ABSTRACT This is an application for a diversity supplement award for Dr. Pilar Hernandez-Con, a doctoral student in the Department of Pharmaceutical Outcomes & Policy, College of Pharmacy, at University of Florida (UF). Dr. Hernandez-Con aims to establish herself as a health services researcher specializing in studying the syndemic of hepatitis C virus (HCV), human immunodeficiency virus (HIV), and substance use disorders (SUDs). This diversity supplement award aims to provide Dr. Hernandez-Con with the necessary support to: (1) enhance her understanding and expertise of social determinants of health (SDoH) influencing the HCV/HIV syndemic among individuals with SUDs; (2) gain training in natural language processing (NLP) methodologies to extract clinically meaningful data from unstructured clinical notes; and (3) improve her grant writing skills leading to the submission of a K99/R00 grant before the end of the support period. To achieve these goals, Dr. Hernandez- Con will collaborate with an interdisciplinary mentoring team, including primary mentor, Dr. Haesuk Park, the principal investigator of the parent grant and an expert in HCV and HIV infections among vulnerable populations, along with co-mentors Dr. Jingchuan Serena Guo, an epidemiologist specializing in SDoH and EHR; Dr. Amie Goodin, an expert in health policy and SUDs; and Dr. Yonghui Wu, a computer scientist with expertise in NLP applied to EHR data. Dr. Robert L. Cook, a primary care physician and researcher specializing in HIV and SUDs, will serve as her career development advisor. The opioid crisis in the United States has resulted in a sharp increase in HCV and HIV infections among individuals with SUD, yet a significant proportion remain unaware of their infections, contributing to further transmission. Dr. Hernandez-Con’s proposed study will focus on identifying important SDoH affecting the HCV/HIV/SUD syndemic. This proposal has two specific aims: (1) developing and validating a polysocial risk score (PsRS) utilizing NLP methods and UF Health EHR data to comprehensively assess SDoH that influence HCV infection and HCV/HIV co-infection; and (2) developing and validating an algorithm to predict the risk of HCV infection and HCV/HIV co-infection, comparing its performance to algorithms with and without PsRS alongside demographic and clinical characteristics. The insights gained from this study will inform effective implementation strategies for healthcare providers, researchers, and policy makers. They will also serve as the foundation for future research to better understand the role of SDoH in the HCV/HIV syndemic and health outcomes in vulnerable populations particularly individuals with SUDs.