Targeting improved patient outcomes using innovative product listing agreements: a survey of Canadian and international key opinion leaders
Bibliographic record
Abstract
OBJECTIVES: To address the uncertainty associated with procuring pharmaceutical products, product listing agreements (PLAs) are increasingly being used to support responsible funding decisions in Canada and elsewhere. These agreements typically involve financial-based rebating initiatives or, less frequently, outcome-based contracts. A qualitative survey was conducted to improve the understanding of outcome-based and more innovative PLAs (IPLAs) based on input from Canadian and international key opinion leaders in the areas of drug manufacturing and reimbursement. METHODS: Results from a structured literature review were used to inform survey development. Potential participants were invited via email to partake in the survey, which was conducted over phone or in person. Responses were compiled anonymously for review and reporting. RESULTS: Twenty-one individuals participated in the survey, including health technology assessment (HTA) key opinion leaders (38%), pharmaceutical industry chief executive officers/vice presidents (29%), ex-payers (19%), and current payers/drug plan managers/HTA (14%). The participants suggested that ~80%-95% of Canadian PLAs are financial-based rather than outcomes-based. They indicated that IPLAs offer important benefits to patients, payers, and manufacturers; however, several challenges limit their use (eg, administrative burden, lack of agreed-upon endpoint). They noted that IPLAs are useful in rapidly evolving therapeutic areas and those associated with high unmet need, a quantifiable endpoint, and/or robust data systems. The Canadian Agency for Drugs and Technologies in Health, the pan-Canadian Pharmaceutical Alliance, and other arms-length organizations could play important roles in identifying uncertainty and endpoints and brokering pan-Canadian PLAs. Industry should work collaboratively with payers to identify uncertainty and develop innovative mechanisms to address it. CONCLUSION: The survey results indicated that while challenging, use of IPLAs may be associated with various benefits. Collaboration among stakeholders remains key: Canadian agencies could play an important role in the success of these agreements, while industry should be proactive in offering solutions that will help improve outcomes across the entire health care system.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".