Use of Real-World Data Sources for Canadian Drug Pricing and Reimbursement Decisions: Stakeholder Views and Lessons for Other Countries
Bibliographic record
Abstract
BACKGROUND: Canada has a long history of the use of clinical evidence to support healthcare decision making. Given improvements in data holdings and analytic capacity in Canada and stakeholder interest, the purpose of this study is to reflect on perceptions of the value of real-world evidence in pricing and reimbursement decisions, barriers to its optimal use in pricing and reimbursement, current initiatives that may lead to its increased use, and what role the pharmaceutical industry may play in this.Methods/ResultsTo capture stakeholder perceptions, ninety-one participants identified as key stakeholders were identified according to background roles and geography and invited to participate in four round table discussions conducted under Chatham House rule. Important themes emerging from these discussions included: (i) the need to understand what "real world" evidence means; (ii) barriers to using real world evidence from differences in access, governance, inter-operability, system structures, expertise, and quality across Canadian health systems; (iii) differing views on industry's role. CONCLUSIONS: The use of real-world data in Canada to inform pricing and reimbursement decisions is far from routine but nascent and slowly increasing. Barriers, including interoperability concerns, may also apply to other federated health systems that need to focus on the networking of healthcare administrative data across provincial jurisdictional boundaries. There also appears to be a desire to see better use of pragmatic trials linked to these administrative data sets. Emerging initiatives are under way to use real world evidence more broadly, and include identification of common data elements and approaches to networking data.
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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 | 0.107 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".