Variations in the Medical Device Authorization and Reimbursement Landscape: A Case Study of 2 Cardiovascular Devices Across 4 Countries
Bibliographic record
Abstract
BACKGROUND: The authorization process and coverage/reimbursement mechanisms for medical devices play critical roles in device adoption and usage. However, international variation in these processes remains poorly characterized, especially with regard to data transparency and the effects of reimbursement on usage. METHODS: This study examined publicly available databases, governmental agency recommendations and policies, and press releases from the United States, Canada, the United Kingdom, and the Netherlands to compare the regulatory approval processes and coverage/reimbursement mechanisms for 2 novel cardiovascular devices introduced in the early and late 2000's: the Watchman left atrial appendage occlusion device and the Impella percutaneous ventricular assist device. In addition to qualitative comparisons for each country, this study compared the date of the first regulatory review, time from submission to review completion, device approval date, agency approval requirements, number of review cycles, and necessity of postapproval studies as determined by the regulator, date of funding decision, final funding decision, and requirements for device reimbursement by relevant government payors. RESULTS: Authorization data were easily accessible for the United States and Canada but extremely limited for the United Kingdom and the Netherlands. Chronologically, authorization occurred ≈10 years earlier in Europe (United Kingdom and the Netherlands) than in North America (United States and Canada) for both devices. The United States was the only country where the principal public payor (Medicare) explicitly reimbursed both procedures. The United States was similarly notable for more rapid adoption and higher utilization of both devices than the other countries, with the Watchman implanted at 3.4 devices per 100 000 adults annually and Impella used in 7 to 8 procedures per 100 000 people annually. In contrast, uptake was far lower in Canada and Europe. CONCLUSIONS: This research provides insights into how differences among countries in authorization and reimbursement mechanisms may impact the adoption and usage of medical devices, and may inform future policies on these processes.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".