OD01 Delays In Funded Access To Medicines: A Global Perspective
Bibliographic record
Abstract
Introduction There are significant delays in the funded access to medicines. Studies indicate that in many countries it takes more than a year for patients to have funded access to medicines after market authorization. This study aimed to understand the disparities in timelines for funded access to medicines across different countries and to identify underlying reasons for this access gap. Methods We conducted a scoping review to examine the nature of health technology assessment (HTA) processes, current methods, and policies for medicines in ten jurisdictions. The jurisdictions included in this study are Australia, Canada, France, Germany, South Korea, the Netherlands, United Kingdom (divided into England, Scotland and Wales), and United States of America. The information was extracted from the websites of International Network of Agencies for Health Technology Assessment (INAHTA) member agencies in the selected jurisdictions, grey literature from governments’ websites, and peer-reviewed literature. Results Overall median time from submission of the evidence dossier to HTA recommendations for most jurisdictions is 22 weeks. Although there are similarities in the time taken to reach a funding decision, there are considerable variations in the time taken for patients to have funded access to medicines after HTA recommendations. Only a few countries mentioned a specific timeline within which medicines approved for funding should be listed. Time taken for price negotiations and other arrangements (i.e., risk-sharing agreements) may contribute to varying timelines for listing medicines for funding. Mostly, such negotiations are confidential and may not be time limited. Conclusions There was surprising consistency, globally, in the time it takes for funding decisions after medicines registration. The causes of delays in the medicines’ listing decisions are multifactorial and mostly occur after HTA recommendations. The parallel regulatory-assessment process and prioritization tend to reduce the time to a funding decision. However, transparency is needed in the listing process to improve overall timeliness.
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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 | 0.021 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".