Surgical Cost Awareness Program Study: Impact of a Novel, Real-Time, Cost Awareness Intervention on Operating Room Expenses in Thoracoscopic Lobectomy
Bibliographic record
Abstract
BACKGROUND: For surgical patients, operating room expenses are significant drivers of overall hospitalization costs. Surgical teams often lack awareness of the costs associated with disposable surgical supplies, which may lead to unnecessary expenditures. The aim of this study is to evaluate whether a Surgical Cost Awareness Program would reduce operating room costs. STUDY DESIGN: A prototype software displays the types and costs of disposable instruments used in real-time during surgery and generates insight-driven operative cost reports, which are automatically sent to the surgeons. 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 2nd through June 23, 2021, and from June 28th through December 22, 2021, respectively. The primary outcome was mean per case surgical disposables cost. RESULTS: Three hundred twenty-two lobectomies were evaluated throughout the study period (control: n = 164; intervention: n = 158). Baseline 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 disposable costs after the intervention. Intraoperative and postoperative outcomes did not differ between the cohorts. CONCLUSIONS: Providing real-time educational feedback to surgical teams significantly reduced costs associated with disposable surgical equipment without compromising perioperative outcomes for lobectomy. Integrating the novel AssistIQ software across other procedural settings may generate further data insights with the potential for significant cost savings on a larger scale.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".