Purpose of Review <p>This review examines the potential benefits of non-invasive remote monitoring in patients with heart failure (HF), focusing on early detection of clinical deterioration and reducing hospitalizations. Key questions addressed include: Can remote monitoring prevent hospitalisations in patients with HF? Does it improve quality of life and promote self-care? Is it cost-effective? Can artificial intelligence (AI) facilitate its implementation?</p> Recent Findings <p>Monitoring with non-wearable and wearable devices reduces hospitalizations by detecting early signs of deterioration and enhancing self-care behaviour. While the initial investment can be high, the long-term cost-effectiveness is supported by reduced hospitalisations. AI is increasingly integrated into monitoring systems, enhancing predictive accuracy and personalized care.</p> Summary <p> Remote monitoring reduces mortality and hospitalisations in patients with HF, with benefits in cost-effectiveness, and the potential to optimize care delivery by integrating AI. Future research should focus on identifying monitoring strategies for specific HF populations, such as patients with advanced HF.</p>

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Non-Invasive Remote Monitoring in Heart Failure: Towards Wearable Devices and Artificial Intelligence Solutions

  • Camila S. Pizarro,
  • Bas B.S. Schots,
  • Mark J. Schuuring,
  • Pim van der Harst,
  • René van Es,
  • Marish I.F.J. Oerlemans

摘要

Purpose of Review

This review examines the potential benefits of non-invasive remote monitoring in patients with heart failure (HF), focusing on early detection of clinical deterioration and reducing hospitalizations. Key questions addressed include: Can remote monitoring prevent hospitalisations in patients with HF? Does it improve quality of life and promote self-care? Is it cost-effective? Can artificial intelligence (AI) facilitate its implementation?

Recent Findings

Monitoring with non-wearable and wearable devices reduces hospitalizations by detecting early signs of deterioration and enhancing self-care behaviour. While the initial investment can be high, the long-term cost-effectiveness is supported by reduced hospitalisations. AI is increasingly integrated into monitoring systems, enhancing predictive accuracy and personalized care.

Summary

Remote monitoring reduces mortality and hospitalisations in patients with HF, with benefits in cost-effectiveness, and the potential to optimize care delivery by integrating AI. Future research should focus on identifying monitoring strategies for specific HF populations, such as patients with advanced HF.