Cost-effectiveness of remote monitoring for cardiac implantable electronic devices compared with conventional follow-up: a systematic review
Bibliographic record
Abstract
Abstract Background Remote monitoring (RM) of cardiovascular implantable electronic devices (CIEDs) is a form of virtual patient care that involves electronic transmission of CIED diagnostics and remote assessment of this information by clinic staff. Despite expert recommendations advocating its use, adoption remains modest due to inconsistent funding policies across health systems. Purpose This study aims to identify and synthesize existing literature in the cost-effectiveness of RM compared with in-person clinic assessments alone in patients with CIEDs. Methods We conducted a systematic review of available economic evaluations (including cost-effectiveness, cost-utility, cost-consequence analyses) and costing analyses (which examines costs but not clinical benefits) of RM following CIED implantation compared to in-person clinic follow-up alone. Study outcomes included incremental costs, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratio (ICER). Results Of the 1151 unique citations, a total of 27 studies were included. The studies were from Europe (n=20), USA (n=5), or Canada (n=2). Remote transmissions, when detailed in the study, were predominantly patient- and/or physician- triggered, but some also included automatic home monitor transmissions. Fourteen studies (52%) were costing analyses, and the remaining 13 studies were economic evaluations. The majority of studies (78%; n=21) reported cost-savings associated with RM compared to in-clinic follow up alone. Of the 13 economic evaluations, there were 6 cost-utility analyses, which all reported that RM provided additional QALYs for additional costs that met country-specific thresholds for good value in health care. Studies varied greatly in the costs considered, the outcomes measured, and the time horizons used. Conclusion Remote monitoring of patients with CIEDs was associated with cost-savings in most studies and across different healthcare systems. RM may be considered cost-effective when conventional thresholds for good value in health care are adopted. Despite heterogeneity in methods of economic evaluation in the studies included, the overall data in this systematic review supports greater implementation of RM technology to improve health system costs and efficiency.
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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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".