Pan-Canadian Pharmaceutical Alliance (pCPA): Timelines Analysis and Policy Implications
Bibliographic record
Abstract
This analysis follows our recent study showing that Canadian public reimbursement delays have lengthened from regulatory approval to listing decisions by public drug plans and delayed public access to innovative medicines, mainly due to processes following the Common Drug Review (CDR) and the pan-Canadian Oncology Drug Review (pCODR). Public drug plans participate in a pan-Canadian Pharmaceutical Alliance (pCPA) joint negotiation process before making decisions about whether or not to reimburse a product reviewed through CDR and pCODR. This research aims to report the findings from a comprehensive analysis of pCPA process times, times to reimbursement by public payers in Canada, and to explore the opportunities to reduce total delays in public reimbursement with a specific focus on the pCPA process. An analysis was conducted of pCPA timelines with respect to making decisions about products and indications reviewed through CDR/pCODR, and focusses on three separate time components: time to begin negotiating, time spent negotiating, and time to implement the negotiation (i.e., time to list) in each of nine jurisdictions (i.e., 10 provinces of Canada, excluding Quebec). This study demonstrates the role of post-CDR/pCODR processes in large and lengthening delays to listing new medicines. Notably, oncology products have experienced the longest increases in time to begin negotiating and to complete negotiations. Trends in listing times post-pCPA across provinces are less clear, however, it appears that consistency in terms of timelines across provinces is not happening quite so smoothly for oncology products compared to non-oncology products. Listing rates also appear to be declining for non-oncology products, although this trend is less conclusive for oncology products. Challenges need to be addressed to improve efficiency, transparency, and ultimately reduce pCPA timelines and total timelines to public reimbursement. Suggested ways to improve and streamline the listing process are: (1) transparent target timelines and associated performance incentives for the pCPA and public plan decisions, (2) parallel HTA-pCPA processes to enable pCPA negotiations to start part-way through the HTA review and allow pCPA negotiation information to be fed back into the HTA review, and (3) innovative agreements that consider patient input and earlier coverage with real-world evidence development.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".