¡Doctor, no hablo inglés!: AI applied in Community Health Settings to Support Care Journeys

Federally Qualified Health Centers (FQHCs) are the backbone of care for underserved populations in the United States, serving millions of patients regardless of their ability to pay. As these centers face growing demand and limited resources, AI is emerging as a tool to support everything from clinical workflows to patient engagement. In this session, I offer a window into the community health center ecosystem in the US with a focus on Massachusetts, and explore how different FQHCs are piloting AI to support their patient populations.

Among the most persistent challenges in this space is language access. Approximately 25% of FQHC patients prefer care in a language other than English, and health centers are legally mandated to provide interpretation services - a very costly mandate. As a case study, I present Lexi, an AI-powered language access platform I helped build as a founding member of a Harvard-originated startup. Lexi enables real-time communication across the entire patient journey, from scheduling calls and front desk intake to clinical consultations. I walk through use cases across diverse care settings and user types, showcasing how patients and healthcare staff used the platform in FQHCs in Massachusetts and describing the impact of its implementation.

Speaker: Daniel Rodriguez Rodriguez. Master in Design Engineering. Harvard University. Founding Member of Lexi AI.