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Record W4416292535 · doi:10.1097/jhq.0000000000000511

Standardizing Inventory Reduces Reprocessing Time and Costs Through Worker Task Familiarity in Medical Devices

2025· article· en· W4416292535 on OpenAlexaboutno aff
Gurjovan Sahi, Jin Du, Aazad Abbas, Ajay Shah, Johnathan R. Lex, Albert Yee, Jérémie Larouche, Jay Toor

Bibliographic record

VenueJournal for Healthcare Quality · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMedical Device Sterilization and Disinfection
Canadian institutionsnot available
Fundersnot available
KeywordsTask (project management)MEDLINETask analysisPatient safety

Abstract

fetched live from OpenAlex

BACKGROUND: Surgical instrument inventory optimization leads to sizable cost savings through tray reduction. Yet, a commonly overlooked benefit is the increase in efficiency stemming from reduced task variety for health care workers resulting from this reduction in inventory variety. We hypothesized that reducing the variety of surgical instrument trays would lead to significant improvement in reprocessing time, labor cost savings, and staff satisfaction. METHODS: We conducted a 12-month observational study at an academic hospital's medical device reprocessing (MDR) department before and after inventory optimization. The evaluated outcome measures were MDR time saved, labor cost reduction, and worker satisfaction as measured by an anonymized survey. RESULTS: After standardization, the results revealed that the time savings of new MDR technicians (14 ± 6.2 minutes) were significantly higher than the time savings of experienced MDR technicians (4.6 ± 5.7 minutes) ( p < .001). The total reprocessing cost savings equal $2,575.96 Canadian Dollars (CAD) annually. We found a higher satisfaction with the standardized tray among MDR technicians, with eight of nine new MDR technicians (89%) significantly preferring it, and 12 of 19 (63%) experienced MDR technicians "somewhat" preferring the standardized tray. CONCLUSION: Standardizing surgical trays enhances efficiency, reduces costs, and improves staff satisfaction, making it a valuable strategy in inventory management.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.691
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.045
GPT teacher head0.427
Teacher spread0.382 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations0
Published2025
Admission routes1
Has abstractyes

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