Interventions to reduce wait times in emergency departments in Canadian hospitals: A scoping review
Bibliographic record
Abstract
OBJECTIVES: Prolonged wait times in Canada's Emergency Departments (EDs) adversely impact patients, hospital staff, and the healthcare system. Despite the growing literature on ED wait times in Canada, our understanding of what strategies work to reduce wait time remains sporadic due to the absence of a current, comprehensive mapping of the interventions implemented within EDs. This scoping review aims to address this gap and map ED interventions in Canada, which may be useful for policymakers and healthcare professionals to make evidence-informed decisions. STUDY DESIGN: Scoping Review. METHODS: Utilizing Arksey and O'Malley's methodological framework, we summarized peer-reviewed articles on interventions in Canadian EDs from January 2010 to May 2024. To categorize and interpret the diverse interventions, we conducted a narrative synthesis using Braun and Clark's thematic analysis method. RESULTS: We identified 21 articles, predominantly focusing on Ontario (n = 16). Most studies utilized retrospective evaluations (n = 16), followed by cluster randomized trials (n = 2), quasi-experimental design (n = 1), prospective survey (n = 1), and before-after design (n = 1). Nearly all were in high-volume urban EDs, with one in a rural setting. Interventions were categorized into five themes: Alternative Location, Financial Incentives, Health Workforce Enhancement, Process Improvement, and Integrated Intervention. While alternative ED locations, health workforce enhancement, and integrated approaches showed promise in reducing ED wait times, financial incentives and process improvement initiatives showed mixed results. CONCLUSIONS: The reviewed interventions focused on strengthening ED operational efficiencies, but sustainable wait time reduction necessitates multifaceted, context-specific approach. Future research should consider broader health system challenges and ED contextual issues.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.001 | 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".