Development of a Culturally and Linguistically Informed Pain Expression Library and Assessment Module for Asian Americans Using Artificial Intelligence

Principal Investigator:

Shinye Kim, assistant professor of counseling psychology

Co-Principal Investigator:

Maichou Lor, assistant professor of nursing

The Numeric Rating Scale (NRS) standard for pain assessment, referred to as the 0-10 scale, is simple and convenient; however, it is severely limited in capturing the cultural, linguistic, and emotional nuances of chronic pain experienced among racial and ethnic minorities and low English proficiency patients. As a result, these patients are likely to be clinically undertreated and their medical files are likely to be under-documented.

Asian American immigrants have been historically excluded in pain science, despite being the fastest growing minority group in the U.S. Thus, there is a critical need to identify Asian American cultural and linguistic pain communication patterns and expressions so that providers have complete information for clinical decisions.

This project will improve diagnostic accuracy and treatment efficacy of multiple chronic pain conditions, including informing whether to prescribe opioids and at what level.

The research methodology will include in-depth interviews with Asian immigrants to understand their language use relating to pain. Researchers will then build a cultural and language-specific pain dictionary and expression library database based on distinct cultural and linguistic factors. They will then expand the database by adding other psychosocial pain factors. These data will be used to develop a prototype biopsychosocial pain analysis digital health technology (web/mobile app) that includes Chinese, Vietnamese, Korean, and Hmong pain terms, idioms, and linguistic patterns. The researchers will also write a health policy brief addressing critical cultural-linguistic implications of pain care disparities in the Asian population.

The long-term goal is to develop and implement effective communication interventions and digital health technologies that remove cultural and linguistic hurdles between healthcare providers and low English proficiency patients, increasing diagnostic accuracy and treatment efficacy for underserved populations and ultimately reducing health disparities.