Background: Remote monitoring (RM) of insertable loop recorders (ILRs) generates high volumes of transmissions, often increasing clinic workload. We evaluated the impact of cloud-based AccuRhythm™ AI algorithms combined with structured nursing oversight on transmission volume and clinical workload. Methods: Retrospective analysis of 103 consecutive patients implanted with LINQ II ICMs at Ospedale Annunziata, Cosenza, monitored between 2022 and 2025. Nursing staff programmed individualized alert settings and performed primary triage of transmitted events. AccuRhythm filtering was applied via CareLink; transmitted events were reviewed by the nursing team and supervising electrophysiologists. Outcomes were reduction in false alerts, number of eliminated alerts, and estimated nursing time saved. Results: The combined approach produced substantial reductions in non-actionable alerts: pause alerts decreased by ~99% and AF alerts by ~50%, yielding 1,782 fewer false alerts during the study period. Time-motion estimates indicate a conservative nursing time saving of 336 hours overall (≈100 hours/year), equivalent to ~6% of a full-time nurse equivalent. Nurse expertise was critical for personalized alert tuning, ensuring preservation of clinically relevant transmissions and appropriate escalation. Conclusion: In this real-world single-center experience, AI-enhanced filtering paired with dedicated nursing management markedly reduced non-actionable transmissions and decreased clinical workload while maintaining diagnostic yield. Optimal RM for ILRs requires integration of advanced algorithms and continuous nurse-driven parameter optimization to maximize efficiency and patient safety.
Associazione Nazionale Medici Cardiologi Ospedalieri
