Optimizing the surgical instrument tray to immediately increase efficiency and lower costs in the operating room
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
BACKGROUND: Surgical trays are often poorly configured and can be ongoing sources of frustration and excess costs. We conducted an observational study to determine if the use of a customized mathematical inventory optimization model would result in a greater reduction in the number of instruments on a surgical tray than a clinician review of the tray. METHODS: Utilization of instruments on the major orthopedic tray at a large academic hospital was documented over 80 procedures. Processes in the medical device reprocessing department and operating room were observed to comprehensively quantify all associated costs. Results of the observations were applied to a customized mathematical model to determine the ideal tray configuration. For comparison, a clinician review was also performed. RESULTS: The mathematical model alone produced an ideal tray size of 47 instruments, a reduction of 41 instruments from the original size of 88 instruments (47% reduction). This represented $34 440 in annual savings. In contrast, the clinician review alone suggested an ideal tray size of 67 instruments (23% reduction), representing $17 640 in annual savings. When clinicians were provided with the additional information from the model, they reduced the tray size to 51 instruments (42% reduction), producing $31 870 in savings. The mathematical model yielded an additional 22% instrument reduction and $14 230 in savings compared with clinician review alone. CONCLUSION: Our mathematical model is generalizable and can be applied to all specialties and hospitals to determine optimal tray configuration. As such, the financial implications are broad; at our institution, application to all surgical trays would result in $205 000 of savings annually. Surgeons and managers looking to streamline surgical trays should consider this evidence-based approach.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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