Evaluating emergency department transfers from urgent care centres: insights for paramedic integration with subacute healthcare
Bibliographic record
Abstract
OBJECTIVE: Paramedics redirecting non-emergent patients from emergency departments (EDs) to urgent care centres is a new and forthcoming strategy to reduce overcrowding and improve primary care integration. Which patients are likely not suitable for paramedic redirection are unknown. To describe and specify patients inappropriate for urgent care centres, we examined associations between patient characteristics and transfer to the ED after patients initially presented to an urgent care centre. METHODS: A population-based retrospective cohort study of all adult (≥18 years) visits to an urgent care centre from 1 April 2015 to 31 March 2020 in Ontario, Canada. Binary logistic regression was used to determine unadjusted and adjusted associations between patient characteristics and being transferred to an ED using OR and 95% CIs. We calculated the absolute risk difference for the adjusted model. RESULTS: A total of 1 448 621 urgent care visits were reported, with 63 343 (4.4%) visits transferred to an ED for definitive care. Being 65 years and older (OR 2.29, 95% CI 2.23 to 2.35), scored an emergent Canadian Triage and Acuity Scale of 1 or 2 (OR 14.27, 95% CI 13.45 to 15.12) and higher comorbidity count (OR 1.51, 95% CI 1.46 to 1.58) had added odds of association with being transferred out to an ED. CONCLUSION: Readily available patient characteristics were independently associated with interfacility transfers between urgent care centres and the ED. This study can support paramedic redirection protocol development, highlighting which patients may not be best suited for ED redirection.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".