Jennifer Williams, Stanford University

MassAITC Cohort: Year 2 (Aging)

The goal of this project is to improve of quality care and overall quality of life for those who are currently underserved and diagnosed with chronic obstructive pulmonary disease (COPD). This requires improved communication among patients, providers, and the health care team. The interim between clinic visits can be a critical time, and strategies to improve communication and manage patients’ symptoms directly in the home would be beneficial. One of the most important issues is detecting and managing the early signs of COPD exacerbations, which could allow for improved patient support and a decrease in hospital admissions, cost, and an overall reduction in morbidity, and mortality.

This project will develop and perform initial tests of an approach to monitoring older adults with COPD in the home using an unobtrusive metric derived from acoustic features collected during normal cell phone use to detect preventable exacerbations that would normally lead to hospitalization. COPD is a prevalent disease of the elderly population, with a disproportionate under-diagnosis and management with low-income and minority patients. Intervention in a timely manner could prevent significant morbidity and mortality. Patients may have progressive symptoms and signs that go undetected at home, and by targeting underserved elderly patients with COPD, we can help to support and bridge barriers to accessing care and help to identify and manage early signs of COPD exacerbations, ultimately leading to an improved overall quality of life with a decrease in hospitalizations.

We plan to augment our current approach to remote COPD care by developing and testing a mobile app that will integrate an acoustic metric of COPD severity developed at Samsung Research America, Inc. with new mobile health coaching approaches available through MassAITC. We plan to work with Samsung Research America to add their AI algorithm for classifying COPD severity to new clinical protocols that track lung function and predict COPD exacerbations over time.