Drug Policy: Making Effective Drugs Available Without Bankrupting the Healthcare System
Bibliographic record
Abstract
To the extent possible, drug policy should be based upon good quality evidence. This must extend beyond the traditional focus on efficacy and safety in carefully selected patients, to evidence about real-world effectiveness, cost-effectiveness and safety of drugs. This paper will consider methods of improving the quality of the evidence currently available, and the implications of requiring that evidence. Historically, there has been a direct link between research evidence and policy at the level of licensing - drugs are only made available after they have been shown to be safe and efficacious in well-designed and independently assessed research studies. We propose that this reliance on evidence be logically extended to cover the formulary inclusion and post-marketing surveillance aspects of modern prescription drug policy. More specifically we propose that the decision to initially list a drug on a benefit formulary be based on evidence from relevant head-to-head comparisons and well-designed cost-effectiveness analyses. This evidence would be produced by industry in cooperation with independent peer-reviewed funding agencies. Drugs could only be added to a formulary if they met specific predetermined criteria, and drugs could be removed as superior alternatives became available. The provincial governments are monopsony buyers of medicines, and they wield the power to determine public payer "market access'for medicines. This power (within and across provinces) could be used more effectively to negotiate price in the context of reimbursement. The effect of different methods of influencing prescribing (e.g., 'limited access?) upon drug utilization and patient outcomes should be rigorously assessed, including the randomization of groups of patients or communities to different strategies. We also propose that all drugs on the formulary would be subject to a well-designed post-marketing surveillance program. This program would build on the existing passive reporting of adverse events by adding a proactive system that would systematically describe the use and impact of drugs. The notion of drug safety would be extended to include not only adverse events, but also inappropriate use of drugs that results inpatients receiving drugs that do not benefit them. Inappropriate use wastes resources and can put patients and populations at risk.
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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 | 0.100 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.020 | 0.033 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.030 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".