CANTRAC is a bilingual research group made up of research faculty from UCSD, UABC-Mexicali, SDSU, SDSU IV or Imperial Valley, this core group of stakeholders will engage in multiple online and in person meetings using a human centered design approach to bring together additional policymakers, stakeholders, and other researchers from both sides of the border to ideate, develop mental models, and identify key themes related to binational translational cancer research based on the National Center for Advancing Translational Science Framework (https://ncats.nih.gov/about/about-translational-science/spectrum) including clinical research (clinical trials), public health, bioinformatics, implementation, and more.

We’re investigating underdiagnosis and differential diagnosis patterns of Alzheimer’s and dementia among LEP and minority patients using electronic medical record (EMR) data. This involves statistical analysis of diagnostic trends and care accessibility.

Mixed methods study to assess patient understanding and perspectives on treatment process and risks, specifically cardiotoxicity risk, among limited English proficient and English proficient women diagnosed with left-side breast cancer. 

Convenience sample of university affiliated research personnel, students, and staff related to their experiences interacting with non-English proficient patients, specifically focused on research.

This project applies Natural Language Processing (NLP) to unstructured medical records. We aim to identify underdiagnosis and misdiagnosis in geriatric assessments, particularly for LEP and minority patients, by extracting insights from clinical notes.

Mixed methods time-motion and qualitative study to assess oncologist human-computer interactions to identify opportunities for integrating geriatric assessment, improve clincal workflow, and reduce burnout.

Evaluation and D&I project to assess baseline and biometric data for improved self-management among diverse, low-income community-dwelling older adults with multiple chronic conditions (MCC). Improving multi-user telemedicine deployment to diverse, low-income older adults in congregate housing with a focus on those without a caregiver and who are limited English proficient.

This project analyzes disparities in internet access by merging datasets from the American Community Survey (ACS) and FCC Broadband. The goal is to identify correlations between low broadband speeds and regions with high minority populations, which may contribute to healthcare inequities.