Millions Saved in Head and Neck Free Flap Reconstruction at a High-Volume Center: A Cost Analysis
Bibliographic record
Abstract
Background: Within a resource-limited healthcare system, an emphasis on financial accountability is imperative. Over the past decade at our institution, there have been many operational changes employed to improve patient care during oncologic head and neck resections with free flap (HNFF) reconstruction. The objective of this study is to assess whether these changes are associated with cost savings. Methods: A retrospective cohort study that included consecutive patients treated from January 2007 to February 2020 was performed. The perspective of the third payer party was used and direct costs were considered. The peri-operative period was defined as the day of surgery and subsequent admission. Total peri-operative cost was defined as staffing, material, reconstructive surgeon, anesthetist, and admission costs. Costs are represented in Canadian Dollars ($CAD) adjusted for inflation. Results: There were 590 consecutive cases. Average age was 61 with a male proportion of 69% (n = 409). Tumor type, need for tracheostomy, neck dissection, anatomic region resected, 30-day re-operation, and re-admission did not change significantly over the study period ( P > 0.05). The mean total operative time per case decreased by 4.1 h over the study period. The median length of stay per patient decreased by 4.5 days. The total peri-operative cost per patient during the study period decreased by $19,928. Net cost savings to the third-party payer over the study period was $8,142,962. Conclusion: A culture of improvement-focused teamwork allowed for several advances over the study period. These were associated with improved patient care, operative efficiency, and significant cost savings of HNFF reconstruction.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".