Improvement Initiative to Ensure Quality Instrumentation in the OR
Bibliographic record
Abstract
At Seattle Children's Hospital, in November 2016, the operating room (OR) physicians reported experiencing a high number of issues occurring during cases and believed a significant amount was related to sterile processing department (SPD) errors. These errors, hereafter called "defects," were not defined or routinely reported. There was no method of capturing these defects. There was no root cause analysis or trending of defect data. This project aimed to improve the quality of surgical instruments received in the OR. METHODS: The SPD and OR leaders collaborated to develop an OR Case Sign-Out form to capture defects during the case. The data were triaged and assigned to specific departments for root cause analysis. The SPD related data were depicted with a Pareto chart to highlight the most significant opportunities for improvement. We developed a driver diagram and identified the following interventions: orientation and competency, technician OR rotation, capacity/full-time employee analysis, surgical instruments inventory, instrument pouch work trigger, work environment, preventative maintenance, and instrument wrap reduction. RESULTS: A 56% improvement in "Non-Sterile" defects was achieved. While a centerline shift in "Sterile" defects was not observed, the most significant "Sterile" defect, "breach of soft instrument wrap," dropped from 8 occurrences (at baseline) to 1. The number of OR case sign-out forms collected plateaued at 47%, which could indicate missing defect data. CONCLUSIONS: SPD improved quality in the OR by reducing instrument defects. The physicians gained a mechanism for reporting barriers and tracking improvements. Ultimately, the utilization of lean tools and a quality improvement approach helped drive process changes, creating a more efficient, collaborative, and safe procedural environment for patients and staff.
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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 | 0.025 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".