Research

PI: Katherine Kim

This study contributes to the knowledge on application of digital health technology to the coordination and self-management of chronic illness among a wide range of communities such as those who speak languages other than English, have vision and manual dexterity challenges, or have low literacy. Understanding the feasibility and usability of a system that uses artificial intelligence for multi-language applications and navigation of software by voice is important to improving health for all people. By developing and studying AI-enabled accessibility we contribute to interventions that may prevent chronic illness, improve health outcomes, and tackle the challenges of health disparities.

PI: Katherine Kim

Chronic illnesses such as diabetes and hypertension challenge goals of healthy aging, with burdens on individuals, family caregivers, and the healthcare system. Uncontrolled chronic illnesses are a risk factor for cognitive decline, Alzheimer's disease and related dementias, and frailty. We need solutions for older adults to age with independence, to lead healthier lives, and to maintain access to their healthcare services when needed. The questions we want to answer are: What are all the possible behavioral, lifestyle, and medical treatment options for people with chronic illness? When and how should those interventions be rolled-out for the best outcomes over time as people age (trajectories)? How could you weigh all the potential scenarios and make the best decisions? We use data from remote monitoring, clinical care, and healthcare utilization, to develop Health Digital Twins (HDTs) for community-dwelling older adults with diabetes and/or hypertension and insights for both the individual and healthcare providers. Digital twins can be defined as (physical and/or virtual) machines or computer-based models that are simulating or "twinning" the life of a physical entity (an object, process, human, or a human-related feature). We generate HDTs via deep phenotyping and application of two state-of-the-art AI methods to take advantage of the pros and limit the cons of each: a generative model using variational autoencoder and a large language model coupled with retrieval-augmented generation. HDTs leverage population level data across urban and rural settings and combines it with a patient's unique data, to deliver personalized recommendations.