Designing Beyond Language: Sociotechnical Barriers in AI Health Technologies for Limited English Proficiency
Michelle Huang, Violeta J. Rodriguez, Koustuv Saha, Tal August
TL;DR
The paper investigates how AI health technologies intersect with the needs of LEP patients, focusing on Spanish-speaking communities in the U.S. Through storyboard-driven interviews with 14 patient navigators and reflexive thematic analysis, it identifies linguistic, cultural, literacy, and privacy barriers that influence technology adoption, as well as opportunities for AI to reduce social and institutional hurdles. The authors propose design guidelines to embed AI within existing care practices, emphasize trust, dialect-level adaptation, and privacy protections, and caution against overreliance on AI at the expense of human support. The work advances our understanding of when and how AI can meaningfully support LEP patients and care teams, offering practical implications for developers, clinicians, and policymakers aiming to promote equitable digital health. Future research should incorporate LEP patients’ perspectives and develop prototyped, low-bandwidth, culturally aligned AI tools that respect privacy and augment, rather than replace, human care.
Abstract
Limited English proficiency (LEP) patients in the U.S. face systemic barriers to healthcare beyond language and interpreter access, encompassing procedural and institutional constraints. AI advances may support communication and care through on-demand translation and visit preparation, but also risk exacerbating existing inequalities. We conducted storyboard-driven interviews with 14 patient navigators to explore how AI could shape care experiences for Spanish-speaking LEP individuals. We identified tensions around linguistic and cultural misunderstandings, privacy concerns, and opportunities and risks for AI to augment care workflows. Participants highlighted structural factors that can undermine trust in AI systems, including sensitive information disclosure, unstable technology access, and low digital literacy. While AI tools can potentially alleviate social barriers and institutional constraints, there are risks of misinformation and uprooting human camaraderie. Our findings contribute design considerations for AI that support LEP patients and care teams via rapport-building, education, and language support, and minimizing disruptions to existing practices.