Characteristics of frequent emergency department users in British Columbia, Canada: a retrospective analysis
Bibliographic record
Abstract
BACKGROUND: Frequent emergency department users disproportionately account for rising health care costs. We aimed to characterize frequent emergency department users in British Columbia, Canada. METHODS: We performed a retrospective analysis using health administrative databases. We included patients aged 18 years or more with at least 1 emergency department visit from 2012/13 to 2015/16, linked to hospital, physician billing, prescription and mortality data. We used annual emergency department visits made by the top 10% of patients to define frequent users (≥ 3 visits/year). RESULTS: Over the study period, 13.8%-15.3% of patients seen in emergency departments were frequent users. We identified 205 136 frequent users among 1 196 353 emergency department visitors. Frequent users made 40.3% of total visits in 2015/16. From 2012/13 to 2015/16, their visit rates per 100 000 BC population showed a relative increase of 21.8%, versus 13.1% among all emergency department patients. Only 1.8% were frequent users in all study years. Mental illness accounted for 8.2% of visits among those less than 60 years of age, and circulatory or respiratory diagnoses for 13.3% of visits among those aged 60 or more. In 2015/16, frequent users were older and had lower household incomes than nonfrequent users; the sex distribution was similar. Frequent users had more prescriptions (median 9, interquartile range [IQR] 5-14 v. 1, IQR 1-3), primary care visits (median 15, IQR 9-27 v. 7, IQR 4-12) and hospital admissions (median 2, IQR 1-3 v. 1, IQR 1-1), and higher 1-year mortality (10.2% v. 3.5%) than nonfrequent users. INTERPRETATION: Emergency department use by frequent users increased in BC between 2012/13 and 2015/16; these patients were heterogenous, had high mortality and rarely remained frequent users over multiple years. Our results suggest that interventions must account for heterogeneity and address triggers of frequent use episodes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".