A Qualitative Study to Understand the Barriers and Enablers in Implementing an Enhanced Recovery After Surgery Program
Bibliographic record
Abstract
OBJECTIVE: Explore the barriers and enablers to adoption of an Enhanced Recovery after Surgery (ERAS) program by the multidisciplinary perioperative team responsible for the care of elective colorectal surgical patients. BACKGROUND: ERAS programs include perioperative interventions that when used together have led to decreased length of stay while increasing patient recovery and satisfaction. Despite the known benefits of ERAS programs, uptake remains slow. METHODS: Semistructured interviews were conducted with general surgeons, anesthesiologists, and ward nurses at 7 University of Toronto-affiliated hospitals to identify potential barriers and enablers to adoption of 18 ERAS interventions. Grounded theory was used to thematically analyze the transcribed interviews. RESULTS: Nineteen general surgeons, 18 anesthesiologists, and 18 nurses participated. The mean time of each interview was 18 minutes. Lack of manpower, poor communication and collaboration, resistance to change, and patient factors were cited by most as barriers. Discipline-specific issues were identified although most related to resistance to change. Overall, interviewees were supportive of implementation of a standardized ERAS program and agreed that a standardized guideline based on best evidence; standardized order sets; and education of the staff, patients, and families are essential. CONCLUSIONS: Multidisciplinary perioperative staff supported the implementation of an ERAS program at the University of Toronto-affiliated hospitals. However, major barriers were identified, including the need for patient education, increased communication and collaboration, and better evidence for ERAS interventions. Identifying these barriers and enablers is the first step toward successfully implementing an ERAS program.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".