Incident atrial fibrillation in the emergency department in Ontario: a population-based retrospective cohort study of follow-up care
Bibliographic record
Abstract
BACKGROUND: Continuity of care has been shown to be poor following in-hospital discharge, and there are substantially fewer resources to facilitate follow-up care arrangements after discharge from an emergency department. Our objective was to assess the frequency, timeliness and predictors for obtaining follow-up care following discharge from an emergency department in Ontario with a new diagnosis of atrial fibrillation. METHODS: We conducted a retrospective cohort study involving all patients discharged from the 157 nonpediatric emergency departments in Ontario, who received a new diagnosis of atrial fibrillation between 2007 and 2012. We determined the frequency of follow-up care with a family physician, cardiologist or internist within 7 (timely) and 30 days of the emergency department visit, and assessed the association of emergency and family physician characteristics, including primary care model type, with obtaining timely follow-up care. RESULTS: Among 14 907 patients discharged from Ontario emergency departments with a new, primary diagnosis of atrial fibrillation, half (n = 7473) had timely follow-up care. At 30 days, 2678 patients (18.0%) still had not obtained follow-up care. Among emergency and family physician factors, lack of a family physician had the largest independent association with acquiring timely follow-up care (odds ratio [OR] 0.58, 95% confidence interval [CI] 0.50-0.69). Using patients with a family physician belonging to a primarily fee-for-service remuneration model as the comparison group, patients with a family physician belonging to a capitation-based Family Health Network, as part of a Family Health Team, were less likely to receive timely follow-up care (OR 0.73, 95% CI 0.62-0.86), as were those whose family physician belonged to the same model type that was not part of a Family Health Team (OR 0.77, 95% CI 0.60-0.97). INTERPRETATION: Only half of the patients who were discharged from an emergency department in Ontario with a new diagnosis of atrial fibrillation were seen within 7 days of discharge. The most influential factor was having a family physician; patients with a family physician being remunerated via primarily fee-for-service methods were more likely to be seen within 7 days than those who were reimbursed through a primarily capitation model. Systems-wide solutions are needed to ensure timely follow-up care is available for all patients with chronic diseases.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".