Remote Monitoring of Cardiovascular Implantable Electronic Devices in Canada: Survey of Patients and Device Health Care Professionals
Bibliographic record
Abstract
BACKGROUND: Remote monitoring is used to supplement in-clinic follow-up for patients with cardiac implantable electronic devices (CIEDs) every 6-12 months. There is a need to optimize remote management for CIEDs because of the consistent increases in CIED implants over the past decade. The objective of this study was to investigate real and perceived barriers to the use of remote patient management strategies in Canada and to better understand how remote models of care can be optimized. METHODS: We surveyed 512 CIED patients and practitioners in 22 device clinics in Canada. RESULTS: Device clinic surveys highlighted significant variation and inconsistency in follow-up care for in-clinic and remote visits across and within clinics. This survey showed that funding policies and management of additional workflow are barriers to optimal use and uptake. Despite this, device clinics perceive remote follow-up as a valuable resource and an efficient way to manage patient follow-up. Patients were broadly satisfied with their CIED follow-up care but identified barriers related to coordination of care, visit logistics, and information needs. Views varied as a function of clinical or sociodemographic characteristics. Most patients (n = 228; 91%) expressed a desire to receive a phone call from their device clinic after a remote transmission has been received. CONCLUSIONS: Lack of a unified, guideline-supported approach to follow-up after CIED implant, and discrepant funding policies across jurisdictions, are significant barriers to the use of remote patient management strategies in Canada. Efforts to increase or expand use of remote follow-up must recognize these barriers and the needs of specific subgroups of patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".