Reducing otolaryngology surgical inefficiency via assessment of tray redundancy
Bibliographic record
Abstract
BACKGROUND: Health care costs in Canada continue to rise. As a result of this relentless increase in healthcare spending, ways to increase efficiency and decrease cost are constantly being sought. Surgical treatment is the mainstay of therapy for many conditions in the field of Otolaryngology- Head and Neck Surgery. The evidence suggests that room exists to optimize tray efficiency as a novel means of improving operating room throughput. METHODS: We conducted a review of instruments on surgical trays for 5 commonly performed procedures between July 5th, 2013 and September 20th, 2013 at St Joseph's Hospital. The Instrument Utilization Rate was calculated; we then designed new 'optimized' trays based on which instruments were used at least 20% of the time. We obtained tray building times from Central Processing Department, then calculated an overall mean time per instrument (to pack the freshly washed instruments). We then determined the time that could be saved by using our new optimized trays. RESULTS: In total, 226 instrument trays were observed (Table 1). The average Instrument Utilization Rate was 27.8% (+/- 13.1). Our optimized trays, on average, reduced tray size by 57%. The average time to pack one instrument was 17.7 seconds. CONCLUSIONS: By selectively reducing our trays, we plan to reduce tray content by an average of 57%. It is important to remember that this number looks at only 5 procedures in the Department of Otolaryngology- Head and Neck Surgery. If this was expanded city-wide to the rest of the departments, the improved efficiency could potentially be quite substantial.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".