Remote Patient Monitoring and Artificial Intelligence

EP: 1.Clinical Presentation of Arrhythmias

EP: 2.Long-Term Consequences of Arrhythmias

EP: 3.Common Comorbidities and Underdiagnosis Associated With Atrial Fibrillation

EP: 4.Unmet Needs in the Management of Atrial Fibrillation

EP: 5.Optimizing the Management of Atrial Fibrillation Through Team-Based Care

EP: 6.Best Practices for Diagnosing Arrhythmia

EP: 7.Prescribed Ambulatory Cardiac Rhythm Monitors

EP: 8.Electronic Health Records and Consumer Wearables

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EP: 9.Remote Patient Monitoring and Artificial Intelligence

EP: 10.The Potential Role of Screening for Atrial Fibrillation

Remote patient monitoring (RPM) goes beyond simply tracking heart rhythms; it plays a vital role in managing symptoms, assessing treatment effectiveness, and preventing complications. For RPM to be cost-effective and clinically meaningful, it requires a structured system with appropriate tools and a dedicated care team that actively reviews and acts on the incoming data. Without this feedback loop, monitoring devices can generate data that clinicians don’t have the time or resources to manage, reducing the potential benefits. Specifically in arrhythmia management, RPM can help evaluate treatment success, monitor for recurrence after interventions like ablation, and support comprehensive disease management by integrating risk factor control, medication adherence, and symptom tracking.

The integration of RPM in cardiology is evolving, drawing lessons from other fields like diabetes care, where continuous glucose monitoring (CGM) has become a standard, patient-centered approach. CGM devices provide real-time data that patients and care teams use actively, fostering better outcomes through education and engagement. Similarly, wearable heart rhythm monitors can empower patients to capture and share meaningful data, helping reduce anxiety and guide timely care decisions. However, the implementation of RPM requires new workflows and interprofessional collaboration to maximize its benefits, balancing data collection with meaningful clinical use.

Artificial intelligence (AI) is poised to transform RPM by helping to sift through large volumes of data and delivering actionable insights directly to clinicians. AI-driven clinical decision support systems could flag important changes and suggest next steps, reducing the burden on busy providers. While promising, these technologies need more research and thoughtful integration to ensure they complement, rather than replace, clinical judgment. Ultimately, combining advanced technology with human expertise will be essential to realize the full potential of RPM in improving patient care.