Interventions to improve consultations in the emergency department: A systematic review
Bibliographic record
Abstract
Abstract Objectives Emergency department (ED) consultations with specialists are necessary for safe and effective patient care. Delays in the ED consultation process, however, have been shown to increase ED length of stay (LOS) and contribute to ED crowding. This review aims to describe and evaluate the effectiveness of interventions to improve the ED consultation process. Methods Eight primary literature databases and the gray literature were searched to identify comparative studies assessing ED‐based interventions to improve the specialist consultation process. Two independent reviewers identified eligible studies, assessed study quality, and extracted data. Individual or pooled meta‐analysis for continuous outcomes were calculated as mean differences (MDs) with 95% confidence intervals (CIs) using a random‐effects model was conducted. Results Thirty‐five unique comparative intervention studies were included. While the interventions varied, four common components/themes were identified including interventions to improve consultant responsiveness ( n = 11), improve access to consultants in the ED ( n = 9), expedite ED consultations ( n = 8), and bypass ED consultations ( n = 7). Studies on interventions to improve consult responsiveness consistently reported a decrease in consult response times in the intervention group with percent changes between 10% and 71%. Studies implementing interventions to improve consult responsiveness (MD −2.55, 95% CI −4.88 to –0.22) and interventions to bypass ED consultations (MD −0.99, 95% CI −1.43 to –0.56) consistently reported a decrease in ED LOS; however, heterogeneity was high (I 2 = 99%). Evidence on whether any of the interventions were effective at reducing the proportion of patients consulted or subsequently admitted varied. Conclusions The various interventions impacting the consultation process were predominately successful in reducing ED LOS, with evidence suggesting that interventions improving consult responsiveness and improving access to consultants in the ED also improve consult response times. Health care providers looking to implement interventions to improve the ED consultation process should identify key areas in their setting that could be targeted.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".