Financial Impact of Cancer Drug Wastage and Potential Cost Savings From Mitigation Strategies
Bibliographic record
Abstract
PURPOSE: Cancer drug wastage occurs when a parenteral drug within a fixed vial is not administered fully to a patient. This study investigated the extent of drug wastage, the financial impact on the hospital budget, and the cost savings associated with current mitigation strategies. METHODS: We conducted a cross-sectional study in three University of Toronto-affiliated hospitals of various sizes. We recorded the actual amount of drug wasted over a 2-week period while using current mitigation strategies. Single-dose vial cancer drugs with the highest wastage potentials were identified (14 drugs). To calculate the hypothetical drug wastage with no mitigation strategies, we determined how many vials of drugs would be needed to fill a single prescription. RESULTS: The total drug costs over the 2 weeks ranged from $50,257 to $716,983 in the three institutions. With existing mitigation strategies, the actual drug wastage over the 2 weeks ranged from $928 to $5,472, which was approximately 1% to 2% of the total drug costs. In the hypothetical model with no mitigation strategies implemented, the projected drug cost wastage would have been $11,232 to $149,131, which accounted for 16% to 18% of the total drug costs. As a result, the potential annual savings while using current mitigation strategies range from 15% to 17%. CONCLUSION: The financial impact of drug wastage is substantial. Mitigation strategies lead to substantial cost savings, with the opportunity to reinvest those savings. More research is needed to determine the appropriate methods to minimize risk to patients while using the cost-saving mitigation strategies.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".