System for Assessing Health Severity and Predicting Readmissions


A smartphone-based system for monitoring patients to identify and prevent severe cardiac events.

Key Benefits

  • A remote monitoring system that uses smartphone data to accurately predict a user’s risk for a severe cardiac event (e.g., heart attack).
  • Tracks the user's movement, location, and physical activity to calculate FDA-accepted risk scoring.
  • May reduce the high mobility and mortality rate of various cardiovascular diseases.

Market Summary

Cardiovascular disease is the leading cause of death in the United States, resulting in more than 600,000 annual deaths, and is a significant contributor to healthcare costs. Traumatic injury, smoking tobacco, diets high in fat and cholesterol, excessive alcohol intake, and sedentary lifestyles are the primary risk factors for the disease. Despite improved medical treatment outcomes for heart failure (HF), readmission rates following a severe event remain high. Efforts are underway to devise new strategies to reduce readmission rates with patient-centered approaches, such as telemonitoring and wearables. Hence, there is an unmet need to develop new solutions that can reduce hospital readmission rates, leading to decreased morbidity and mortality of this devastating disease.

Technical Summary

Researchers at Emory have developed an automated remote patient monitoring system for identifying patients at at-risk for severe cardiac events. The method comprises a predictive analysis algorithm that accurately emulates Kansas City Cardiomyopathy Questionnaire 12 (KCCQ-12), a reliable FDA-recognized health status measure for HF patients. The smartphone application passively collects location, activities (calls, texting, typing), physical movement, social networking, and environmental data. The inventors have created a beta version of the application. Evaluation of 10-at risk patients with the application showed the ability to accurately predict KCCQ scores.

Developmental Stage

Advanced beta application with human data.

Patent Information

App Type Country Serial No. Patent No. File Date Issued Date Patent Status
Nationalized PCT - United States United States 17/295,248   5/19/2021   Pending
Nationalized PCT - Foreign EP 19893550.4   6/16/2021   Pending
Tech ID: 18226
Published: 11/27/2023