Can multi-criteria decision analysis (MCDA) be implemented into real-world drug decision-making processes? A Canadian provincial experience.
Bibliographic record
Abstract
OBJECTIVE:To describe the implementation of multi-criteria decision analysis (MCDA) into a Canadian public drug reimbursement decision-making process, identifying the aspects of the MCDA approach, and the context that promoted uptake. METHODS:Narrative summary of case study describing the how, when, and why of implementing MCDA. RESULTS:Faced with a fixed budget, a pipeline of expensive but potentially valuable drugs, and potential delays to drug decision making, the Ministry of Health (i.e., decision makers) and its independent expert advisory committee (IAB) sought alternative values-based decision processes. MCDA was considered highly compatible with current processes, but the ability as a stand-alone intervention to address issues of opportunity cost was unclear. The IAB nevertheless collaboratively voted to implement an externally developed MCDA with support from decision makers. After several months of engagement and piloting, implementation was rapid and leveraged strong pre-existing formal and informal communication networks. The IAB as a whole rates new submissions which serves as an input into the deliberative process. CONCLUSIONS:MCDA can be a highly adaptable approach that can be implemented into a functioning drug reimbursement setting when facilitated by (i) a truly limited budget; (ii) a shared vision for change by end-users and decision makers; (iii) using pre-existing deliberative processes; and (iv) viewing the approach as a decision framework rather than the decision (when appropriate). Given the current limitations of MCDA, implementing an academically imperfect tool first and evaluating later reflects a practical solution to real-time fiscal constraints and impending delays to drug approvals that may be faced by decision makers.
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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 | 0.049 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".