Surgical Instrument Optimization to Reduce Instrument Processing and Operating Room Setup Time
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 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.
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 | 0.001 | 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 | 0.001 | 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) | 0.001 | 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