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Record W4282974731 · doi:10.1093/ajhp/zxac167

Impact of technology-assisted versus manual sterile compounding on safety and efficiency in a Canadian community hospital

2022· article· en· W4282974731 on OpenAlexaffabout
Mark Fan, Danny Yang, Becky Ng, Jocelyn Jackson, Katherine Bouris, Sharon Eng, Edith Rolko, Patricia Trbovich

Bibliographic record

VenueAmerican Journal of Health-System Pharmacy · 2022
Typearticle
Languageen
FieldHealth Professions
TopicSafe Handling of Antineoplastic Drugs
Canadian institutionsUniversity of TorontoNorth York General Hospital
Fundersnot available
KeywordsCompoundingTraceabilityComputer sciencePatient safetyTask (project management)MedicineOperations managementEngineeringSoftware engineering

Abstract

fetched live from OpenAlex

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 The TAWS increased detection of (1) errors in the amount of drug withdrawn from vials (manual vs TAWS, 0.4% [5/1,350] vs 1.2% [18/1565]; P 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.660

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0060.002
Scholarly communication0.0030.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.046
GPT teacher head0.432
Teacher spread0.386 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueAmerican Journal of Health-System PharmacySame topicSafe Handling of Antineoplastic DrugsFrench-language works237,207