How AI Triage Changes Cardiac Device Monitoring

From Alert Overload to Intelligent Care is a data-driven series examining how cardiac remote monitoring is evolving to meet the realities of modern care. As alert volumes grow and care teams face increasing clinical and operational pressure, this series explores the evidence behind smarter monitoring workflows – including AI-assisted triage – and what they mean for patient safety, clinician well-being, and long-term sustainability. Each article focuses on real-world challenges, validated data, and practical insights.

Remote monitoring has transformed cardiac device care — but it has also changed the operational demands placed on device clinics. 

As transmission volumes increase and device programs face a growing data deluge in cardiac remote monitoring, teams must review and document a rising number of alerts, many of which do not require clinical action.

AI-assisted triage introduces a different model. Instead of routing every transmission to a human reviewer, intelligent systems analyze incoming data in real time, filtering non-actionable events and escalating only those that warrant attention.

The result is a workflow designed not just for scale — but for sustainability.

What Is AI Triage in Cardiac Device Monitoring?

AI triage in cardiac device monitoring refers to the use of machine learning algorithms to analyze incoming device transmissions in real time and determine whether they require clinical review.¹ 

Rather than sending every transmission to a human reviewer, AI systems evaluate the data against trained models that identify patterns associated with clinically significant events.

When a transmission is determined to be non-actionable — such as routine device checks or benign rhythm variations — it can be dismissed automatically. Transmissions that meet defined clinical thresholds are escalated to the care team for further evaluation.¹

This approach shifts triage from a fully manual process to a hybrid model, where automation filters noise and clinicians focus on decision-making.

Importantly, AI triage does not replace physician oversight. It supports clinical workflows by applying consistent criteria at scale, helping reduce variability and review burden while maintaining safety standards.¹,³

The Operational Strain of Rising Transmission Volumes

Remote monitoring programs have expanded rapidly over the past decade. 

In one large multicenter cohort, more than half of patients transmitted at least one alert in a year, totaling over 82,000 alerts — illustrating the sheer volume of data device clinics now process.

With improved connectivity and broader adoption of cardiac implantable electronic devices (CIEDs), clinics now manage significantly higher transmission volumes than traditional staffing models were designed to support.

Each transmission, whether clinically meaningful or not, requires review, documentation, and workflow processing. Sustained exposure to high volumes of non-actionable transmissions can contribute to alert fatigue and clinical risk, particularly in high-throughput programs. 

As volumes increase, so does the administrative burden placed on device clinics.

Experts have noted that sustaining high-quality remote monitoring requires workflow evolution, not simply additional manual review.² 

Automation is increasingly viewed as a necessary component of modern device clinic operations, particularly as transmission frequency and patient panels grow.²

AI-assisted triage builds on this shift, applying intelligent filtering at the point of data intake rather than downstream in the review process.

Why AI Triage Matters for Device Clinics

The challenge facing device clinics is not simply alert volume — it is sustainability.

As patient panels grow and transmission frequency increases, staffing models built around fully manual review become increasingly difficult to maintain.² Expanding personnel alone does not always solve the problem; workflow redesign is often required to preserve quality while managing scale.² 

Many organizations are actively pursuing strategies to reduce data burden in cardiology clinics without compromising safety.

AI-assisted triage addresses this structural issue at the intake level. By filtering non-actionable transmissions before they reach clinician queues, clinics can reduce cognitive load, shorten review times, and allow staff to operate at top-of-license.

This shift does not remove clinical judgment. Instead, it reallocates attention toward higher-risk events — such as true arrhythmias, device malfunctions, and significant physiological changes — where expertise has the greatest impact.

More broadly, the integration of artificial intelligence into cardiology reflects a model of human-guided AI in cardiac care, where technology enhances performance without replacing clinical judgment.³

AI triage reflects that same principle within remote device monitoring.

As remote monitoring continues to evolve, intelligent filtering may become less of a competitive advantage and more of an operational necessity.

Real-World Impact: AI Triage at Scale

While the concept of AI-assisted triage is compelling in theory, its value depends on real-world performance.

Indeed, analyses show that over half of RM transmissions can be redundant or non-clinically relevant, with only a minority containing actionable alerts — underscoring the need for intelligent triage.

In a large multicenter evaluation, AI-enabled triage was deployed across 78 device clinics and analyzed data from more than 74,000 cardiac devices over an eight-month period.¹ The system processed nearly 700,000 transmissions in parallel with traditional technician workflows.

The results demonstrated a meaningful reduction in clinician-facing workload. AI triage forwarded 22.8% of transmissions to clinics, compared to 38.5% forwarded through standard technician review — reducing the volume reaching care teams by approximately 40%.¹

Importantly, the system operated alongside existing workflows rather than replacing them, allowing clinics to compare performance directly while maintaining clinical oversight.

These findings suggest that AI triage can substantially decrease review burden without eliminating human decision-making — offering a scalable model for modern device clinic operations.¹

The Next Question: Can Efficiency and Safety Coexist?

AI-assisted triage introduces a new model for managing transmission volume — one built on intelligent filtering rather than fully manual review.

Early real-world data suggest meaningful reductions in clinician-facing workload, offering a path toward more sustainable device clinic operations. But efficiency alone is not enough.

Any change to patient care workflows must be evaluated through the lens of clinical safety. Reducing alert volume is only valuable if clinically meaningful events continue to be identified reliably and without delay.

As remote monitoring evolves from adoption to optimization, the conversation must move beyond workload reduction to a deeper question: can automation preserve — or even strengthen — safety standards?

In the next article, we examine the clinical validation of AI-assisted triage, including sensitivity data and what it means for patient safety in modern cardiac device programs.

You can read the article here.

References

1. Landolina M, et al. Remote monitoring of implantable cardioverter-defibrillators (EVOLVO Study). Circulation.
2. Hindricks G, et al. Remote monitoring of ICDs: clinical safety and outcomes. Europace.
3. Slotwiner DJ, et al. Clinical validation of AI-assisted device monitoring workflows. JACC Advances.

Next in the series → Preserving Clinical Safety While Reducing Alert Volume