Overcrowding Indicators in Emergency Departments Across Countries: Scoping Review
Bibliographic record
Abstract
Background: Emergency department (ED) overcrowding is a persistent global health issue associated with adverse patient outcomes, diminished staff performance, and compromised health-system efficiency. Despite widespread recognition of the problem, there is no universally accepted approach to monitoring ED overcrowding. The use of disparate, nonstandardized indicators hampers cross-country comparison and the development of effective policies. A comprehensive synthesis of indicators currently used is essential to guide the adoption of robust, evidence-based metrics across diverse health care settings. Objective: This study aims to identify, consolidate, and categorize indicators that have been used internationally to assess ED overcrowding and to highlight gaps in their use. Methods: A comprehensive scoping review was conducted from October to November 2023 using four databases: PubMed, Scopus, Emerald Insight, and Google Scholar. Studies were systematically searched using predefined eligibility criteria. Level 1 and 2 screening were independently conducted by 9 researchers (NNMN, KASIP, NFS, NJN, MK, ZL, NNRA, LKY, and ISS) to minimize bias and enhance reliability, and discrepancies were resolved by consensus. A third reviewer (ISS) performed a full-text review, synthesis, and descriptive analysis. Indicators were categorized into input, throughput, and output. Input refers to factors driving ED demand, throughput encompasses internal ED processes such as triage, diagnostics, and treatment, and output addresses challenges in transferring patients to inpatient beds, such as bed shortages or delays. Descriptive analyses were then used to consolidate these indicators and to establish their relative importance. They were ranked based on frequency of reporting across diverse countries and health care settings. Results: Out of 1347 articles screened, 117 articles were included in the study. A total of 307 indicators were retrieved and then consolidated into 26 distinct indicators. The majority of indicators were classified within the throughput domain (209/307, 68%), followed by the output domain (62/307, 20%) and the input domain (36/307, 12%). The most common throughput indicator, which was frequently reported, was ED length of stay, cited 87 times, followed by left without being seen and waiting time, each reported 30 times. Length of stay consistently emerged as a primary marker of systemic bottlenecks and operational inefficiencies across health care systems. Conclusions: This review indicates that throughput measures, particularly length of stay, dominate current approaches to assessing ED overcrowding, whereas input and output indicators remain comparatively underrepresented. By consolidating 26 distinct indicators from 117 studies, this study provides a comprehensive evidence base to support the standardization of metrics for monitoring ED overcrowding internationally. These findings offer practical guidance for policymakers and health care leaders seeking to refine performance indicators, enhance benchmarking, and evaluate interventions aimed at improving patient flow. Further research should prioritize validation of underused indicators and the development of composite measures that better capture the complexity of ED crowding across diverse health care settings.
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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 | 0.038 | 0.170 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.046 | 0.046 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 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".