Development of an Enhanced Recovery After Surgery Guideline and Implementation Strategy Based on the Knowledge-to-action Cycle
Bibliographic record
Abstract
BACKGROUND: Enhanced Recovery After Surgery (ERAS) protocols have been shown to increase recovery, decrease complications, and reduce length of stay. However, they are difficult to implement. OBJECTIVE: To develop and implement an ERAS clinical practice guideline (CPG) at multiple hospitals. METHODS: A tailored strategy based on the Knowledge-to-action (KTA) cycle was used to develop and implement an ERAS CPG at 15 academic hospitals in Canada. This included an initial audit to identify gaps and interviews to assess barriers and enablers to implementation. Implementation included development of an ERAS guideline by a multidisciplinary group, communities of practice led by multidiscipline champions (surgeons, anesthesiologists, and nurses) both provincially and locally, educational tools, and clinical pathways as well as audit and feedback. RESULTS: The initial audit revealed there was greater than 75% compliance in only 2 of 18 CPG recommendations. Main themes identified by stakeholders were that the CPG must be based on best evidence, there must be increased communication and collaboration among perioperative team members, and patient education is essential. ERAS and Pain Management CPGs were developed by a multidisciplinary team and have been adopted at all hospitals. Preliminary data from more than 1000 patients show that the uptake of recommended interventions varies but despite this, mean length of stay has decreased with low readmission rates and adverse events. CONCLUSIONS: On the basis of short-term findings, our results suggest that a tailored implementation strategy based on the KTA cycle can be used to successfully implement an ERAS program at multiple 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 | 0.049 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".