People who make frequent emergency department visits based on persistence of frequent use in Ontario and Alberta: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: The factors that underlie persistent frequent visits to the emergency department are poorly understood. This study aimed to characterize people who visit emergency departments frequently in Ontario and Alberta, by number of years of frequent use. METHODS: This was a retrospective cohort study aimed at capturing information about patients visiting emergency departments in Ontario and Alberta, Canada, from Apr. 1, 2011, to Mar. 31, 2016. We identified people 18 years or older with frequent emergency department use (top 10% of emergency department use) in fiscal year 2015/16, using the Dynamic Cohort from the Canadian Institute of Health Information. We then organized them into subgroups based on the number of years (1 to 5) in which they met the threshold for frequent use over the study period. We characterized subgroups using linked emergency department, hospitalization and mental health-related hospitalization data. RESULTS: We identified 252 737 people in Ontario and 63 238 people in Alberta who made frequent visits to the emergency department. In Ontario and Alberta, 44.3% and 44.7%, respectively, met the threshold for frequent use in only 1 year and made 37.9% and 38.5% of visits; 6.8% and 8.2% met the threshold for frequent use over 5 years and made 11.9% and 13.2% of visits. Many characteristics followed gradients based on persistence of frequent use: as years of frequent visits increased (1 to 5 years), people had more comorbidities, homelessness, rural residence, annual emergency department visits, alcohol- and substance use-related presentations, mental health hospitalizations and instances of leaving hospital against medical advice. INTERPRETATION: Higher levels of comorbidities, mental health issues, substance use and rural residence were seen with increasing years of frequent emergency department use. Interventions upstream and in the emergency department must address unmet needs, including services for substance use and social supports.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".