Assessing Pharmacy Costs of Intravenous Push Controlled Substance Waste in Hospital-Based Areas: A Multi-Site Study
Bibliographic record
Abstract
Intravenous push (IVP) administration of controlled substances in hospital settings presents operational challenges related to medication waste, documentation, and diversion risk. This multi-site observational study aimed to quantify the pharmacy workforce time and associated costs linked to IVP waste management across a 16-hospital health system in Southwest Florida. Data were collected from over 4400 controlled substance transactions involving fentanyl, midazolam, hydromorphone, morphine, ketamine, and lorazepam. Methods included automated transaction analysis, manual chart reviews, and software-based compliance case evaluations. Results indicated patterns of partial dose waste, particularly for midazolam (85.2%) and hydromorphone (78.8%), and identified opportunities where documentation efforts could be further optimized through automation. Manual review of 333 incidents required an average of 6 min and 43 s per case, extrapolating to over 496 h of quarterly pharmacy labor or nearly 1985 h annually. Software-based case reviews added another 32 h per quarter or 130 h annually. Additionally, waste receptacle systems incurred over USD 1.1 million in capital costs and USD 322,500 in annual maintenance, with technician labor contributing further operational burden. These findings underscore the resource demands of IVP waste management and support the need for standardized dosing, enhanced documentation workflows, and pharmacy-led interventions to improve efficiency and reduce diversion 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".