Improving Value in Colorectal Cancer Care: An Economic Analysis of Enhanced Recovery Protocols at a Community Hospital
Bibliographic record
Abstract
Background: Enhanced recovery protocols (ERPs) have been shown to improve the outcomes of gastrointestinal cancer care, leading to reduced morbidity of gastrointestinal treatment and reduced delays in systemic therapy. ERP implementation has also previously shown a reduction in length of stay (LOS) without changing the readmission rate; however, the economic cost associated with these measures has not yet been quantified. The aim of this study was to evaluate the economic costs of ERP implementation for colorectal cancer at a community hospital. Methods: The Diagnostic Related Group (DRG) codes were used to assess costs associated with the hospitalizations of cases in the ERP versus non-ERP groups. The American Hospital Association (AHA) Annual Survey from 1999 to 2015 was used to provide the expenses per day for inpatient hospitalization in the United States. Postoperative LOS, average healthcare costs, and postoperative complications between ERP-protocol and non-ERP protocol groups were analyzed using analysis of variance (ANOVA) and independent t -tests. Results: The AHA survey estimated that $2,265 was incurred per day for non-profit hospitals in Florida and $2,346 was incurred per day for the United States. For all DRG codes, the ERP-participating group was associated with a shorter LOS and reduced health care costs. LOS-associated cost was compared between ERP and non-ERP groups: for DRG 329, the total savings was $162,118.8 (n = 12 non-ERP versus n = 8 ERP, P = 4.39 × 10 -18 ); for DRG 330, $314,552.64 (n = 36 non-ERP versus n = 24 ERP, P = 2.72 × 10 -22 ); and for DRG 331, $89,302.73 (n = 11 non-ERP versus n = 23 for ERP, P = 4.19 × 10 -20 ). Conclusions: The implementation of an ERP protocol for colorectal cancer was associated with significantly reduced costs in a community hospital. World J Oncol. 2023;14(5):401-405 doi: https://doi.org/10.14740/wjon1580
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".