Time from approval to reimbursement recommendations in healthcare systems with centralized HTA processes. Focus on the Polish HTA agency
Bibliographic record
Abstract
BACKGROUND: To analyze the time from drug registration to reimbursement recommendations, we examined medicinal products, including new clinical indications, registered by the EMA between 2014 and 2019 across various therapeutic areas. MATERIALS AND METHODS: The Polish Agency for Health Technology Assessment and Tariffication (AOTMiT) was compared with 11 agencies in England, Wales, Ireland, Scotland, the Netherlands, Norway, France, Germany, New Zealand, Canada, Australia. A total of 1,942 recommendations published by 12 HTA agencies were analyzed. RESULTS: The time from registration to recommendation in Poland was statistically significantly longer than for the other countries. The analysis revealed noticeable differences in the time it takes from drug registration to recommendation across the countries included in this analysis. Analyzing trends from 2014 to 2019 across individual countries, there appears to be a slight tendency toward a decrease in the median time from registration to recommendation in many agencies. CONCLUSIONS: This may suggest improvements in the processes of the recommending authorities and the companies responsible for providing data for assessment. Despite Poland having one of the longest times from registration to recommendation among the countries analyzed, there has been a clear year-over-year decrease in the time to publication of reimbursement recommendations.
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 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 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".