Return on investment of the Enhanced Recovery After Surgery (ERAS) multiguideline, multisite implementation in Alberta, Canada
Bibliographic record
Abstract
Background: Enhanced Recovery After Surgery (ERAS) is a global surgical qualityimprovement initiative. Little is known about the economic effects of implementing multiple ERAS guidelines in both the short and long term. Methods: We performed a return on investment (ROI) analysis of the implementation of multiple ERAS guidelines (for colorectal, pancreas, cystectomy, liver and gynecologic oncology procedures) across multiple sites (9 hospitals) in Alberta using 30-, 180- and 365-day time horizons. The effects of ERAS on health services utilization (length of stay of the primary admission, number of readmissions, length of stay of the readmissions, number of emergency department visits, number of outpatient clinic visits, number of specialist visits and number of general practitioner visits) were assessed by mixed-effect multilevel multivariate negative binomial regressions. Net benefits and ROI were estimated by a decision analytic modelling analysis. All costs were reported in 2019 Canadian dollars. Results: The net health system savings per patient ranged from $26.35 to $3606.44 and ROI ranged from 1.05 to 7.31, meaning that every dollar invested in ERAS brought $1.05 to $7.31 in return. Probabilities for ERAS to be cost-saving were from 86.5% to 99.9%. The effects of ERAS were found to be larger in the longer time horizons, indicating that if only the 30-day time horizon had been used, the benefits of ERAS would have been underestimated. Conclusion: These results demonstrated that ERAS multiguideline implementation was cost-saving in Alberta. To produce a better ROI, it is important to consider a broad range of health service utilizations, long-term impact, economies of scale, productive efficiency and allocative efficiency for sustainability, scale and spread of ERAS implementations.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".