Surgical instrumentation: the true cost of instrument trays and a potential strategy for optimization
Bibliographic record
Abstract
Objective: Operating rooms (OR) generate a large portion of hospital revenue and waste. Consequently, improving efficiency and reducing waste is a high priority. Our objective was to quantify waste associated with opened but unused instruments from trays and to compare this with the cost of individually wrapping instruments.Methods: Data was collected from June to November of 2013 in a 550-bed hospital in the United States. We recorded the instrument usage of two commonly-used trays for ten cases each. The time to decontaminate and reassemble instrument trays and peel packs was measured, and the cost to reprocess one instrument was calculated.Results: Average utilization was 14% for the Plastic Soft Tissue Tray and 29% for the Major Laparotomy Tray. Of 98 instruments in the Plastics tray (n = 10), 0% was used in all cases observed and 59% were used in no observed cases. Of 110 instruments in the Major Tray (n = 10), 0% was used in all cases observed and 25% were used in no observed cases. Average cost to reprocess one instrument was $0.34-$0.47 in a tray and $0.81-$0.84 in a peel pack, or individually-wrapped instrument.Conclusions: We estimate that the cost of peel packing an instrument is roughly two times the cost of tray packing. Therefore, it becomes more cost effective from a processing standpoint to package an instrument in a peel pack when there is less than a 42%-56% probability of use depending on instrument type. This study demonstrates an opportunity for reorganization of instrument delivery that could result in a significant cost-savings and waste reduction.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".