Characteristics and Outcomes of Implementing Emergency Department-based Intensive Care Units: A Scoping Review
Bibliographic record
Abstract
Introduction: The prolonged stay of critically ill patients in the emergency department (ED) may lead to worse clinical outcomes. An emergency department (ED)-based intensive care unit (ICU) is one of the proposed solutions to deliver critical care in the ED. We thus aimed to characterize existent ED-ICU models and their reported association with clinical outcomes in critically ill adult patients. Methods: We searched the Ovid MEDLINE database from inception to October 2, 2023. We included studies that report an ED-ICU structure, defined as a space capable of providing ICU-level care within or adjacent to the ED, and its characteristics. We excluded personnel-focused intervention (without the presence of a separated space) or a space without ICU-level care capability. We collected information on process measures, patient-related outcomes, and cost-related outcomes. Results: We screened 2,824 studies, of which 125 full-text articles were assessed for eligibility and 31 studies were included in this scoping review. Studies reported on 14 ED-ICUs across seven countries, with capacities ranging from 3-17 beds. All ED-ICUs served early and ongoing critical care needs in the ED, including three distinct themes: short-stay; palliative care; and disaster-response ICUs. Implementing the ED-ICU was associated with decreased time to ICU-level care and reduced number of inpatient ICU admissions, but it was not consistently associated with improved survival. Conclusion: Several ED-ICUs have been established around the world with different characteristics depending on local needs. Implementation of the ED-ICU may be associated with improved clinical outcomes and patient flow.
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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 | 0.010 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".