for general use. In almost all of the seven analysed examples, the pre-market approval in Europe was granted 2-5 years before authorisation in other systems. The evidence used for CE-marking is not known due to its highly decentralised authorisation system and the lack of transparency. Since authorisation in Europe is granted earlier, the clinical evidence is naturally less mature. In contrast, none of the seven medical devices has so far been recommended for reimbursement. The pre-reimbursement assessments most often state that current evidence is not enough to ensure patient benefit and safety. Some devices are recommended for research only. Discussion and conclusion: The results support the call for a change in the European authorisation system towards a transparent and evidence-based regulation process. Conditional coverage or coverage under evidence development is applied as an instrument to close the gap between immature data and reimbursement requirements.
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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 | 0.012 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".