SUMMARY

Noncommunicable disease (NCDs) including cardiovascular disease and diabetes have become the primary source of human mortality and disability in the 21st century, accounting for over 70% of deaths and 61% of disability-adjusted years worldwide. Medical infrastructure, which for decades sought to reduce the incidence and severity of communicable diseases, has proven insufficient in meeting the intensive and long-term monitoring needs of many NCD patient groups. However, recent trends in soft, wearable medical devices and cloud technologies offer a possible alternative to traditional in-person clinical monitoring. Soft sensors interfaced via a mobile device with the cloud could leverage this remote computational power to bring novel machine learning and signal processing directly to patients, displaying key health metrics such as heart rate and blood pressure in real-time. Using similar machine learning tools, this same cloud system could also provide long-term guidance to clinicians informed by the patient’s history, creating a pipeline between patients and their physicians. This is demonstrated here, wherein cloud-interfaced soft devices facilitate the remote monitoring of NCD patient groups whose needs have proven difficult for contemporary clinical practices to accommodate, among them at-risk postpartum women and newborns with single ventricular heart disease. A mobile application, central cloud pipeline, and multiple soft sensors were designed to make a robust, highly scalable, remote monitoring ecosystem. In several clinical trials, this combination of novel soft device technology and delivery of cloud-based analysis to patient and clinician was shown successfully to detect and monitor disease progression, determine patient risk, and augment clinical decision-making for patient groups whose monitoring demands have proven intractable in standard clinical practice.