Implementation of Enhanced Recovery After Surgery: a strategy to transform surgical care across a health system
Bibliographic record
Abstract
BACKGROUND: Enhanced Recovery After Surgery (ERAS) programs have been shown to have a positive impact on outcome. The ERAS care system includes an evidence-based guideline, an implementation program, and an interactive audit system to support practice change. The purpose of this study is to describe the use of the Theoretic Domains Framework (TDF) in changing surgical care and application of the Quality Enhancement Research Initiative (QUERI) model to analyze end-to-end implementation of ERAS in colorectal surgery across multiple sites within a single health system. The ultimate intent of this work is to allow for the development of a model for spread, scale, and sustainability of ERAS in Alberta Health Services (AHS). METHODS: ERAS for colorectal surgery was implemented at two sites and then spread to four additional sites. The ERAS Interactive Audit System (EIAS) was used to assess compliance with the guidelines, length of stay, readmissions, and complications. Data sources informing knowledge translation included surveys, focus groups, interviews, and other qualitative data sources such as minutes and status updates. The QUERI model and TDF were used to thematically analyze 189 documents with 2188 quotes meeting the inclusion criteria. Data sources were analyzed for barriers or enablers, organized into a framework that included individual to organization impact, and areas of focus for guideline implementation. RESULTS: Compliance with the evidence-based guidelines for ERAS in colorectal surgery at baseline was 40%. Post implementation compliance, consistent with adoption of best practice, improved to 65%. Barriers and enablers were categorized as clinical practice (22%), individual provider (26%), organization (19%), external environment (7%), and patients (25%). In the Alberta context, 26% of barriers and enablers to ERAS implementation occurred at the site and unit levels, with a provider focus 26% of the time, a patient focus 26% of the time, and a system focus 22% of the time. CONCLUSIONS: Using the ERAS care system and applying the QUERI model and TDF allow for identification of strategies that can support diffusion and sustainment of innovation of Enhanced Recovery After Surgery across multiple sites within a health care system.
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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 | 0.050 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| 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".