Cardiovascular disease (CVD) remains a leading — and growing — cause of morbidity and mortality worldwide, with the economic burden of care projected to skyrocket over the coming decades.
Recent findings on heart failure published by the Heart Failure Society of America reveal a rise in the prevalence, mortality, and impact of this condition in the United States. 1 And a recent study estimates that by 2050, the total costs associated with cardiovascular disease in the U.S. will surpass $1 trillion annually, placing unprecedented strain on healthcare systems. 2 Early detection and intervention remain the most effective strategies for mitigating these costs for this growing problem, yet systemic barriers often delay diagnosis and treatment.
AI-enabled electrocardiogram (ECG) technology has the potential to offer a transformative approach to addressing these challenges. By leveraging artificial intelligence (AI) to rapidly analyze and interpret near real-time cardiac monitoring data, healthcare providers may be able to detect potential issues sooner, intervene earlier, and support improved outcomes — all while driving down the mounting cost burden of CVD.
The Cost of CVD: Earlier Is Always Better
Cardiovascular disease is not only the leading cause of death globally, but also one of the most cost-intensive conditions. According to the American Heart Association, total cardiovascular disease-related costs in the United States are projected to triple to $1.8 trillion by 2050. 3 This includes direct medical expenses, such as hospitalizations, procedures, and medications, as well as indirect costs like lost productivity and long-term disability care.
On an individual level, the average cost per patient with heart failure is estimated to be approximately $24,383 annually. 4 Much of this financial burden is driven by acute episodes requiring emergency intervention, frequent hospital readmissions, and intensive long-term management. Most of these costs stem from treating disease at advanced stages when options are fewer, interventions are more expensive, and outcomes are poorer.
The solution is prevention and early intervention. When cardiac issues are detected and managed early — before they progress to severe or chronic stages — patients may experience better health outcomes, and the healthcare system can avoid the costliest interventions. While the solution to reducing costs has long been clear, the path to achieving it has not been. Several complex barriers stand in the way of proactive, early-stage CVD diagnosis.
The Systemic Challenge: Barriers to Preventive Cardiac Care
Despite the increasing availability of advanced diagnostic tools, numerous systemic barriers continue to hinder the shift from reactive to preventive cardiac care. Patients are commonly diagnosed after symptoms become acute — by which time treatment is more complex, costly, and less likely to succeed.
One challenge is the limited access to continuous, high-quality cardiac monitoring, especially in underserved or rural populations. Furthermore, common issues like intermittent snapshots, false alarms, clinical overload, fragmented care coordination, and data interpretation difficulties also contribute to missed opportunities for early detection.
Heart failure, in particular, often remains undetected until patients experience severe exacerbations. A survey by the American College of Cardiology found that only 62 percent of patients had been diagnosed with heart failure prior to their hospitalization for acute decompensated heart failure. 5
The median time from symptom onset to diagnosis can be several months, during which time the condition may worsen and become more difficult to treat. In fact, for an individual with newly developed heart failure, the timeline for diagnosis can take as long as 30 months from the initial onset of clinical symptoms. 6 These diagnostic delays not only reduce the chances of successful intervention but also drive up healthcare costs due to emergency care, extended hospital stays, and longterm medication use.
While initiatives like the American Heart Association’s heart failure improvement campaign, for example, aim to close care gaps, 7 the reality is that without scalable, integrated solutions capable of supporting early diagnosis and continuous monitoring, patients will continue to fall through the cracks. The need for preventive-focused infrastructure has never been more urgent.
How AI-Powered Remote Cardiac Monitoring Solves the Problem
AI-enabled ECG technology, frequently referred to as remote cardiac monitoring, has the potential to address the persistent challenges in cardiac care by offering solutions that enhance detection, reduce costs, and improve patient outcomes. Following are five ways AI-powered remote cardiac monitoring could mitigate the issues contributing to delayed cardiac diagnosis.
From Intermittent Snapshots to Continuous Monitoring. Traditional monitoring methods rely on intermittent snapshots of heart activity, which often miss critical abnormalities and lead to delayed diagnoses and more costly interventions down the line. AI-enabled RPM platforms, however, could provide continuous, high-fidelity monitoring, capturing a rich dataset over extended periods.
By detecting transient yet clinically significant events — such as brief arrhythmias — earlier, clinicians may be able to take action before conditions worsen, helping reduce costly emergency visits, hospital admissions, and invasive surgical procedures. In fact, remote patient monitoring has been associated with significantly lowering 30-day readmission rates (10 percent) compared to traditional care (23 percent). 8
From Manual Interpretation to AI-Powered Insight. Unlike manual data interpretation, which can be prone to human error, delays, or missed diagnoses, AI has the potential to offer immediate, actionable insights that may improve diagnostic accuracy. AI algorithms could excel at rapidly analyzing complex cardiac data to identify patterns indicative of cardiac events. One study showed that an AI-enabled ECG correctly identified subtle patterns of atrial fibrillation (AFib) with 90 percent accuracy. 9
Faster, more accurate detection allows for more timely intervention, potentially reducing the need for expensive acute care and lowering the long-term costs associated with advanced disease progression.
From False Alarms to High-Fidelity Diagnoses. False alarms are a common problem that plagues monitoring systems, 10 and a major cost driver in cardiac care are the follow-up tests and treatments prompted by these false alarms. AI models that are trained on vast datasets, however, could filter out benign irregularities and noise, ensuring that clinicians are alerted to the most meaningful anomalies.
This kind of precision may be able to minimize unnecessary tests, consultations, and interventions, mitigating the overutilization of resources and reducing patient burden. One study, for example, showed that an AI system was able to eliminate more than two-thirds (approximately 70 percent) of AFib false positives. 11
From Post-Symptomatic Intervention to Predictive, Preventive Care. Intervention often happens after an event, when it may be too late. AI has the potential to go beyond near real-time detection and could anticipate adverse cardiac events such as AFib or heart failure exacerbations by analyzing long-term trends.
Predictive insights could enable providers to act before conditions escalate, e.g., by prescribing medication adjustments, lifestyle interventions, or closer monitoring. Proactive care not only may improve a patient’s quality of life but might also prevent expensive hospitalizations, emergency care episodes, and chronic disease complications, offering substantial long-term savings. This care makes a difference: according to reports, approximately 80 percent of premature heart disease and strokes are actually preventable through early intervention and risk management. 12
From Clinical Overload to Reduced Burden. According to a 2024 report, ~81 percent of providers say they’re overworked. 13 But technology can help by eliminating tedious tasks and freeing up more time for patient care. The adoption of digital health tools among physicians is growing, and they report improved clinical outcomes and work efficiency as the top factors influencing their interest. 14
Many AI-powered platforms are being designed to integrate smoothly with existing clinical workflows and electronic health record (EHR) systems. With user-friendly interfaces and seamless interoperability, clinicians could use these tools without disruption or additional administrative burden. This streamlined approach may reduce time spent on manual data review and documentation, saving operational costs and improving staff efficiency.
Putting AI-Enabled Remote Cardiac Monitoring into Practice
Scalability and seamless integration are essential for AI-enabled remote cardiac monitoring to deliver its full benefits. Implementing these technologies across healthcare systems requires compatibility with existing infrastructure and workflows to ensure ease of use and foster broad adoption.
When properly integrated, AI-powered monitoring has the potential to extend advanced diagnostic capabilities beyond traditional hospital settings, making high-quality care accessible to underserved populations. By enabling continuous, remote monitoring, these systems are able to bridge critical gaps in care and enhance patient outcomes. 15
Furthermore, AI-enabled remote cardiac monitoring may improve diagnostic accuracy, reduce unnecessary hospital readmissions, and lower the need for emergency interventions. Predictive capabilities could support preventive care, helping clinicians address potential issues before they escalate. This efficiency may ultimately lead to substantial cost savings and better patient outcomes.
To support the integration and scalability of AI-enabled remote cardiac monitoring, providers may want to consider performing the following tasks:
Conduct readiness assessments to understand the current infrastructure and identify gaps.
Prioritize solutions that are interoperable with existing EHRs and devices.
Train clinical staff to build familiarity and trust in new AI tools.
Establish standardized protocols to guide when and how AI insights are acted upon.
Collaborate with technology partners to ensure compliance, cybersecurity, and ongoing support.
As healthcare systems continue to confront rising costs associated with CVD, AI-enabled remote cardiac monitoring could offer a practical, scalable solution to enhance care quality while reducing expenditures.
The Future of CHD Looks Brighter with AI-Enabled Technologies
The escalating costs and prevalence of cardiovascular disease demand innovative solutions that can enhance care quality while alleviating financial burden. AI-enabled ECG technology may offer a compelling and actionable response to these challenges.
By providing clinicians with near real-time insights and empowering them to detect potential issues sooner, AI-driven platforms may be able to simultaneously address the challenge of poor outcomes along with the issue of outsized costs. As the healthcare landscape continues to evolve, adopting these technologies may be critical for achieving better, more efficient care. Now is the time for healthcare providers to think about integrating scalable, AI-powered solutions to potentially meet the needs of a growing population at risk for cardiovascular disease. Finally, solving the pervasive CVD problem might become much more achievable.
This article was written by Stuart Long, CEO of InfoBionic.Ai, Chelmsford, MA. For more information, visit here .
References
- HF Stats 2024: Heart Failure Epidemiology and Outcomes Statistics. Journal of Cardiac Failure.
- U.S. cardiovascular disease costs set to skyrocket by 2050, new study reveals. News-Medical.net.
- Population shifts, risk factors may triple U.S. cardiovascular disease costs by 2050. American Heart Association.
- A Systematic Review of Medical Costs Associated with Heart Failure in the USA (2014-2020). Pharmacoeconomics.
- Acute Decompensated Heart Failure: The Need For The Patient’s Perspective. Cardiology Magazine.
- Hayhoe B, Kim D, Aylin PP, Majeed FA, Cowie MR, Bottle A. Adherence to guidelines in management of symptoms suggestive of heart failure in primary care. Heart.
- American Heart Association launches initiative to improve heart failure care. News-Medical.net.
- Bhatia A , Maddox T. Remote Patient Monitoring in Heart Failure: Factors for Clinical Efficacy. NIH.
- Mayo Clinic study shows AI could enable accurate, inexpensive screening for atrial. Mayo Clinic.
- Drew B, Harris P, Zègre-Hemsey, J, et al. Insights into the Problem of Alarm Fatigue with Physiologic Monitor Devices: A Comprehensive Observational Study of Consecutive Intensive Care Unit Patients. NIH.
- Mittal S , Oliveros S , Li J , Barroyer T , Henry C , Gardella C. AI Filter Improves Positive Predictive Value of Atrial Fibrillation Detection by an Implantable Loop Recorder. JACC.
- Vital Signs: Preventing 1 Million Heart Attacks and Strokes. CDC.
- Physician Compensation Report 2024.
- AMA: Physicians propelling health care’s digital transformation. AMA.
- Prasad Vudathaneni V , Brahmam Lanke R , Chinnadurai Mudaliyar M , Varaprasad Movva K , et al. The Impact of Telemedicine and Remote Patient Monitoring on Healthcare Delivery: A Comprehensive Evaluation.
