Peiyin Hung, PhD - an assistant professor at the University of South Carolina Arnold School of Public Health, where she teaches doctoral-level research methods courses and employs the tools of quantitative analysis and health services research in the context of rural health.

Dr. Hung received her Ph.D. degree in Health Services Research, Policy, and Management from University of Minnesota - Twin Cities and a Master of Science Degree in Public Health with a focus on health policy and health economics from Emory University. Prior to coming to the United States, Dr. Hung obtained her Bachelor of Business Administration from Chung-Shan Medical University, worked as a data analyst at a hospital system in Taiwan, and participated in a Central Healthcare Network Project in the Taiwan Ministry of Health and Welfare.

Her research primarily uses administrative and survey data to examine the quality and financial outcomes related to patient and provider's choices. Most of her work integrates:

  • Building statistical models to simulate rural healthcare delivery processes
  • Identifying effective health policy interventions to address health disparities
  • Implementing technology changes to help close gaps in costs, access, and quality of care
Her quantitative and qualitative research has been published in multiple peer-reviewed journals, disseminated in policy briefs, highlighted in over 70 national and local media, and presented at state and national conferences across the country.

Dr. Hung's career goal is to develop a research program that assists community, state, and federal agencies in evaluating and identifying evidence-based healthcare interventions ensuring vulnerable populations, including those in underserved areas, prepare transitions from one care setting to another, with subsequent improvements in patient outcomes for all. The quality innovation with evidence-based medicine and electronic health records has led to a new health care century. She also helps health care facilities measure their performance and distinguish small area variation in the process.