Atrial fibrillation care in rural communities: a mixed methods study of physician and patient perspectives
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) is a serious heart arrhythmia associated with devastating outcomes such as stroke. Inequitable rural AF care may put patients at risk. Virtually delivered specialty AF care offers a viable option, but stakeholder perceptions of this option within the context of rural AF care is unknown. The study purpose was to obtain patient and primary care physician perspectives of rural AF care and virtually delivered AF care as a potential option. METHODS: Using a mixed methods design, AF patients (n = 101) and physicians (n = 15) from three rural communities participated in focus groups and/or surveys. Focus group data were thematically analyzed, survey data were descriptively analyzed, and data were triangulated. RESULTS: Findings captured patients' and physicians' perceptions of prioritized, needs, concerns and problems in AF management, available/unavailable services, and their ideas about virtual AF care. Patients and physicians identified eclectic problems in managing AF. Overall, patients felt ill informed about managing their AF and their most salient problems related to fatigue, exercise intolerance, weight maintenance, sleep apnea, and worry about stroke and bleeding. Physicians found treating patients with co-morbidities and cognitive decline problematic and balancing risks related to anticoagulation challenging. Patients and physicians identified education as a pressing need, which physicians lacked time and resources to meet. Despite available rural services, access to primary and cardiology care was a recurring challenge, and emergency department (ED) use highly contentious but often the only option for accessing care. Physicians' managed AF care and varied in the referrals they made, often reserving them for complex situations to avoid patient travel. Patients and providers supported a broad approach to virtual AF care, tailored to an inclusive rural patient demographic. CONCLUSIONS: The study offered valuable physician and patient perspectives on AF care in rural communities including diverse management challenges, gaps in access to primary and specialty services that made ED an often used but contentious option. Findings point to the potential value of virtual care designed to reach patients with AF across the spectrum and geared to local contexts that preserve the vital role of primary care physicians in AF care in their communities.
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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 | 0.000 | 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".