Impact of technology-assisted versus manual sterile compounding on safety and efficiency in a Canadian community hospital
Bibliographic record
Abstract
PURPOSE: Interventions to improve the safety and efficiency of manual sterile compounding are needed. This study evaluated the impact of a technology-assisted workflow system (TAWS) on sterile compounding safety (checks, traceability, and error detection), and efficiency (task time). METHODS: Observations were conducted in an oncology pharmacy transitioning from a manual to a TAWS process for sterile compounding. Process maps were generated to compare manual and TAWS checks and traceability. The numbers and types of errors detected were collected, and task times were observed directly or via TAWS data logs. RESULTS: Analysis of safety outcomes showed that, depending on preparation type, 3 to 4 product checks occurred in the manual process, compared to 6 to 10 checks with TAWS use. TAWS checks (barcoding and gravimetric verification) produced better traceability (documentation). The rate of incorrect-drug errors decreased with technology-assisted compounding (from 0.4% [5 of 1,350 preparations] with the manual process to 0% [0 of 1,565 preparations] with TAWS use; P < 0.02). The TAWS increased detection of (1) errors in the amount of drug withdrawn from vials (manual vs TAWS, 0.4% [5/1,350] vs 1.2% [18/1565]; P < 0.02), and (2) errors in the amount of drug injected into the final container (manual vs TAWS, 0% [0/1,236] vs 0.9% [11/1,272]; P < 0.002). With regard to efficiency outcomes, TAWS use increased the mean mixing time (manual vs TAWS, 275 seconds vs 355 seconds; P < 0.001), had no significant impact on average visual checking time (manual vs TAWS, 21.4 seconds vs 21.6 seconds), and decreased average physical checking time (manual vs TAWS, 58.6 seconds vs 50.9 seconds; P < 0.001). CONCLUSION: In comparison to manual sterile compounding, use of the TAWS improved safety through more frequent and rigorous checks, improved traceability (via superior documentation), and enhanced error detection. Results related to efficiency were mixed.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".