Applying quality improvement principles to improve accident and emergency department overcrowding and flow in Rwanda: a case study
Bibliographic record
Abstract
Few case studies exist related to hospital accident and emergency department (A&E) quality improvement efforts in lowerresourced settings. We sought to report the impact of quality improvement principles applied to A&E overcrowding and flow in the largest referral and teaching hospital in Rwanda. A pre- and post-intervention study was conducted. A linked set of strategies included reallocating room space based on patient/visitor demand and flow, redirecting traffic, establishing a patient triage system and installing white boards to facilitate communication. Two months post-implementation, the average number of patients boarding in the A&E hallways significantly decreased from 28 (pre-intervention) to zero (post-intervention), p < .001. Foot traffic per dayshift hour significantly decreased from 221 people to 160 people (28%, p < .001), and non-A&E related foot traffic decreased from 81.4% to 36.3% (45% decrease, p < .001). One hundred percent of the A&E patients have been formally triaged since the implementation of the newly established triage system. Our project used quality improvement principles to reduce the number of patients boarding in the hallways and to decrease unnecessary foot traffic in the A&E department with little investment from the hospital. Key success factors included a collaborative multidisciplinary project team, strong internal champions, data-driven analysis, evidence-based interventions, senior leadership support, and rapid application of initial implementation learnings. Results to date show the application of quality improvement principles can help hospitals in resource-limited settings improve quality of care at relatively low cost.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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 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".