ArticleArthroplasty today2026
Optimizing Daily Surgical Scheduling Improves Operative Time Consumption: A Retrospective Study.
Article in Arthroplasty today, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
9 authors.
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Abstract
Background: Arthroplasty cases represent some of the highest volume and resource-intensive orthopaedic procedures. Most cost containment efforts have focused on restricting access to operating room (OR) time rather than improving efficiency through scheduling optimization. However, approaches such as bin-packing optimization (BPO) can be used to generate more efficient schedules. This study compares manual scheduling with BPO to evaluate their impact on OR efficiency. Our hypothesis is that BPO scheduling produces more efficient OR schedules compared to manual scheduling. Methods: Data were collected retrospectively for a mid-sized hospital performing elective orthopaedic surgery (majority hip and knee arthroplasty). Data included operative time, overtime, and number of OR days. BPO model was used to simulate an optimized schedule and compared to the historic manual schedule by comparing the number of OR days required to complete the same surgical volume. The primary outcome was the total number of OR days required. Results: All surgeons experienced a median 10% reduction (range: 9.2%-18.3%) in OR days required using BPO schedules (mean 183 days) vs historical scheduling (mean 207 days). Daily OR configurations differed from historical ones in 87% of cases, and the number of configurations per surgeon dropped from an average of 6 to 4. Conclusions: BPO increased OR throughput by 10% without increasing operative time. This suggests potential for reduced costs, shorter wait lists, and improved surgeon satisfaction for hip and knee arthroplasty. Future work should incorporate predictive modeling using patient- and procedure-specific data. Level of Evidence: III - Retrospective study.
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