Pediatric Emergency Department Return Visits
Bibliographic record
Abstract
OBJECTIVES: Emergency department (ED) return visits (RVs) leading to hospital admission are a quality measure that can potentially signal gaps in patient care. Systematic capture and investigation of RVs at a case level can provide an understanding of patient- and visit-level factors associated with RVs, and thus inform system-level quality improvement (QI) opportunities. Our objective is to describe the development of a database that enables tracking and analyzing of all pediatric ED RVs, to understand recurring themes and inform QI initiatives. METHODS: A single-center retrospective cohort study was conducted at a quaternary care children's hospital during a 3-year period (December 2013 to November 2016). All 72-hour RVs were audited for patient- and visit-level variables and clinicians completed root-cause analyses of their RVs. Using descriptive statistics, variables associated with RVs and system-level quality themes were identified. RESULTS: Of 214,047 ED patient visits, 1546 (0.7%) patients returned within 72 hours and were admitted. The RV patients had higher acuity scores on both visits compared with all ED visits, and the RV group had a higher proportion of children younger than 12 months than the overall ED visit group (25.0% vs 16.2%). The underlying cause for the majority of RVs was determined to be natural disease progression (63%), whereas 9% were callbacks for positive blood cultures or discrepant radiology results, and 6% were categorized as misdiagnoses. Several successful QI initiatives were completed as a result of the program. CONCLUSIONS: Systematic monitoring and investigation of all ED RVs provides an innovative and effective approach to seeking provider- and system-level improvement opportunities.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".