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This is part 4 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.
Any technology introduced into patient care must be evaluated through the lens of safety. Remote monitoring itself has a strong safety record, enabling earlier detection of actionable events and reducing unnecessary emergency visits. Randomized trials have demonstrated faster clinical response times and reductions in urgent in-person care compared to traditional follow-up.¹
AI-based triage builds on this foundation. In the Atlas AI evaluation, the system achieved approximately 99% sensitivity for clinically meaningful events, meaning it identified virtually all true positives.³ Manual review under real-world conditions demonstrated substantially lower sensitivity, reflecting the impact of fatigue and variability.³
See how AI-assisted workflows can reduce alert volume while preserving safety. Request a demo.
High sensitivity ensures that efficiency gains do not come at the cost of missed events. By reviewing every transmission instantly and consistently, AI functions as a safety net – escalating critical events reliably while reducing overall alert volume.²,³
References
- Landolina M, et al. Remote monitoring of implantable cardioverter-defibrillators (EVOLVO Study). Circulation.
- Hindricks G, et al. Remote monitoring of ICDs: clinical safety and outcomes. Europace.
- Slotwiner DJ, et al. Clinical validation of AI-assisted device monitoring workflows. JACC Advances.
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Next in the series → Scaling Device Clinics Without Adding Staff