Assessment procedures as basis of reimbursement decisions for medical devices- an international overview
Bibliographic record
Abstract
Background: Worldwide, various cost and benefit assessment programs for medical devices with different methodology are performed. The aim of this study is highlighting characteristics of some selected countries concerning this matter. Methods: A systematic literature search in Medline, Embase, the Cochrane Library and INAHTA-database was conducted. Additionally, hand searches on websites of country-specific healthcare facilities and single HTA organizations were performed. For the international overview, the countries Australia, the United Kingdom, Canada and the United States of America were selected. Expert consultations were conducted, using a standard questionnaire, supplemented by country-specific questions. Results: Various assessment procedures for medical devices were identified and differences in the influence of cost and benefit assessments on reimbursement decisions were discovered. Because of the heterogeneity of healthcare systems, country-specific programs have different health policy impacts, which complicates a direct comparison. In the selected countries, the methodology for assessing an innovation, the required evidence, patient-relevant outcome parameters and reference methods are determined specifically to the product. To identify relevant medical innovations for benefit assessments, systems of Horizon Scanning are established. Pilot projects for accompanying technology assessments of innovations are promising, but currently not finalized. Conclusion: Benefit assessments and HTA of medical devices become more and more important to ensure the financial sustainability of healthcare systems. Pilot projects for accompanying technology assessment in some healthcare systems are expedited and offer the potential for an adaptation to other countries. Horizon Scanning is a useful method for the identification of promising innovations for subsequent benefit assessment. Rapid HTA can quickly answer limited research questions.
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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 | 0.061 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.029 | 0.023 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".