An economic evaluation of the Enhanced Recovery After Surgery (ERAS) multisite implementation program for colorectal surgery in Alberta
Bibliographic record
Abstract
BACKGROUND: In February 2013, Alberta Health Services established an Enhanced Recovery After Surgery (ERAS) implementation program for adopting the ERAS Society colorectal guidelines into 6 sites (initial phase) that perform more than 75% of all colorectal surgeries in the province. We conducted an economic evaluation of this initiative to not only determine its cost-effectiveness, but also to inform strategy for the spread and scale of ERAS to other surgical protocols and sites. METHODS: We assessed the impact of ERAS on patients’ health services utilization (HSU; length of stay [LOS], readmissions, emergency department visits, general practitioner and specialist visits) within 30 days of discharge by comparing pre- and post-ERAS groups using multilevel negative binomial regressions. We estimated the net health care costs/savings and the return on investment (ROI) associated with those impacts for post-ERAS patients using a decision analytic modelling technique. RESULTS: We included 331 pre- and 1295 post-ERAS patients in our analyses. ERAS was associated with a reduction in all HSU outcomes except visits to specialists. However, only the reduction in primary LOS was significant. The net health system savings were estimated at $2 290 000 (range $1 191 000–$3 391 000), or $1768 (range $920–$2619) per patient. The probability for the program to be cost-saving was 73%–83%. In terms of ROI, every $1 invested in ERAS would bring $3.8 (range $2.4–$5.1) in return. CONCLUSION: The initial phase of ERAS implementation for colorectal surgery in Alberta is cost-saving. The total savings has the potential to be more substantial when ERAS is spread for other surgical protocols and across additional sites.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".