Potential effects of rational prescribing on national health care spending: More than half a billion dollars in annual savings.
Bibliographic record
Abstract
OBJECTIVE: To estimate the cost savings that could result from implementation of a rational prescribing model for drug classes that are equivalent in terms of efficacy, toxicity, and convenience. DESIGN: The top 10 drug classes based on annual spending were gathered from the Canadian Institute for Health Information. They were reviewed for potential inclusion in the study based on the ability to compare intraclass medications. When equivalence in efficacy, toxicity, and convenience was determined from a literature review, annual prescribing data were gathered from the National Prescription Drug Utilization Information Systems Database. The potential cost savings were then calculated by comparing current market shares with potential future market shares. SETTING: Canada. MAIN OUTCOME MEASURES: Estimated differences in spending produced by a rational prescribing model. RESULTS: Statins, proton pump inhibitors, angiotensin-converting enzyme inhibitors, and selective serotonin reuptake inhibitors were determined to have class equivalence for efficacy, toxicity, and convenience. Total current annual spending on these classes is $856 million through public drug programs, and an estimated $1.97 billion nationally. Through rational prescribing, annual savings could reach $222 million for public drug programs, and $521 million nationally. CONCLUSION: Most of the potential savings are derived from deprescribing the newest patent-protected medications in each class. Avoiding prescribing the newest intraclass drug, particularly in the absence of research to support its superiority in relevant clinical outcomes, could lead to considerable savings in health care expenditures and might push the pharmaceutical industry to innovate rather than imitate.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".