High-throughput Operating Room System for Joint Arthroplasties Durably Outperforms Routine Processes
Bibliographic record
Abstract
BACKGROUND: Recent publications have focused on increased operating room (OR) throughput without increasing total OR time. The authors hypothesized that a system of parallel processing for lower extremity joint arthroplasties sustainably reduces nonoperative time and increases throughput. METHODS: The high-throughput parallel processing strategy included neuraxial anesthesia performed in an "induction room" adjacent to the OR, patient selection, an additional circulating nurse, and end-of-case transfer of care to a recovery room nurse who transported the patient from the OR to recovery. Instruments and supplies were prepared in a dedicated sterile setup area. Data were extracted from administrative databases. Group comparisons used standard statistical methods; statistical process control was used to evaluate performance over time. RESULTS: There were 688 historic control cases from 299 days over 16 months, and 905 high-throughput cases from 304 days spanning 24 consecutive months starting September 1, 2004. Throughput increased from 2.6 +/- 0.7 (mean +/- SD) to 3.4 +/- 0.8 arthroplasties per day per room. Nonoperative time decreased by 36 min (or 50%) per case. Operative time also decreased by 14 min (12%) per case. The end time for the high-throughput OR day was only 16 min later than control. Nonoperative time, operative time, and throughput remained significantly improved after 2 yr of operation. Contribution margin increased 19.6%. CONCLUSION: Reorganizing the perioperative work process for total joint replacements sustainably increased OR throughput. Because joint arthroplasties generated a positive margin greater than the incremental cost, the high-throughput system improved financial performance.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".