Developing implementation strategies to adopt Enhanced Recovery After Surgery (ERAS®) guidelines
Bibliographic record
Abstract
BACKGROUND: Strong implementation strategies are critical to the success of Enhanced Recovery after Surgery (ERAS®) guidelines, though little documentation exists on effective strategies, especially in complex clinical situations and unfamiliar contexts. This study outlines the process taken to adopt a novel neonatal ERAS® guideline. METHODS: The implementation strategy was approached in a multi-pronged, concurrent but asynchronous fashion. Between September 2019 and January 2020, healthcare providers from various disciplines and different specialties as well as parents participated in the strategy. Multidisciplinary teams were created to consider existing literature and local contexts including potential facilitators and/or barriers. Task forces worked collaboratively to develop new care pathways. An audit system was developed to record outcomes and elicit feedback for revision. RESULTS: 32 healthcare providers representing 9 disciplines and 5 specialties as well as 8 parents participated. Care pathways and resources were created. Elements recommended for a successful implementation strategy included identification of champions, multidisciplinary stakeholder involvement, consideration of local contexts and insights, patient/family engagement, education, and creation of an audit system. CONCLUSION: A multidisciplinary and structured process following principles of implementation science was used to develop an effective implementation strategy for initiating ERAS® guidelines.
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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 | 0.108 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".