A Dedicated Orthopaedic Trauma Room Improves Efficiency While Remaining Financially Net Positive
Bibliographic record
Abstract
OBJECTIVES: To determine the impact of dedicated orthopaedic trauma room (DOTR) implementation on operating room efficiency and finances. DESIGN: Retrospective cost-analysis. SETTING: Single midsized academic-affiliated community hospital in Toronto, Canada. PARTICIPANTS: All patients that underwent the most frequently performed orthopaedic trauma procedures (hip hemiarthroplasty, open reduction internal fixation of the ankle, femur, elbow and distal radius), over a 4-year period from 2016 to 2019 were included. INTERVENTION: Patient data acquired for 2 years before the implementation of a DOTR was compared with data acquired for a 2-year period after its implementation, adjusting for the number of cases performed. MAIN OUTCOME MEASUREMENTS: The primary outcome was surgical duration. The secondary outcome was financial impact, including after-hours costs incurred and opportunity cost of displaced elective surgeries. RESULTS: One thousand nine hundred sixty orthopaedic cases were examined pre- and post-DOTR. All procedures had reduced total operative time post-DOTR (mean improvement of 33.4%). The number of daytime operating hours increased 21%, whereas after-hours decreased by 37.8%. Overtime staffing costs were reduced by $24,976 alongside increase in opportunity costs of $22,500. This resulted in a net profit of $2476. CONCLUSIONS: Our results support the premise that DOTRs improve operating room efficiency and can be cost efficient. Our study also specifically addresses the hesitation regarding potential loss of profit from elective surgeries. Widespread implementation can improve patient care while still remaining financially favorable. LEVEL OF EVIDENCE: Economic Level IV. See Instructions for Authors for a complete description of levels of evidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 teacher head, 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".