Interventions Intended to Alleviate Emergency Department Overcrowding
Bibliographic record
Abstract
Why Is This an Issue? Emergency department (ED) overcrowding is a known issue in Canada that puts patients’ lives and health at risk when treatment needs within the ED exceed the resources required to address them. The causes and consequences of ED overcrowding are complex, varied, and extend beyond the ED. Left unchecked, ED overcrowding contributes to a deteriorating standard of care as staff become overworked and burned out. What Are the Potential Interventions to Address ED Overcrowding? This roundup of 87 new and emerging interventions intended to alleviated ED overcrowding complements CADTH’s Emergency Department Overcrowding: An Environmental Scan of Contributing Factors and a Summary of Systematic Review Evidence on Interventions. Interventions were included in this report if they are not captured in CADTH’s complimentary report Emergency Department Overcrowding: An Environmental Scan of Contributing Factors and a Summary of Systematic Review Evidence on Interventions because they are either new or are not yet in wide use in EDs and health systems across Canada and could be reasonably be expected to affect ED overcrowding (e.g., by improving patient flow through the ED or reducing the number of people seeking care in the ED). Interventions that could address multiple causal factors (e.g., e-consult services) were also included. What Is the Potential Impact? Decreasing ED overcrowding has the potential to improve the standard of care delivered to those accessing EDs and contribute to decreasing the burden on ED staff, regardless of the intervention or which contributing factor it aims to act upon. Some interventions that can alleviate ED overcrowding also have an impact on primary and community care; bolstering preventive care, increasing access to diagnostic testing for those who need it, and improving access to health care supports can improve health outcomes. What Else Do We Need to Know? The pan-Canadian issue of ED overcrowding calls for real solutions. CADTH is examining the evidence and expert-informed considerations and producing a series of publications about the causes and consequences of, and solutions to, ED overcrowding in health care systems across the country. The interventions identified in this report may be of interest to senior health care decision-makers who are anticipating health system innovation and transformation.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".