Short-Term Outcomes of Paramedic Treat and Discharge: A Cohort Study of Emergency Service Use in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES: Canadian emergency departments (EDs) are under unprecedented strain due to record-high patient volumes and limited capacity expansion. To mitigate overcrowding, Ontario implemented a paramedic 'treat and discharge' model, enabling paramedics to assess, treat, and discharge patients with resolved seizure, hypoglycemia, or supraventricular tachydysrhythmia under specific clinical criteria. This study evaluates the short-term safety and effectiveness of this model by examining subsequent emergency service utilization. METHODS: We conducted a retrospective cohort study using linked data from paramedic services in southwestern Ontario and a provincial health care database from June 1, 2023, to November 15, 2024. All patients discharged by paramedics under this model were included. We analyzed the incidence of emergency service use (9-1-1 calls and ED visits), mortality, and other health care utilization within seven days post-discharge, categorized by the reason for service use as related-cause and all-cause. We computed descriptive statistics and relative risk (RR) with 95% confidence intervals (CIs). RESULTS: = 4), while tachydysrhythmia had none. All patients visiting an ED either following a 9-1-1 call or by walk-in for a related-cause were discharged. The overall incidence of all-cause emergency service use was 14 patients (11.4%). There were no deaths in the community or ED within seven days. Seizure patients had a higher risk of using emergency services after discharge if they had called for paramedics within the 14 days prior (RR 6.80, 2.44-18.96) or received paramedic medications (RR 5.83, 2.89-12.21). No significant risk increase was observed for patients tended to by primary care paramedics compared to advanced care, or those presenting with an emergent acuity on initial contact. CONCLUSIONS: The low incidence of subsequent emergency service use supports the safety and feasibility of the paramedic treat and discharge model for select patients. These findings highlight the potential of alternative care models that optimize paramedic and ED resources and suggest that treat and discharge directives could be expanded to additional patient cohorts.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".