Surgical Cost Awareness Program: Impact of a Novel, Real-Time Cost Awareness Intervention on Operating Room Expense in Thoracoscopic Lobectomy
Bibliographic record
Abstract
INTRODUCTION: For surgical patients, operating room expense is a significant driver of overall hospitalization cost. Surgical teams often lack awareness of the cost associated with surgical supplies, which may lead to unnecessary expenditure. The purpose of this study was to evaluate whether a surgical cost awareness program (SCUP) would reduce operating room cost. METHODS: The Surgical IQ software displays the type and cost of disposable instruments used in real time during operation and generates postoperative cost reports. A prospective pre-post controlled trial of thoracoscopic lobectomy procedures performed by 7 surgeons at a single academic center was conducted. Control and intervention groups consisted of consecutive cases from February 2, 2021, to June 23, 2021, and from June 28, 2021, to December 22, 2021, respectively. The primary outcome was mean per case surgical disposables cost. RESULTS: A total of 322 thoracoscopic lobectomies were evaluated throughout the study period (control: n = 164; intervention: n = 158). Baseline patient and tumor characteristics were comparable between groups. Mean disposables cost per case was $3,320.73 ±,$814.83 in the control group compared with $2,567.64 ± $594.59 in the intervention group, representing a mean cost reduction of $753.08 (95% CI, $622.29 to $883.87; p < 0.001). All surgeons experienced a reduction in disposables cost after the intervention. Intraoperative and postoperative outcomes did not differ between the cohorts (Table). Table. - Cost Use Measures and Surgical Outcomes Variable Control group (N = 164) Intervention group (N = 158) p Value Disposables cost (CAD$), mean (SD) 3,320.73 (±814.83) 2,567.64 (±594.59) <0.001 Operative time (minutes), mean (SD) 112 (±45) 114 (±44) 0.704 Conversion from thoracoscopy to thoracotomy, n (%) 7 (4.3%) 5 (3.2%) 0.819 Transfusion <24 hours after operation, n (%) 8 (4.9%) 4 (2.5%) 0.414 Reoperation, n (%) 6 (3.7%) 4 (2.5%) 0.794 Chest tube duration (days), median (IQR) 4 (3-6) 3 (2-6) 0.348 Hospital length of stay (days), median (IQR) 5 (3-7) 4 (3-6) 0.537 Postoperative complication, n (%) 47 (28.7%) 41 (25.6%) 0.540 Readmission <60 days after operation, n (%) 13 (7.9%) 6 (3.8%) 0.621 CONCLUSION: Providing real-time educational feedback to surgical teams significantly reduced cost associated with surgical supplies without compromising surgical outcomes for lobectomy. Integrating the novel Surgical IQ software across other procedural settings may generate data insight with the potential for significant cost savings.
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 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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".