RSMI HEALS

Project Summary/Abstract There are 26 million limited English proficient (LEP) people in the U.S. population (those who speak English less than “very well”); the two most frequently spoken languages are Spanish (63%) and Chinese (7%). The LEP population faces disparities in cancer outcomes, in part due to communication barriers and lack of access to language interpretation services. Technology holds great promise for efficient, scalable remote interpreting solutions to bridge the language barrier. However, there is still no evidence-based gold standard for technology-based interpreting. Currently, there are 3 technology-based, people-rendered methods employed for remote interpreting: 1) Remote Consecutive Medical Interpreting (RCMI; “audio consecutive”), the most commonly utilized, 2) Remote Consecutive Video Medical Interpreting (RCVI; “video consecutive”), a growing resource, and 3) Remote Simultaneous Medical Interpreting (RSMI), “UN-style” simultaneous interpreting applied to the medical encounter, which holds tremendous promise for closely approximating a same language encounter, decreasing interpreting errors, and improving outcomes. Further, with the rapid advance of artificial intelligence (AI) solutions, there is AI potential for less expensive, more scalable interpreting services delivery. RSMI HEALS will use a Hybrid Type 2 design, combining a randomized controlled trial (RCT), conducted across 3 diverse cancer clinics, with a Consolidated Framework for Implementation Research (CFIR) process evaluation, to gather both clinical efficacy and real-world implementation evidence on the optimal technology- enabled medical interpretation modality. The RCT will enroll 576 Spanish- and Mandarin-speaking LEP patients with Stages II and III breast cancer to compare RSMI (UN-style) with RCMI (audio consecutive) and with RCVI (video consecutive) interpreting. Specific Aim 1 is to compare across arms (A) the proportion of interpreting errors of clinical significance (primary outcome), and B) i) appointment adherence, ii) patient knowledge of treatment/instructions, iii) the patient-provider relationship (using the PEPPI), and iv) efficiency by interpreted medical fact. Specific Aim 2 is to utilize the CFIR process analysis to gather data on a) integrating host institution and systems factors/policies into the intervention, and b) implementation potential, through the exploration of the following: i) facilitators of and barriers to a) intervention delivery, and b) intervention sustainability after study completion; and ii) how the interventions and their delivery could be refined to improve future adoption and sustainability. Specific Aim 3 is to utilize evidence from the RCT and the CFIR process evaluation to outline policy and funding implications. Our exploratory aim is to conduct error and efficiency analyses of RSMI vs AI-RSMI, and to gauge the potential acceptability of AI-RSMI with patient and facility surveys. These findings will inform future multisite large-scale R01 and/or PCORI-like studies to further address equitable cancer care for LEP populations across a variety of languages, settings, and diseases.