针对英语能力有限患者，研究AI医疗工具如何既缓解沟通障碍又避免加剧不平等。
Designing Beyond Language: Sociotechnical Barriers in AI Health Technologies for Limited English Proficiency
- 通过访谈14位患者引导员，探索AI在医疗中的角色与潜在冲突。
- 发现隐私泄露、技术不稳定和低素养会削弱对AI的信任。
- 建议设计注重建立信任、语言支持和减少对现有流程的干扰。
美国英语能力有限（LEP）患者面临语言之外的系统性医疗障碍，包括程序与制度性限制。尽管人工智能可通过即时翻译和就诊准备辅助沟通与照护，但也可能加剧既有不平等。我们通过对14位患者导航员开展基于故事板的访谈，探讨了AI如何影响西班牙语LEP人群的就医体验。研究识别出语言与文化误解、隐私担忧，以及AI在增强照护流程中的机遇与风险。参与者强调结构性因素会削弱对AI系统的信任，如敏感信息泄露、技术接入不稳定及低读写能力。尽管AI可能缓解社会障碍与制度约束，但仍存在误导性信息传播和人际互动减少的风险。研究提出设计建议：通过建立信任、提供教育与语言支持，最小化对现有实践的干扰，以更好服务LEP患者与医护团队。
原文摘要 · Abstract (English)
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 literacy. While AI tools can potentially alleviate social barriers and institutional constraints, there are risks of misinformation and reducing human-to-human interactions. Our findings contribute AI design considerations that support LEP patients and care teams via rapport-building, educational and language support, and minimizing disruptions to existing practices.
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