Understanding Strategies to Reduce the Impact of Non-urgent Visits to the Pediatric Emergency Department
Bibliographic record
Abstract
CONTEXT: The pediatric emergency department (PED) is increasingly being used for non-urgent reasons. This impacts PED input and throughput, and contributes to overcrowding. To identify solutions, it is essential to identify and describe the approaches that have been trialed. OBJECTIVE: We completed a scoping review to identify and then describe the design and outcomes of all initiatives undertaken to reduce the impact of non-urgent visits on the PED. DATA SOURCES: We searched 4 databases (MEDLINE, EMBASE, EBM, and CINAHL) to identify research published from the database inception until March 31, 2024. STUDY SELECTION: Studies met our inclusion criteria if they focused on the pediatric ED, defined non-urgent visits, described an intervention (hypothesizing it would reduce the impact of non-urgent visits on the PED), and reported on the interventions impact. DATA EXTRACTION: The title and abstract of each study were independently screened for inclusion by 2 reviewers (E.Q., K.N.), and disagreements were resolved by deliberation until consensus was achieved. This process was then repeated for the full text of all articles. RESULTS: In total, we screened 11,600 articles and 20 were included. Nine interventions focused on PED input, 10 on PED throughput, and 1 on both PED input and throughput. Definitions of non-urgent visits and outcomes measures used to assess the effectiveness of an intervention differed between studies. Three types of strategies employed to reduce the impact of non-urgent visits on the PED were identified, these include (1) engaging nonpediatric emergency medicine clinicians by including them into the PED or connecting non-urgent patients to community locations for care, (2) reorganizing PED operations in anticipation of non-urgent visits, and (3) providing education to prevent future non-urgent visits. CONCLUSIONS: Consistent definitions of non-urgent visits and standardized outcome measures may allow for more precise comparisons between studies. We identify 3 commonly employed strategies that may help reduce the impact of non-urgent visits on the PED.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".