Use and impact of technology-assisted workflow (TAWF) systems for drug compounding in pharmacy practice: a scoping literature review
Bibliographic record
Abstract
Abstract Objectives The aim of this study was to review studies describing the use and the impact of technology-assisted workflow (TAWF) systems for drug compounding in hospital pharmacy. Content This is a scoping literature review. A search was conducted on studies describing or evaluating the use of TAWF published from January 1st, 2015 to July 31st, 2021. Two databases were searched (PubMed and Embase), followed by a search on Google Scholar. Summary 218 articles were screened and 17 were identified as meeting the inclusion criteria. TAWFs all included preparation assistance software (17/17), barcode reader (17/17), photo or video taking (17/17), and some included gravimetric systems (8/17), and the use of robots (2/17). A majority of the studies included used technology for parenteral preparations (15/17, one for oral preparations only (1/17), and one used technology for both types of preparations (1/17). Most of the articles selected presented drugs prepared for adults (10/17), the others presented drugs intended for children (4/17) or for a mix of adults and children (3/17). Four parameters were evaluated: error detection rate (n=15), preparation and validation time (n=7), and costs generated or saved (n=7). Ten studies evaluated the pre-post impact of implantation of a TAWF (10/17). Outlook Given the heterogeneity of the data available, the use of TAWF was associated with an increased ability to detect preparation errors, a reduction in preparation time and costs, and increased satisfaction of pharmacy technicians and pharmacists. However, better quality studies are needed to confirm the positive impacts studied.
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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 | 0.024 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".