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.