iRhythm, Lucem Health Team Up on Predictive AI Solution for Early Arrhythmia Detection 

iRhythm Technologies Inc. has forged a strategic partnership with Lucem Health to provide earlier identification of undiagnosed cardiac arrhythmias in patient populations with elevated arrhythmias risk.

“Healthcare is entering an era where the goal is no longer just to detect disease, but to predict it,” said iRhythm President/CEO Quentin Blackford said. “Together with Lucem Health, iRhythm is helping lead a new way forward in care, with the goal of reaching patients before symptoms surface and before complications arise. This is a bold step toward predictive, preventive, and precise care powered by AI, informed by data, and designed for scale. We believe more than 27 million people in the U.S. alone could benefit from proactive cardiac monitoring¹—and this is just the beginning.”

Traditional care models often rely on reactive diagnosis and can leave arrhythmias undetected until stroke, hospitalization, or worse. This partnership brings together Lucem Health’s Reveal artificial intelligence (AI)-powered early disease detection platform and iRhythm’s diagnostic service to shift that paradigm. By identifying risk earlier and enabling targeted cardiac monitoring, clinicians can potentially intervene sooner, improve outcomes across patient populations with elevated arrhythmia risk, and support scalable, data-driven strategies for health systems focused on value-based care.

“Each day, clinicians find themselves reacting to the circumstances of the patients in their exam rooms — they often don’t have the time or the information they need to deliver truly proactive care,” Lucem Health Founder/CEO Sean Cassidy stated. “Together with iRhythm, we’re bringing predictive intelligence to healthcare’s front lines—enabling earlier action, smarter resource allocation, and better outcomes for patients.”

A Predictive AI Solution for Smarter, Earlier Arrhythmia Detection

The first commercial offering from the collaboration is an exclusive, AI-powered solution that analyzes subtle patterns in clinical and electronic health record (EHR) data to help identify elevated arrhythmia risk in individuals with Type 2 diabetes (T2D), chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), and coronary artery disease (CAD)—individuals who may otherwise not be flagged as candidates for ambulatory cardiac monitoring. This offering enables healthcare organizations to proactively pinpoint patients with one or more specifically diagnosed or undiagnosed clinical conditions who could benefit from earlier cardiac monitoring and intervention.

Once identified, appropriate patients can be monitored using iRhythm’s clinically proven Zio ECG monitors and service. The Zio ECG device is worn for up to 14 days, enabling continuous, uninterrupted heart rhythm monitoring, and the end-to-end service, powered by an advanced FDA-cleared AI algorithm, delivers actionable insights, reviewed and curated by qualified cardiac technicians, to help clinicians make the right diagnosis the first time and support timely care.

By integrating predictive AI, the new offering builds on iRhythm’s existing proactive monitoring programs deployed with healthcare systems focused on population health management and should enable even earlier arrhythmia risk identification and targeted intervention. The solution is designed to support accountable care organizations (ACOs), integrated health systems, payviders, and other managed care organizations that take on financial responsibility for the cost and quality of care as they pursue scalable value-based care strategies.

Early pilot testing conducted by iRhythm, in collaboration with Lucem Health, suggests promising improvement in targeting patient populations with elevated arrhythmia risk and enabling earlier clinical engagement with greater precision. Both organizations anticipate that use of the AI-powered predictive tool will increase arrhythmia detection among an estimated 27 million undiagnosed U.S. patients,¹  helping reduce healthcare resource utilization (HCRU) and costs, and improve patient outcomes.

The Cost of Missed Arrhythmias and the Case for Earlier Detection

Cardiac arrhythmias, conditions in which the heart beats too fast, too slow, or irregularly,² affect roughly one in 20 U.S. adults.³ Left undetected and untreated, they can lead to stroke, heart failure, hospitalization, or death,⁴ making early identification and intervention critical. Yet in many care pathways for individuals with T2D and/or other comorbid conditions, arrhythmias are not routinely screened, despite elevated risk.⁵

A growing body of evidence highlights the opportunity to detect arrhythmias earlier in at-risk populations—particularly around key clinical turning points in disease progression, as seen in recent data on patients with T2D.

New research presented at the American Diabetes Association’s 85th Scientific Sessions (ADA 2025), based on a real-world study of more than 30 million U.S. adults, found that arrhythmias—often asymptomatic—frequently cluster around key moments in disease progression, particularly in T2D patients. Many arrhythmias were identified just before or shortly after diagnoses of CKD or major adverse cardiovascular events (MACE) such as stroke or heart failure.⁶

Additional findings reinforce the broader clinical and economic impact of arrhythmias across chronic conditions like T2D and COPD, and the value of earlier detection and monitoring.

Data presented at the American Heart Association’s 2024 Scientific Sessions revealed that patients with T2D and/or COPD who develop arrhythmias experience up to twice as high hospitalization rates, 35%–50% higher emergency care costs, and $46,000 average annual healthcare expenses compared to $30,000 for those without arrhythmias.⁷

Adding to this evidence, new research presented at the American Thoracic Society (ATS) International 2025 conference, based on a real-world study of more than 2.5 million U.S. adults, found that COPD patients with arrhythmia have greater HCRU and costs compared to those without the condition. However, among COPD patients with arrhythmia, those who were monitored had lower HCRU and costs compared to those who were never monitored.⁸

Together, these findings underscore the clinical and economic impact of earlier, smarter detection—and the opportunity for predictive solutions like this initial offering from the iRhythm–Lucem Health collaboration to support value-based care models—reduce unnecessary healthcare utilization, and improve outcomes at scale for patient populations with elevated arrhythmia risk.

U.S.-based innovative care delivery organizations, accountable care organizations (ACOs), integrated health systems, payviders, and other managed care organizations that assume financial responsibility for the cost and quality of care can learn more about the predictive AI solution⁹ and the way(s) it could support their population health strategies and value-based care goals by visiting iRhythm’s Value-Based Care page to connect with the iRhythm team.

iRhythm is a digital health care company that creates trusted solutions that detect, predict, and prevent disease. Combining its Zio wearable biosensors and cloud-based data analytics with powerful proprietary algorithms, iRhythm distills data from millions of heartbeats into clinically actionable information. Through a relentless focus on patient care, Rhythm aims to deliver better data, better insights, and better health for all.

Lucem Health helps healthcare providers accelerate disease detection and treatment using practical, responsible AI, so they can improve patients’ lives and increase the clinical and financial yield from care delivery resources.

References
¹ iRhythm internal estimate based on analysis of public and proprietary sources, including U.S. Census Bureau data, CDC healthcare utilization data, Medicare Public Use Files, IQVIA, Komodo Health, Definitive Healthcare, and peer-reviewed literature on arrhythmia prevalence, symptom presentation, and diagnostic pathways. Full source list available upon request.
² What is an arrhythmia? National Heart Lung and Blood Institute, 2022. https://www.nhlbi.nih.gov/health/arrhythmias.
³ Desai et al. Arrhythmias. StatPearls [Internet], 2023. https://www.ncbi.nlm.nih.gov/books/NBK558923/
⁴ Ataklte et al. Meta-analysis of ventricular premature complexes and their relation to cardiac mortality in general populations. The American Journal of Cardiology, 2013.
⁵ Bhave, P. D., & Soliman, E. Z. (2024). Should patients with diabetes be routinely screened for atrial fibrillation? Expert Review of Cardiovascular Therapy, 22(1–3), 5-6. https://doi.org/10.1080/14779072.2024.2328645
⁶ Russo P, Nathan R, Pfeffer D, Kamdar S, Wright B, Boyle K. Incidence and Timing of Major Arrhythmias in T2D and CKD: A Real-World Analysis [poster presentation]. Presented at: American Diabetes Association (ADA) 85th Scientific Sessions; June 20–23, 2025; Chicago, IL, USA.
⁷ Russo P, Nathan R, Pfeffer D, Kamdar S, Wright B, Boyle K. Real-World Evidence on Health Care Resource Utilization and Economic Burden of Arrhythmias in Patients with Diabetes and COPD [poster presentation]. Presented at: American Heart Association (AHA) Scientific Sessions; November 16-18, 2024; Chicago, IL. Available at: https://s205.q4cdn.com/296879096/files/doc_events/2024/11/1/120-001752-003_DA_2024-AHA-Poster-iRhythmtech_RWE-HCRU-Arrhythmias_v2.pdf
⁸ Russo P, Nathan R, Pfeffer D, Poh J, Jha V, Singh H, Wright B, Boyle K. Real-World Evidence on Health Care Resource Utilization and Economic Burden of Arrhythmias in Patients with COPD [poster presentation]. Presented at: American Thoracic Society (ATS) 2025 International Conference; May 16–21, 2025; San Francisco, CA, USA.
⁹ The predictive-AI solution does not represent the functionality of any Zio branded medical device.