The impact of nurse-led atrial fibrillation clinics on patient and healthcare outcomes: a systematic mixed studies review
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation, the most common arrhythmia worldwide, continues to increase as the population ages. Patients with atrial fibrillation, particularly those newly diagnosed or who have multiple comorbidities, have high healthcare utilisation rates. Nurse-led atrial fibrillation clinics have developed to improve care and guidance for atrial fibrillation patients, with the potential to reduce hospital presentations and healthcare utilisation. Atrial fibrillation clinics that provide specialised and patient-centred care have improved patient symptom management, quality of life and reduced healthcare utilisation and costs. AIMS: The aim of this study was to provide the first synthesis of evidence for the impact of nurse-led atrial fibrillation clinics on patient, healthcare utilisation, and quality of care outcomes. METHODS: This systematic mixed studies review examined citations from three databases: Medline, CINAHL and Embase, using the search terms 'atrial fibrillation' and 'clinic', and related concepts. Seventeen moderate to high quality articles were selected. RESULTS: Overall, atrial fibrillation clinics were more cost effective, had shorter wait times and reduced hospitalisation and emergency department visits. Symptoms and sinus rhythm restoration were comparable in the nurse-led compared to physician-led cardioversion clinics. Findings related to patient knowledge and patient satisfaction were mixed, while mortality rates were lower, and patient medication adherence was higher in nurse-led atrial fibrillation compared to usual care. Quality of life and guideline adherence was increased in patients receiving nurse-led atrial fibrillation care compared to usual care. CONCLUSION: Nurse-led clinics improved healthcare, patient and quality care outcomes. Future research might examine the role of technology in delivery of nurse-led clinics in rural/remote areas as well as patient experiences with nurse-led clinics.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| 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".