Linguistic and Cultural Adaptation of Remote Medication Dispensing System (Hub+Spoke) for OUD Treatment Among Limited English Proficient (LEP) Populations

About This Grant

The opioid epidemic is an urgent public health crisis in the United States: despite this, access to treatment for opioid use disorder (OUD) remains limited. Among patients seeking OUD treatment, those with Limited English Proficiency (LEP) show lower rates of treatment engagement, largely due to unmet language access needs in existing care delivery systems. The long-term objective of this project is to adapt Opio's ZING Satellite™ system—which expands MOUD delivery through satellite medication units—to function effectively for LEP patients through a multilingual implementation model. ZING Satellite combines telehealth with robotic methadone dose preparation to enable opioid treatment program (OTP) nurses to serve patients at satellite medication units. This Phase I SBIR project will focus on three specific aims: (1) identify language access and patient engagement priorities for LEP patients through structured interviews and community feedback sessions with two healthcare partner organizations serving LEP patients; (2) adapt the ZING Satellite system to support multilingual user interfaces, AI-powered live translated captioning, and language-adapted workflows and instructional materials; and (3) evaluate the feasibility and usability of the adapted system with LEP patients and clinical staff using scenario-based testing and structured implementation feedback. The project will use a community-based participatory research (CBPR) approach grounded in iterative stakeholder engagement, mixed-methods data collection, and external research support. This study improves the linguistic accessibility and usability of OUD treatment for LEP patients in a sustainable and scalable fashion. The findings will be relevant for digital health developers, addiction treatment providers, and policymakers seeking to improve medications for opioid use disorder (MOUD) access, and will offer a scalable model for adapting healthcare technologies—combining hardware and software solutions—to better serve patients with Limited English Proficiency across a range of clinical contexts.