Emergency Department Overcrowding: An Environmental Scan of Contributing Factors and a Summary of Systematic Review Evidence on Interventions
Bibliographic record
Abstract
Emergency department (ED) overcrowding occurs when the demand for health services in the ED exceeds the capacity of the ED, hospital, or community to deliver quality care in a reasonable amount of time. Overcrowding is worsening in jurisdictions across Canada and there is a need to address its many causes and identify potential solutions. This report uses a modified version of a conceptual model developed by Asplin et al. (2003) that organizes the emergency care system into 3 interdependent parts: input (arrival to the ED), throughput (flowing through the ED), and output (leaving the ED). We also examined an additional fourth part related to contextual factors and systems that affect overcrowding but lay outside of input, throughout, and output. Examples of factors include, but are not limited to, increased complexity of needs (input), diagnostic testing and procedures (throughput), boarding (output), and limited resources for mental health and substance use (outside the ED). Examples of interventions that were effective in some settings include, but are not limited to, prehospital decision-making by first responders, which reduced ED visits (input); short stay crisis units for people experiencing mental health challenges, which improved emergency department length of stay, wait times, boarding, and patient safety (throughput); ED-based discharge planning, which reduced ED return visits (output); and time-based policy reforms, which reduced ED length of stay (outside the ED). Most of the factors we identified in the published literature existed either outside of the ED or at the interface of the ED and other health care services (input and output), whereas most of the interventions we identified existed within the ED (throughput). We heard from participants (during multistakeholder dialogue sessions) and content experts that ED overcrowding is a complex health system issue for which the causes, impacts, and solutions extend beyond the ED. Specifically, the novel insights we heard included: ED overcrowding is better viewed as a problem of hospital overcrowding and strained resources in the broader social and health care systems. Contributing factors both within and outside the ED influence and interact with each other and are affected by economic, cultural, and institutional realities. Solving the issue requires addressing accountability and implementing multifaceted solutions in which several systems and voices work collaboratively. Existing technologies and data use and collection are not being used to their full potential; they can be better leveraged to alleviate this issue. In the identified literature, there was a lack of explicit reporting around equity and ethical considerations for factors contributing to, and interventions to alleviate, ED overcrowding. Future work should strive to deliberately and explicitly include ethical considerations inherent in research, planning, and policy-making; considerations of equity-deserving groups; and dedicate the time needed to consider the various facets of this issue. This CADTH report and our series of reports on ED overcrowding are a starting point to bridge the literature, stakeholder discussion, and expert opinion to help decision-makers understand the various parts of the issue and consult the relevant updated evidence to inform their work.
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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 | 0.027 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.025 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".