Automate, Illuminate, Predict: A Universal Framework for Integrating Wearable Sensors in Healthcare

Abstract

Background: Wearable sensors have been heralded as revolutionary tools for healthcare. However, while data are easily acquired from sensors, users still grapple with questions about how sensors can meaningfully inform everyday clinical practice and research. Summary: We propose a simple, comprehensive framework for utilizing sensor data in healthcare. The framework includes three key processes that are applied together or separately to (1) automate traditional clinical measures, (2) illuminate novel correlates of disease and impairment, and (3) predict current and future outcomes. We demonstrate applications of the Automate-Illuminate-Predict framework using examples from rehabilitation medicine. Key Messages: Automate-Illuminate-Predict provides a universal approach to extract clinically meaningful data from wearable sensors. This framework can be applied across the care continuum to enhance patient care and inform personalized medicine through accessible, noninvasive technology.

Keywords

• algorithms
• clinical study
• digital biomarkers
• influence and/or predict health-related outcomes
• monitoring
• quantifiable physiological and behavioral data
• wearable sensors