International survey of cardiac device clinic staff determining patient-perceived challenges with remote monitoring
Bibliographic record
Abstract
BACKGROUND: Regular interaction with patients with cardiac implantable electronic devices (CIEDs) provides CIED clinic personnel with unique insights into patient-related barriers and challenges to remote monitoring (RM) implementation. METHODS: Using a global network, an international survey was administered to CIED clinic personnel. Qualitative questions gathered information on perceived challenges with patient connectivity and patient-level barriers associated with RM implementation. RESULTS: A total of 339 responses from 302 unique centres were included in the analysis. Respondents most often were cardiac electrophysiologists (57.8%), followed by nurses (17.1%) and nurse practitioners (7.7%). Most respondents (84.7%) reported at least one challenge in daily RM management. The biggest challenge was the increasing data volume and data variability (38.3%), followed by staff shortage (33.8%), difficulties with billing for technical activities (20.2%), and insufficient RM reimbursement (16.0%). Patient connectivity was identified as a major barrier by 72.7% of respondents. The main concerns were patient literacy (80.8%), followed by a lack of patient understanding of RM importance (65.8%), the lack of required bandwidth or technology to support RM (49.6%), and the cost to the patient (47.9%). Subgroup analyses showed that patient connectivity was often identified as a major barrier by non-MD CIED clinic staff, respondents working in office-based CIED clinics, and respondents from clinics with a per-patient payment reimbursement model. CONCLUSIONS: There are many patient-related barriers identified by non-MD CIED clinic staff. Dedicated strategies to optimise the utilisation and adherence of RM of patients with CIEDs are urgently needed to improve clinical outcomes.
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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) | 0.001 | 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".