Surgical instrumentation: the true cost of instrument trays and a potential strategy for optimization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it