The Unsung Hero: Unpacking the Critical Role of High-Integrity Data in Virtual Cardiac Telemetry 

In the last decade, healthcare has made quantum technological advancements. As the industry transforms from cautious laggard to innovative epicenter, the spotlight often falls on the ‘shiny new object’: the next big breakthrough that will pave the way to progress. But as we peel back the layers of progress, we uncover an unsung hero that impacts almost all areas of healthcare innovation: high-quality data.

As we enter a new era of medicine, an era in which AI will decipher the most cryptic conditions and personalized medicine will tailor treatments to the human genome, the significance of data quality will expand exponentially. Erroneous data can result in critical insights flying under the radar—or worse, lead even the most advanced algorithm astray.

“Garbage in, garbage out” comes to mind, but that statement doesn’t quite capture the implications of poor-quality data in healthcare—where patient lives are on the line. According to a recent NIH report, inaccurate or incomplete healthcare data is a major contributor to medical errors, estimated to cause over 250,000 U.S. deaths per year. While its human cost is high, poor data quality has a financial impact, too. Healthcare workers reportedly spend ~50% of their time dealing with data quality issues, which costs the U.S. healthcare system more than $1 trillion (about $3,100 per person) annually.

Why Data Integrity is Central to the Future of Cardiac Care

Data quality can make or break outcomes in remote patient monitoring (RPM), particularly in the cardiac specialty. While convenient, remote cardiac monitoring has historically lacked the precision and granularity of hospital-collected in-person data. Despite this, it remains popular with patients and providers for its undeniable benefits —like a 38% reduction in hospital admissions and a 51% reduction in ER visits. 

The reason addressing data quality is particularly urgent in cardiac care is two-fold. First, cardiac data is notoriously nuanced, particularly when it comes to non-event data, which holds the most potential to enable more proactive interventions. Second, there’s historically been a significant gap between the quality of data collected remotely and data collected in the hospital. Hospital-grade telemetry is typically administered in the intensive care unit, where continuous monitoring is required. Conversely, RPM technologies have had a far lower standard of acuity, limiting their potential applications. 

While remote cardiac data that provides a lower standard of acuity may be useful in diagnosing conditions that are straightforward, it doesn’t provide the level of precision needed to shape proactive interventions for anomalies occurring outside of a major cardiac event. These applications require the hospital-grade data standard to be applied to remote cardiac telemetry. 

Non-Event Data: A Trove of Insights for Personalized Care 

While programmed alerts serve as crucial indicators of potential cardiac issues, they only represent a fraction of the patient’s monitoring period—accounting for only ~1% of the total data produced, according to some reports. Non-event data, which comprises most of the data collected, holds invaluable insights into the patient’s overall status and potential underlying conditions. 

In many cases, arrhythmias can vary in their intensity and frequency and often do not meet the criteria set to create an alert notification—which means failure to capture and analyze non-event data may lead to missed opportunities for early intervention and prevention of cardiac events. 

Next-gen RPM solutions that offer full disclosure and continuous data lay the groundwork for interventions based on non-event anomalies—but these solutions also produce an outsized amount of data that can increase clinical burden. The integration of AI can combat this issue. Unlike traditional solutions that rely on programmed alerts, AI-driven solutions can sift through vast amounts of data, including non-event data, to identify subtle patterns and anomalies that may signal underlying cardiac issues that have not yet progressed to a major event. It goes without saying these advancements rely heavily on high-integrity data. 

Factors Degrading Cardiac Data

By prioritizing acuity in remote cardiac telemetry, we can overcome data quality issues and unleash its full potential to enable more proactive and personalized cardiac care. But first, we must first understand what degrades cardiac data. 

Studies note that data quality can be affected by factors like complex data collection, poor connectivity, and more. Let’s take a closer look at the common culprits when it comes to remote cardiac monitoring. 

• Device issues
  Sensor accuracy and device malfunction pose significant challenges to data integrity in RPM. Inaccurate sensors or hardware defects may result in erroneous readings or data gaps, leading to misinterpretation of the patient’s condition. Additionally, signal interference or battery issues can disrupt data transmission, compromising the reliability and security of data.

• Poor connectivity
  Poor internet connectivity can lead to RPM data integrity issues. Unstable internet connections can cause data transmission errors and delays that interrupt the monitoring of a patient’s status. Network congestion and bandwidth limitations exacerbate these challenges, necessitating better connectivity solutions.

• Patient compliance
  User adherence and proper usage also play an important role in maintaining data integrity. Non-compliance, like improper device placement or inconsistent usage, can introduce inaccuracies into monitored data, limiting its efficacy. The harder a solution is to use, the more likely patients will not comply with it. 

Tackling Data Integrity Challenges ‘Heart-On’

Understanding the issues that degrade data quality is the first step in addressing them. By prioritizing the following characteristics in a virtual telemetry solution, healthcare decision-makers can ensure data integrity and usher in a new era of AI-enabled interventions. 

• Select a quality system
  To improve data quality, leverage an RPM device/system that offers sensor accuracy, hardware reliability, optimal signal strength, data security, and configurable lead sets that support a higher level of acuity. Remember that quality data always begins with the system—a system known for its quality will minimize data inaccuracies and ensure seamless and safe data transmission. 

• Ensure continuous data
  Seek a solution that goes beyond the contiguous for true, continuous monitoring. Data should be instantly and securely transmitted to the cloud, where it’s rapidly transformed into clinical insight through AI-powered analysis. To ensure all data is being captured and transmitted, implement redundancy measures and utilize advanced networking technologies, as these help mitigate the impact of poor connectivity.

• Emphasize ease of use
  The ideal virtual telemetry solution should be easy and enjoyable for patients to use. Patients are the gatekeepers of their data, and there is no data without their utilization. A simple and streamlined patient experience reduces data degradation and maintains data quality while boosting adherence as they go about their daily life without wires and limitations. 

Ensuring High Integrity Cardiac Data from Anywhere

While data issues persist in many areas of care, the solutions to overcome data challenges in remote cardiac monitoring already exist—and further ensure data integrity is maintained.

InfoBionic.Ai’s virtual cardiac telemetry solution, the MoMe® ARC, harnesses streamlined wearables, continuous monitoring, and AI-enabled analysis to deliver gold-standard data and insights cardiologists need to efficiently take action. Visit www.infobionic.ai to learn more.