The financial impact of an Enhanced Recovery Protocol in colo-rectal surgical care
Bibliographic record
Abstract
Objective: Enhanced Recovery Protocols (ERPs) have been shown in many different settings to lead to quicker recovery for most patients, with a significantly reduced average length of post-operative stay (LOS). A less studied impact of ERPs has been their effect on hospital profitability. While these protocols are resource-intensive and expensive to implement, we argue that they can lead to significantly improved margins. This can be attributed to fewer complications and, more significantly, reductions in LOS resulting in increased patient capacity.Methods: Our ERP was implemented in June of 2014. The protocol was initially used only for colo-rectal cases, both elective and emergent. It contained over 20 pre-, intra-, and post-operative elements of surgical care. One year of data from the ERP cases was compared to contemporaneous controls that did not go through the ERP. Financial data was obtained from the hospital cost accountant. Average LOS was obtained from the EHR.Results: Patients who underwent colo-rectal procedures and participated in the ERP had an average LOS of 5.60 days, while controls stayed for an average of 8.51 days. Financial analysis determined that a full year of compliance with Enhanced Recovery After Surgery (ERAS) protocols added over 2 million dollars to the margin for a return on investment (ROI) of over 10 to 1, mainly by increasing hospital capacity and allowing more admissions.Conclusions: The results demonstrate that ERPs significantly reduce LOS, increasing hospital patient capacity. The higher patient load more than recoups ERP costs. Further collection and analysis of data aims to determine the effect on complications, which also have cost saving potential.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".