Comprehensive Custom-Pak® in Cataract and Vitreoretinal Surgery in Canadian Hospitals: Time and Budget Impact Analysis
Bibliographic record
Abstract
Purpose: Surgical procedure packs are commonly used in ophthalmological surgeries, but quantitative evidence demonstrating the time efficiency and economic impact of their use is limited. Evaluating the time and cost of surgical pack use is particularly important for publicly funded healthcare systems with tight budgets and/or an emphasis on value-based care. This study sought to estimate the economic impact of comprehensive surgical pack use in cataract and vitreoretinal (retina) surgeries across operating room (OR), materials management, and accounting departments in Canada. Methods: A budget impact model developed for the United States (US) from a self-reported cross-sectional study was adapted for Canada. The US study obtained data via an online survey and timing exercises of surgical procedures. The model was adapted using relevant Canadian-specific labor and cost inputs. Use of generic commodity packs (no proprietary equipment-specific supplies) was compared to full utilization of Custom-Pak ® , a comprehensive pack (disposables plus equipment-specific supplies) in cataract and retina surgeries at the facility and aggregate group (provincewide) levels. Results: Switching from generic to comprehensive pack use in all 2500 cataract procedures at a community hospital saves 287 labor hours per year, primarily in the materials management department. Surgery preparation (OR) hours saved allow for an additional 196 potential procedures annually. Most of the Canadian Dollar (CAD) $39,815 annual cost savings are realized for the OR. Aggregating across 50,000 cataract surgeries at the provincial level yields savings of 5608 hours and 3916 additional procedures, translating to hidden cost reductions of CAD$790,632 annually. Implementing full Custom-Pak use at the facility level in 1000 retina cases saves $10,650 annually; provincewide, 127 potential additional procedures can be gained. Conclusion: Comprehensive Custom-Pak use improves efficiency in cataract and retina surgeries saving substantial time and cost in Canadian hospitals, potentially allowing more patients to have these procedures, and reducing wait times. Keywords: cataract, retina, surgical instruments, economic models, Canada, hospital materials management
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".