Optimizing Operating Room Efficiency for Primary Hip and Knee Arthroplasty Using Performance Benchmarks
Bibliographic record
Abstract
Background: With increasing demand for total hip arthroplasty (THA) and total knee arthroplasty (TKA), maximizing operating room (OR) efficiency is critical. This paper sought to examine the implementation of time benchmarks when performing primary TKA and THA. We hypothesized that implementing benchmarks would improve efficiency and the number of joints performed per day. Methods: ; American Society of Anesthesia, 2. Time points, demographics, and adverse events were recorded. Benchmarks to complete 4 joints in 8 h were: anesthesia preparation time (APT) of <11 min, procedure time of <72 min, anesthesia finish time (AFT) of <21 min, and turnover of <22 min. Results: The percentage of cases meeting individual benchmarks for APT was 50.17%; procedure time was 95.25%; AFT was 99.67%; turnover was 65.25%. The means were: APT 11:00 min, Surgical Prep Time 9:00 min, procedure time 55:00 min, AFT 3:00 min, and turnover 19:00 min. Overall, 98.3% (58/59) of ORs had 4 cases completed within 8 h and 52.5% (31/59) had 5 cases within 8 h. Age, body mass index, and consecutive laterality of surgery were determined to affect the likelihood of meeting benchmarks for case time, APT, and turnover. Conclusions: Establishing time benchmarks permitted the introduction of 5 joint days within an 8-h OR without increasing resource utilization. Factors that influence OR efficiency for high-volume primary hip and knee replacements were identified.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".