A qualitative study of the barriers and facilitators impacting the implementation of a quality improvement program for emergency departments: SurgeCon
Bibliographic record
Abstract
BACKGROUND: The implementation of intervention programs in Emergency Departments (EDs) is often fraught with complications due to the inherent complexity of the environment. Hence, the exploration and identification of barriers and facilitators prior to an implementation is imperative to formulate context-specific strategies to ensure the tenability of the intervention. OBJECTIVES: In assessing the context of four EDs prior to the implementation of SurgeCon, a quality improvement program for ED efficiency and patient satisfaction, this study identifies and explores the barriers and facilitators to successful implementation from the perspective of the healthcare providers, patients, researchers, and decision-makers involved in the implementation. SETTINGS: Two rural and two urban Canadian EDs with 24/7 on-site physician support. METHODS: Data were collected prior to the implementation of SurgeCon, by means of qualitative and quantitative methods consisting of semi-structured interviews with 31 clinicians (e.g., physicians, nurses, and managers), telephone surveys with 341 patients, and structured observations from four EDs. The interpretive description approach was utilized to analyze the data gathered from interviews, open-ended questions of the survey, and structured observations. RESULTS: A set of five facilitator-barrier pairs were extracted. These key facilitator-barrier pairs were: (1) management and leadership, (2) available resources, (3) communications and networks across the organization, (4) previous intervention experiences, and (5) need for change. CONCLUSION: Improving our understanding of the barriers and facilitators that may impact the implementation of a healthcare quality improvement intervention is of paramount importance. This study underscores the significance of identifing the barriers and facilitators of implementating an ED quality improvement program and developing strategies to overcome the barriers and enhance the facilitators for a successful implementations. We propose a set of strategies for hospitals when implementing such interventions, these include: staff training, champion selection, communicating the value of the intervention, promoting active engagement of ED staff, assigning data recording responsibilities, and requiring capacity analysis. TRIAL REGISTRATION: ClinicalTrials.gov. NCT04789902. 10/03/2021.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".