Patients’ Experiences With the Fit of Virtual Atrial Fibrillation Care During the Pandemic: Qualitative Descriptive Study
Bibliographic record
Abstract
BACKGROUND: In-person health care has been the standard model of care delivery for patients with atrial fibrillation (AF). Despite the growing use of remote technology, virtual health care has received limited formal study in populations with AF. Understanding the virtual care experiences of patients in specialized AF clinics is essential to inform future planning of AF clinic care. OBJECTIVE: This qualitative descriptive study aimed to understand patients' virtual AF clinic care experiences during the COVID-19 pandemic. METHODS: Participants were recruited from a pool of patients who were receiving care from an AF clinic and who were enrolled in a larger survey study. A total of 8 virtual focus groups (n=30) were conducted in 2 waves between March 2021 and May 2021. Facilitators used a semistructured discussion guide to ask participants questions about their experiences of virtual care and the perceived quality of virtual care and technology support. Three team members initially open coded group data to create a preliminary coding framework. As the analysis progressed, with subsequent focus groups, the code clusters were refined. RESULTS: The participants were primarily male (21/30, 70%), aged ≥65 years (20/30, 67%), and college graduates (22/30, 73%). Patients found virtual care to be highly beneficial. Central to their experiences of virtual care was its fit or lack of fit with their health needs, which was integrally connected to communication effectiveness and their preferred virtual care future. Practical benefits included flexibility, convenience, and time and cost savings of virtual care. Virtual care fit occurred for small, quick, and mundane issues (eg, medication refills) but was suboptimal for new and more complex issues that patients thought warranted an in-person visit. Fit often reflected the effectiveness of communication between patient and provider and that of in-clinic follow-up. There was near-complete agreement among participants on the acceptability of virtual communication with their providers in addressing their needs, but this depended on adequate reciprocal communication. Without the benefit of in-person physical assessments, patients were uncertain and lacked confidence in communicating the needed, correct, and comprehensive information. Finally, participants described concerns related to ongoing virtual care with recommendations for their preferred future using a hybrid model of care and integrating patient-reported data (ie, blood pressure measurements) in virtual care delivery. CONCLUSIONS: Virtual care from a specialty AF clinic provides practical benefits for patients, but they must be weighed against the need for virtual care's fit with patients' needs and problems. The stability and complexity of patients' health needs, their management, and their perceptions of communication effectiveness with providers and clinics must be considered in decisions about appointment modality. Patients' recommendations for future virtual care through use of hybrid models together with systems for data sharing have the potential to optimize fit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".