  Despite the plethora of telehealth applications to assist home-based older
adults and healthcare providers, basic messaging and phone calls are still the
most common communication methods, which suffer from limited availability,
information loss, and process inefficiencies. One promising solution to
facilitate patient-provider communication is to leverage large language models
(LLMs) with their powerful natural conversation and summarization capability.
However, there is a limited understanding of LLMs' role during the
communication. We first conducted two interview studies with both older adults
(N=10) and healthcare providers (N=9) to understand their needs and
opportunities for LLMs in patient-provider asynchronous communication. Based on
the insights, we built an LLM-powered communication system, Talk2Care, and
designed interactive components for both groups: (1) For older adults, we
leveraged the convenience and accessibility of voice assistants (VAs) and built
an LLM-powered VA interface for effective information collection. (2) For
health providers, we built an LLM-based dashboard to summarize and present
important health information based on older adults' conversations with the VA.
We further conducted two user studies with older adults and providers to
evaluate the usability of the system. The results showed that Talk2Care could
facilitate the communication process, enrich the health information collected
from older adults, and considerably save providers' efforts and time. We
envision our work as an initial exploration of LLMs' capability in the
intersection of healthcare and interpersonal communication.

Computation and Language;Artificial Intelligence;Human-Computer Interaction