For millions of patients with congenital heart defects (CHDs), early pediatric interventions are just the start of a lifelong journey. Unfortunately, healthcare providers often navigate this journey without the data they need. Nearly 80% of healthcare data remains unstructured and inaccessible, limiting its use in decision-making. For CHD patients, this creates a “data desert”—a landscape where real-world cardiac data is scarce, fragmented, and inconsistent.
Despite being the most common birth defect world wide, CHD remains misunderstood in real-world clinical settings. This blog post explores why CHD data is so limited and how AI-enabled remote cardiac monitoring has the potential to turn the data desert into a bounty of insights.
Why High-Quality CHD Data Matters
Advancements in surgical and medical management have improved CHD survival rates, but for the 2.4 million Americans currently living with CHD, long-term data gaps continue to create blind spots in patient care. CHD patients face dramatically higher risks of heart failure—220 times higher for children and 8.7 times higher for adults compared to their peers. But heart failure generally develops gradually. With better cardiac monitoring, clinicians may be able to detect subtle warning signs, such as ischemic changes or arrhythmia precursors, long before structural heart damage occurs.
Beyond individual cases, comprehensive CHD datasets could refine predictive models and enable more proactive risk management. So why is high-quality CHD data so scarce? There are three key challenges that contribute to this data deficiency.
The Key Challenges Behind CHD’s Data Deficiency
Challenge 1: Gaps in Care from Pediatric to Adult CHD Management
CHD is a lifelong condition, but medical records often fail to follow patients from infancy to adulthood. Unlike some chronic diseases with centralized tracking, CHD data remains highly fragmented. Despite 40,000 U.S. infants being born with CHD annually, there is no universal registry that tracks lifelong outcomes.
As CHD patients transition from pediatric to adult cardiology, gaps in care emerge. Studies show that 42% of adults with CHD experience care interruptions of over three years, with 8% having gaps exceeding a decade. The same study also found the mean age at the first gap in care was at 19.9 years, which suggests the transition from pediatric to adult care is a critical period for maintaining follow-up. These lapses increase the risk of undiagnosed complications, hospitalizations, and subpar outcomes. Poor interoperability between pediatric and adult cardiology systems further compounds this problem, making it difficult for providers to access comprehensive patient histories.
Additionally, many adult cardiologists specialize in acquired heart disease (like coronary artery disease) and lack training in congenital conditions. This knowledge gap can lead to suboptimal treatment and delayed interventions. The result? Many CHD patients experience years of “data darkness,” where key clinical changes go unnoticed until they cause acute complications.
Challenge 2: Lack of Longitudinal Data Tracking
CHD care often relies on episodic testing, such as brief ECGs or short-term Holter monitors, which capture only limited snapshots of heart function. Standard ECGs last just a few minutes, and Holter monitors cover only 24 to 48 hours—insufficient timeframes for tracking CHD’s evolving nature.
CHD management isn’t just about what happens in a doctor’s office; it’s about how the heart responds to daily life. Without continuous, real-world data, clinicians can struggle to detect early signs of deterioration. And a lack of baseline comparisons makes it even harder to assess whether a patient’s condition is stable or worsening over time.
Challenge 3. Underrepresentation in Clinical Research & High Costs of Care
CHD is one of the most underrepresented conditions in cardiovascular research. Wide anatomical variations, differing surgical histories, and long-term complications make standardized studies difficult. As a result, most research focuses on childhood outcomes, leaving long-term risks—like arrhythmias, heart failure, and stroke—poorly understood.
CHD also carries a heavy financial burden, with hospital costs exceeding $9.8 billion annually. Many cost-reduction strategies focus on adult cardiovascular disease rather than CHD, despite its unique challenges. Reducing these costs requires better data collection and AI-driven insights to prevent unnecessary hospitalizations and improve patient outcomes.
How AI is Transforming CHD Data Collection
AI-enabled remote cardiac monitoring could offer a breakthrough solution to the gaps in care left by the lack of real-world data. By generating near real-time data and using AI to analyze it, this technology may be able to enhance clinical oversight and enable proactive interventions—without adding to clinicians’ workloads. Here’s how AI has the potential to close the CHD data gap:
Solution 1. Bridge the Gap Between Childhood and Adulthood
AI-powered monitoring provides continuous cardiac data, which could help ensure a seamless transition from pediatric to adult care. Instead of fragmented medical records, AI-driven systems may be able to centralize patient data, giving cardiologists a comprehensive view of a patient’s lifelong cardiac history.
Additionally, AI may standardize data across different EHR formats, improving interoperability between pediatric and adult cardiology. This would allow for smoother data sharing and better continuity of care. Remote patient monitoring (RPM) can help CHD specialists to track patients from a distance, reducing the need for in-person visits and expanding access to expert care—especially for those in rural areas.
Solution 2: Unlock Longitudinal CHD Insights
AI-enabled remote cardiac monitoring could transform CHD management by shifting from episodic testing to continuous tracking. Unlike traditional episodic testing, AI-enabled remote monitoring captures continuous heart data, revealing patterns that might otherwise go unnoticed. For example, subtle fluctuations in heart rate trends could indicate early-stage deterioration, or changes in nocturnal heart rate variability might signal worsening heart function.
Research suggests AI-driven learning algorithms could improve imaging efficiency, enhance diagnostic accuracy, and provide early warnings of heart dysfunction. By replacing reactive care with proactive, data-driven insights, AI-enabled RPM may one day be able to improve long-term outcomes for CHD patients.
Solution 3: Scale High-Quality Data for CHD Research & Cost Reduction
Traditional CHD research is limited by small sample sizes and fragmented datasets, which makes it challenging to draw definitive conclusions about long-term outcomes and treatment efficacy. AI-enabled monitoring may be able to overcome this by collecting real-world cardiac data across entire patient populations, allowing researchers to analyze disease progression, surgical outcomes, and treatment effectiveness at scale.
AI could also help reduce costs across the healthcare landscape. Studies show that late detection of CHD complications leads to significantly higher hospital admissions, inpatient days, and healthcare costs. One study of infants diagnosed with CHD found that detection was significantly associated with 52% more hospital admissions, 18% more hospitalized days, and 35% higher inpatient costs. By identifying high-risk patients earlier, AI-driven monitoring may be able to help cardiologists make timely interventions, preventing hospitalizations and reducing financial strain on both patients and healthcare systems.
From CHD Data Desert to Data-Driven Future
For too long, CHD care has been hindered by fragmented data, gaps in long-term monitoring, and limited research. This has led to missed diagnoses, preventable hospitalizations, and an unclear understanding of long-term outcomes. AI-powered remote cardiac monitoring has the potential to change this reality by enabling continuous, scalable, and high-quality data collection.
With deeper insights, earlier interventions, and better long-term tracking, the future of CHD care is no longer defined by gaps and guesswork—it’s being rewritten by data. For CHD patients, the data desert may one day become an oasis of information.
Contact us today to learn how InfoBionic.Ai’s AI-enabled remote cardiac monitoring closes data gaps.