Surgical Instrument Optimization to Reduce Instrument Processing and Operating Room Setup Time
Bibliographic record
Abstract
Objective As health care expenditures rise, novel ways to increase efficiency are sought. The operating room (OR) represents an area where there is opportunity to optimize work flow and supply use. Evidence suggests that instrument redundancy in the OR tends to be high and that direct cost savings can be achieved by “optimizing” surgical trays. The purpose of this study was to quantify the potential time savings associated with surgical tray optimization. Methods Instrument utilization was reviewed for 4 procedures: tonsillectomy, sinus surgery, septoplasty, and septorhinoplasty. Instruments used in <20% of cases were excluded. Data on tray assembly time in the central processing department and instrument setup time in the OR were prospectively collected over a 3‐month period before and after tray optimization. Student’s t test (α = 0.05) was used to determine whether times were significantly different following optimization. Results Tray assembly times were found to be significantly shorter following optimization, with percentage reduction in time ranging from 58% to 66% (P <. 05). In the OR, percentage reduction in setup time ranged from 26% to 37% (P <. 05). Variability in assembly and setup times was also found to be narrower postoptimization. Discussion Tray optimization may reduce stress and adverse events and allow managers to better estimate staffing requirements. Cost‐benefits could not be determined given a limited understanding of how departments choose to redistribute time savings. Implications for Practice Measurable and significant time savings can be achieved by assessing instrument utilization rates and reducing tray redundancy, leading to lower performance variability and improved efficiency.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".