How AI Triage Changes Cardiac Device Monitoring

This is part 3 of an 8-part series.

‍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.

AI-assisted triage offers a practical way to address alert overload. Instead of routing every transmission to a human reviewer, AI algorithms analyze incoming data in real time, automatically dismissing non-actionable transmissions and escalating only those that require clinical attention.²

In a large real-world evaluation, Octagos’ Atlas AI was deployed across 78 clinics and analyzed data from more than 74,000 devices over eight months. The system processed nearly 700,000 transmissions in parallel with traditional workflows. Atlas AI forwarded only 22.8% of transmissions to clinics, compared to 38.5% forwarded by device technicians, reducing alert volume reaching clinicians by approximately 40%.¹

This reduction allows care teams to focus on what matters most: true arrhythmias, device malfunctions, and clinically significant changes. AI does not replace clinical judgment – it enhances it by filtering noise and presenting actionable information consistently and at scale.²,³

Want to see how AI-assisted triage works in practice? Request a demo.

References

1. Slotwiner DJ, et al. Artificial intelligence–enabled triage of cardiac device transmissions. JACC Advances.
2. Varma N. Remote monitoring and the evolving role of automation in device clinics. Heart Rhythm.
3. Rajpurkar P, et al. Artificial intelligence in cardiology. Nature Reviews Cardiology.

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