Patients Who Use Multiple EDs: Quantifying the Degree of Overlap between ED Populations
Bibliographic record
Abstract
INTRODUCTION: The degree to which individual patients use multiple emergency departments (EDs) is not well-characterized. We determined the degree of overlap in ED population between three geographically proximate hospitals. METHODS: This retrospective cohort study reviewed administrative hospital records from 2003 to 2007 for patients registered to receive ED services at an urban academic, urban community, and suburban community ED located within 10 miles of one another. We determined the proportion who sought care at multiple EDs and secondarily characterized patterns of repeat encounters. RESULTS: There were 795,176 encounters involving 282,903 patients. There were 89,776 (31%) patients with multiple encounters to a single ED and 39,920 (14%) patients who sought care from multiple EDs. The 39,920 patients who sought care from multiple EDs generated 185,629 (23%) of all encounters. Patients with repeat encounters involving multiple EDs were more likely to be frequent or highly frequent users (30%) than patients with multiple encounters to a single ED (14%). CONCLUSION: While only 14% of patients received care from more than one ED, they were responsible for a quarter of ED encounters. Patients who use multiple EDs are more often frequent or highly frequent users than are repeat ED visitors to the same ED. Overlap between ED populations is sufficient to warrant consideration by multiple domains of research, practice, and policy.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".