SBIR Phase II: A Therapeutic Machine-Learned Triage Application For Early Detection and Triage of COPD Exacerbations

E-Thera, Inc., Austin TX

Investigators

Abstract

The broader/commercial impact of SBIR Phase II project aims to reduce significant disease flare-ups in patients with Chronic Obstructive Pulmonary Disease (COPD), increase in-patient quality-of-life, and reduce expensive and unnecessary healthcare utilization. COPD is one of the leading chronic conditions driving potentially avoidable hospital admissions, accounting for an estimated $25 B in 2018 patient care costs. Current at-home care support for COPD patients is often completely missing or consists of action plans that fail to provide effective, individualized care. Integrating health management and a smart triage system offering instant healthcare guidance has the potential to reduce unnecessary COPD hospitalization and provide long-term maintenance treatment of COPD symptoms. The proposed project will develop an easy-to use, personalized triage application that catches disease degeneration early, tracks patient health history, and provides decision support to guide patients appropriately. Moreover, an easily accessible, highly accurate, convenient solution can empower patients to make better health decisions early. This SBIR Phase II project focuses on optimizing and deploying a triage application that provides personalized decision support and therapeutic benefit to patients with COPD. The proposed project will validate this application for a new population and test the integrated system. Specific technical tasks include the expansion of algorithms to include jointly diagnosed COPD-asthma patients, an important population at risk, as well as optimization of configurations for usability and performance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.