Successful implementation of an enhanced recovery after surgery programme for elective colorectal surgery: a process evaluation of champions’ experiences
Bibliographic record
Abstract
BACKGROUND: Enhanced recovery after surgery (ERAS) is a multimodal evidence-based approach to patient care that has become the standard in elective colorectal surgery. Implemented globally, ERAS programmes represent a considerable change in practice for many surgical care providers. Our current understanding of specific implementation and sustainability challenges is limited. In January 2013, we began a 2-year ERAS implementation for elective colorectal surgery in 15 academic hospitals in Ontario. The purpose of this study was to understand the process enablers and barriers that influenced the success of ERAS implementation in these centres with a view towards supporting sustainable change. METHODS: A qualitative process evaluation was conducted from June to September 2014. Semi-structured interviews with implementation champions were completed, and an iterative inductive thematic analysis was conducted. Following a data-driven analysis, the Normalization Process Theory (NPT) was used as an analytic framework to understand the impact of various implementation processes. The NPT constructs were used as sensitizing concepts, reviewed against existing data categories for alignment and fit. RESULTS: Fifty-eight participants were included: 15 surgeons, 14 anaesthesiologists, 15 nurses, and 14 project coordinators. A number of process-related implementation enablers were identified: champions' belief in the value of the programme, the fit and cohesion of champions and their teams locally and provincially, a bottom-up approach to stakeholder engagement targeting organizational relationship-building, receptivity and support of division leaders, and the normalization of ERAS as everyday practice. Technical enablers identified included effective integration with existing clinical systems and using audit and feedback to report to hospital stakeholders. There was an overall optimism that ERAS implementation would be sustained, accompanied by concern about long-term organizational support. CONCLUSIONS: Successful ERAS implementation is achieved by a complex series of cognitive and social processes which previously have not been well described. Using the Normalization Process Theory as a framework, this analysis demonstrates the importance of champion coherence, external and internal relationship building, and the strategic management of a project's organization-level visibility as important to ERAS uptake and sustainability.
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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 | 0.038 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| 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".