Improving Operating Room Access and Utilization at an Academic Ophthalmology Department
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: A quality improvement project was conducted to improve operating room (OR) access and utilization for elective ophthalmic cases at a university-affiliated eye center. PATIENTS AND METHODS: This prospective, interventional case series assessed OR utilization across three ORs during a baseline 3-month period. Case scheduling protocols were reviewed, and key drivers of inefficiency were identified. Targeted interventions were implemented to address these barriers. RESULTS: During the baseline period, a mean of 290 surgeries were performed monthly. Scheduling delays were attributed to the lack of standardized order forms for special preoperative/operative needs, untimely completion of special preoperative tests, poor communication regarding open OR times, and inadequate enforcement of an established 14-day block time release. Following interventions, OR utilization increased by 20% and average monthly case volume rose to 350, adding approximately 60 surgeries per month. These improvements were accompanied by a more than twofold increase in use of the standardized EPIC surgical order with laterality (from 39 to 97 cases per month), demonstrating measurable adoption of workflow changes. CONCLUSIONS: Targeted, multidisciplinary interventions improved OR utilization by 20%. Quality improvement projects enhance patient access and optimize the use of institutional resources.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".