# RSMI HEALS

> **NIH NIH R01** · SLOAN-KETTERING INST CAN RESEARCH · 2024 · $763,694

## Abstract

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 futur...

## Key facts

- **NIH application ID:** 10944303
- **Project number:** 1R01MD019804-01
- **Recipient organization:** SLOAN-KETTERING INST CAN RESEARCH
- **Principal Investigator:** FRANCESCA M GANY
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $763,694
- **Award type:** 1
- **Project period:** 2024-08-08 → 2029-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10944303

## Citation

> US National Institutes of Health, RePORTER application 10944303, RSMI HEALS (1R01MD019804-01). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10944303. Licensed CC0.

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