Hwayoung Cho, PhD, RN

Teaching

Dr. Cho teaches graduate Nursing and Nursing Informatics courses for Doctor of Nursing Practice (DNP) programs, including Applied Statistical Analysis, Theory and Research for Nursing, Research Methods and Evidence-Based Practice, and Nursing Informatics and Information Management.

Research

Expertise: electronic health records (EHRs)/clinical decision support (CDS); mHealth; usability; artificial intelligence (AI)/machine learning (ML); health disparities; symptom care

Dr. Cho’s research has focused on using technology for underserved populations to reduce health disparities, promote health outcomes and improve health-related quality of life. Her research work focuses on generating real-world evidence and translating it into informatics tools to facilitate the implementation of evidence-based practice. Specifically, her expertise is to establish the usability of technology-based interventions (e.g., mHealth apps) for patients in underserved communities (racial and ethnic minorities and those from low-socioeconomic groups) and AI/ML-driven EHR-based clinical decision support (CDS) tools developed leveraging large-scale real-world EHR data for clinicians in hospitals.

Service

Dr. Cho holds memberships in the American Medical Informatics Association (AMIA), Academy Health, Sigma Theta Tau International, and Eastern Nurses Research Society. Her service activities include serving on professional association committees such as Member-at-Large for AMIA Clinical Research Informatics Working Groups, AI faculty search committee at UF Health Science Center, Scientific Program Committee for AMIA conferences, Guest Editor of the Special Issue “Mobile Health Technology” for the (international) MDPI Journals Future Internet, and reviewing manuscripts.

Dr. Cho’s areas of expertise are nursing informatics – implementation science using user-centered design and rigorous usability evaluations (including both quantitative/qualitative approaches, such as an innovative eye-tracking method, a card sorting technique, a heuristic evaluation, a cognitive walk-through, think-aloud protocols, focus groups and in-depth interviews) and data science leveraging artificial intelligence (AI), machine learning (ML) with large-scale real-world data from EHRs and claims data Her seminal research contributions include developing, testing, and implementing mobile health applications to support symptom management for people with HIV (PWH) in their everyday life and clinical decision support tools integrated with electronic health records (EHRs) to support optimal decision-making processes and reduce cognitive burden for clinicians in practice.