Individual predictors of frequent emergency department use: a scoping review
Bibliographic record
Abstract
BACKGROUND: A small proportion of patients use an excessively large amount of emergency care resources which often results in emergency department (ED) overcrowding, decreased quality of care and efficiency. There is a need to better identify these patients in order to target those who will benefit most from interventions adapted to their specific needs. We aimed to identify the predictive factors of short-term frequent use of ED (over a 1-year period) and chronic frequent use of ED (over a multiple-year period) and to highlight recurring characteristics in patients. METHODS: A scoping review was performed of all relevant articles found in Medline published between 1979 and 2015 (Ovid). This scoping review included a total of 20 studies, of these, 16 articles focussed on frequent ED users and four others on chronic frequent ED users. RESULTS: A majority of articles confirm that patients who frequently visit the ED are persons of low socioeconomic status. Both frequent and chronic frequent ED users show high levels of health care use (other than the ED) and suffer from multiple physical and mental conditions. CONCLUSIONS: This research highlights which individual factors predict frequent emergency department use. Further research is needed to better characterize and understand chronic frequent users as well as the health issues and unmet medical needs that lead to chronic frequent ED use.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".