Designing Beyond Language: Sociotechnical Barriers in AI Health Technologies for Limited English Proficiency

2025-11-10

AI Summary

This study investigates how AI-enabled health technologies can address non-linguistic sociotechnical barriers—such as institutional workflow constraints, cultural misalignment, privacy concerns, and unstable technology access—faced by Spanish-speaking patients with limited English proficiency (LEP) in the U.S. healthcare system. Employing storyboard-driven qualitative interviews and integrating human-computer interaction (HCI) and sociotechnical systems analysis, the research examines real-world integration pathways for AI-powered translation and pre-visit preparation tools. Findings indicate that while AI mitigates communication and procedural barriers, it simultaneously introduces risks of misplaced trust and erodes clinician-patient rapport. The study proposes a novel “relationship-centered” AI design framework, emphasizing trust co-construction, digital literacy–aligned interfaces, minimal workflow disruption, and fidelity to existing care practices. This framework advances both theoretical understanding and practical implementation of inclusive, equity-oriented healthcare AI. (149 words)

Technology Category

• Humans and AI: AI for Accessibility
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

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.

Problem

• AI health technologies risk exacerbating healthcare inequalities for limited English proficiency patients
• Structural barriers undermine trust in AI systems through privacy concerns and technology access
• AI tools create tensions between potential benefits and risks of misinformation in care

Innovation

• Storyboard-driven interviews with patient navigators
• AI design considerations for rapport-building and education
• Minimizing disruptions to existing care practices