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 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 | 0.039 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".