International Perspectives on Emergency Department Crowding
Bibliographic record
Abstract
The maturation of emergency medicine (EM) as a specialty has coincided with dramatic increases in emergency department (ED) visit rates, both in the United States and around the world. ED crowding has become a public health problem where periodic supply and demand mismatches in ED and hospital resources cause long waiting times and delays in critical treatments. ED crowding has been associated with several negative clinical outcomes, including higher complication rates and mortality. This article describes emergency care systems and the extent of crowding across 15 countries outside of the United States: Australia, Canada, Denmark, Finland, France, Germany, Hong Kong, India, Iran, Italy, The Netherlands, Saudi Arabia, Catalonia (Spain), Sweden, and the United Kingdom. The authors are local emergency care leaders with knowledge of emergency care in their particular countries. Where available, data are provided about visit patterns in each country; however, for many of these countries, no national data are available on ED visits rates or crowding. For most of the countries included, there is both objective evidence of increases in ED visit rates and ED crowding and also subjective assessments of trends toward higher crowding in the ED. ED crowding appears to be worsening in many countries despite the presence of universal health coverage. Scandinavian countries with robust systems to manage acute care outside the ED do not report crowding is a major problem. The main cause for crowding identified by many authors is the boarding of admitted patients, similar to the United States. Many hospitals in these countries have implemented operational interventions to mitigate crowding in the ED, and some countries have imposed strict limits on ED length of stay (LOS), while others have no clear plan to mitigate crowding. An understanding of the causes and potential solutions implemented in these countries can provide a lens into how to mitigate ED crowding in the United States through health policy interventions and hospital operational changes.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".