Moving enhanced recovery after surgery from implementation to sustainability across a health system: a qualitative assessment of leadership perspectives
Bibliographic record
Abstract
Abstract Background Knowledge Translation evidence from health care practitioners and administrators implementing Enhanced Recovery After Surgery (ERAS) care has allowed for the spread and scale of the health care innovation. There is a need to identify at a health system level, what it takes from a leadership perspective to move from implementation to sustainability over time. The purpose of this research was to systematically synthesize feedback from health care leaders to inform further spread, scale and sustainability of ERAS care across a health system. Methods Alberta Health Services (AHS) is the largest Canadian health system with approximately 280,000 surgeries annually at more than 50 surgical sites. In 2013 to 2014, AHS used a structured approach to successfully implement ERAS colorectal guidelines at six sites. Between 2016 and 2018, three of the six sites expanded ERAS to other surgical areas (gynecologic oncology, hepatectomy, pancreatectomy/Whipple’s, and cystectomy). This research was designed to explore and learn from the experiences of health care leaders involved in the AHS ERAS implementation expansion (eg. surgical care unit, hospital site or provincial program) and build on the model for knowledge mobilization develop during implementation. Following informed consent, leaders were interviewed using a structured interview guide. Data were recorded, coded and analyzed qualitatively through a combination of theory-driven immersion and crystallization, and template coding using NVivo 12. Results Forty-four individuals (13 physician leaders, 19 leading clinicians and hospital administrators, and 11 provincial leaders) were interviewed. Themes were identified related to Supportive Environments including resources, data, leadership; Champion and Nurse coordinator role; and Capacity Building through change management, education, and teams. The perception and role of leaders changed through initiation and implementation, spread, and sustainability. Barriers and enablers were thematically aligned relative to outcome assessment, consistency of implementation, ERAS care compliance, and the implementation of multiple guidelines. Conclusions Health care leaders have unique perspectives and approaches to support spread, scale and sustainability of ERAS that are different from site based ERAS teams. These findings inform us what leaders need to do or need to do differently to support implementation and to foster spread, scale and sustainability of ERAS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".