Characterizing high-frequency emergency department users in a rural northwestern Ontario hospital: a 5-year analysis of volume, frequency and acuity of visits.
Bibliographic record
Abstract
INTRODUCTION: High-frequency emergency department users contribute substantially to urban emergency department workloads. The scope of this issue in rural emergency care provision is largely unknown. METHODS: We retrospectively analyzed emergency department visits at the Sioux Lookout Meno Ya Win Health Centre and associated primary care data from 2010 to 2014 for high-frequency (≥ 6 annual visits) and non-high-frequency(< 6 annual visits) emergency department users. RESULTS: High-frequency use of the emergency department was stable over the study period. High-frequency users constituted 7.2% of the emergency department patient population and accounted for 31.3% of the emergency department workload and 24.3% of hospital admissions. High-frequency users had similar clinical presentations as non-high-frequency users but required fewer admissions per emergency department visit (5.3% vs. 7.6%, p < 0.001). High-frequency users had more low-acuity presentations and concurrently accessed primary care services twice as often as non-high-frequency users. Females outnumbered males across all age categories in both user groups. CONCLUSION: High-frequency emergency department use is an important issue for rural hospitals. High use of this rural emergency department was not associated with limited use of primary care services. Aside from accepting that "they will always be with us," more research, particularly qualitative, is needed to understand why some patients frequently visit a rural emergency department.
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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) | 0.000 | 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 | 0.000 |
| 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".