How Clinician-directed Machine Learning Could Introduce Seismic Change for Chronic Care

How can we help millions of chronically ill patients lead healthier lives with fewer hospitalizations? Parsing through individual patient remote monitoring data doesn’t seem like a scalable solution. But what if artificial intelligence could rapidly analyze this information and predict an individual’s health problem, such as worsening heart failure, in time for caregivers to intervene and prevent an emergency department visit or hospital admission? That may sound like a pipe dream, yet the technology to make it happen already exists. This new “prescription” for chronically ill patients would be an algorithm — a personalized disease model unique to each patient, based on streams of data collected from biosensors worn by patients. Aided by advances in clinician-directed machine learning, over time these algorithms could be tuned to predict short-term risk of…


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