The Impact of the Implementation of the Enhanced Recovery After Surgery (ERAS<sup>®</sup>) Program in an Entire Health System: A Natural Experiment in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: The Enhanced Recovery After Surgery (ERAS) program has been shown to reduce length of stay (LOS) in colorectal surgical patients in randomized trials. The impact outside of trial settings, or in subgroups of patients excluded from trials such as individuals with diabetes, is uncertain. We conducted this study to evaluate the impact of ERAS implementation in Alberta, Canada. METHODS: This is a retrospective cohort study and interrupted time series analysis using linked administrative data to examine LOS and postoperative outcomes in the 12 months pre- and post-implementation of ERAS in 2013 for all adults undergoing elective colorectal surgery. RESULTS: Of 2714 patients (mean age 60.4 years, 55% men) with similar demographics and comorbidity profiles in the pre/post-ERAS time periods, LOS was significantly shorter post-ERAS (8.5 vs. 9.5 days, p = 0.01; - 0.84 days [95% CI - 0.04 to - 1.64 days] after adjustment for age, sex, Charlson comorbidity score, procedure type, surgical approach, and hospital). However, interrupted time series demonstrated no significant level change (p = 0.30) or change in slope (p = 0.63) with ERAS implementation, consistent with continuation of an underlying secular trend of reductions in LOS over time. There were no significant differences (in multivariate analysis or ITS) in risk of 30-day death/readmission (14.3% post vs. 13.5% pre-ERAS, aOR 1.12, 95% CI 0.89-1.40), 30-day death/ED visit (27.2% post vs. 30.0% pre, aOR 0.93, 95% CI 0.78-1.10), or 30-day death/readmission/ED visit (27.8% post vs. 30.6% pre, aOR 0.93, 95% CI 0.78-1.10). The 428 patients with diabetes had longer LOS but exhibited no significant difference post- versus pre-ERAS (10.7 vs. 11.6 days, p = 0.53; p = 0.56 for level change and p = 0.66 for slope change on ITS). CONCLUSION: Although there was a secular trend toward decreasing LOS over time in Alberta, ERAS implementation was not associated with statistically significant changes in LOS or postoperative outcomes for all colorectal surgery patients or for those with diabetes. Our study highlights the importance of evaluating system changes (for both uptake and outcomes) rather than assuming trial benefits will translate directly into practice. Interventions to improve LOS and postoperative outcomes for patients with diabetes undergoing colorectal surgery are still needed even in the ERAS era.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".