# Pilot Project 1: AI HEALS (Health Equity Advances through Language Solutions)?

> **NIH NIH U54** · CITY COLLEGE OF NEW YORK · 2024 · $47,100

## Abstract

Project Summary
Of 26 million limited English proficient (LEP) people in the U.S. population (speaking English less than “very
well”), most speak Spanish (63%) and Chinese (7%). The LEP population faces cancer outcomes disparities,
in part due to medical interpretation services barriers. Technology promises efficient, scalable remote
interpreting solutions to bridge the language barrier. However, there is no evidence-based gold standard for
technology- based interpreting. Two technology-based, people-rendered methods used for remote interpreting
are 1) Remote Consecutive Medical Interpreting (RCMI; “audio consecutive”), the most commonly utilized,
and 2) Remote Simultaneous Medical Interpreting (RSMI), “UN-style” simultaneous interpreting applied to
the medical encounter, which may closely approximate a same language encounter, decrease interpreting
errors, and improve outcomes. Further, with artificial intelligence (AI) solutions, there is potential for less
expensive, more scalable interpreting services delivery in the form of AI Simultaneous Medical Interpreting
(AISMI). We will compare RCMI (audio consecutive) (Arm 1), versus RSMI (UN style) (Arm 2) and versus
AISMI (AI UN style) (Arm 3) with Spanish- and Mandarin-speaking actors playing LEP patients and playing
English-speaking providers, to determine comparative error rates of clinical significance and efficiency. We
will write 50 English scripts incorporating content reflecting actual medical oncology encounters. The patient
portion of the scripts will be translated into Spanish and Mandarin. We will have a pool 10 Mandarin and 10
Spanish interpreters. Additionally, we will train a commercially available AI system to provide AISMI for
Spanish and Mandarin speakers. Each script will be human-interpreted twice, once via RSMI and once via
RCMI, and by the AISMI system. There will be 150 interpreted simulations per language (appointments
acted from the scripts with the addition of unscripted live interpreting) that will be audio-recorded and
transcribed (50 for each type of interpreting). We will determine differences in interpreting error rates of
clinical significance (primary outcome) and in interpreting efficiency of utterance (secondary outcome).
Additionally, we will investigate the potential acceptability of AISMI with surveys among Spanish- and
Mandarin-speaking patients and clinicians and administrators. 26 million U.S. people are limited English
proficient, speaking English less than “very well”; lack of access to language interpretation services can
cause limited English proficient patients with cancer to have poor cancer outcomes. There is little information
available on technology-based solutions to providing medical interpretation services. We will compare the
error rates and efficiency of technology-enabled interpretation methods, including remote simultaneous
interpretation (UN style) delivered by artificial intelligence.

## Key facts

- **NIH application ID:** 11011998
- **Project number:** 2U54CA132378-16
- **Recipient organization:** CITY COLLEGE OF NEW YORK
- **Principal Investigator:** Carlos Riobó
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $47,100
- **Award type:** 2
- **Project period:** 2008-09-26 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11011998

## Citation

> US National Institutes of Health, RePORTER application 11011998, Pilot Project 1: AI HEALS (Health Equity Advances through Language Solutions)? (2U54CA132378-16). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/11011998. Licensed CC0.

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