Fangli Geng
Biography
Dr. Fangli Geng is an Assistant Professor in the Department of Health Services, Policy & Practice at Brown University's School of Public Health and an affiliated faculty member at Harvard University's T.H. Chan School of Public Health, Center for Health Decision Science. She holds a Ph.D. in Health Policy and a secondary degree in Data Science from Harvard University. Her research focuses on health policy issues related to aging in the U.S. and China, with an emphasis on designing patient-centered, high-value, and equitable care delivery models that cater to the diverse needs of the elderly and their families.
Dr. Geng has extensive expertise in health economics and advanced quantitative methods, including quasi-experimental design, decision modeling, machine learning, and cost-effectiveness analysis. Her research covers post-acute and long-term care delivery models, payment reforms, organizational structures among hospitals, nursing homes, and home health agencies, as well as quality of care and healthcare staffing issues in nursing homes and home health agencies.
Her contributions to health policy, particularly in post-acute and long-term care, have been published in prominent journals such as Health Affairs, JAMA Health Forum, and BMJ Medicine, and contacted by major media outlets like the Wall Street Journal. Her work has also drawn attention from significant U.S. healthcare bodies, including the Medicare Payment Advisory Commission and the Government Accountability Office.
How does your research, teaching, or other work relate to data or computational science?
My research and teaching are deeply intertwined with data science, as I focus on leveraging data-driven approaches to enhance the quality and effectiveness of post-acute and long-term care for older adults. With a secondary field in Data Science from my PhD, my work involves analyzing large datasets, such as Medicare claims data, to identify patterns and factors that influence care outcomes in skilled nursing facilities (SNFs) and home health agencies (HHAs). By applying machine learning and statistical techniques, I aim to uncover insights that can inform policy and improve care standards.
I am particularly interested in the intersection of data science and health services, exploring how these technologies can be utilized to optimize healthcare delivery and decision-making processes. This interest is reflected in my ongoing research, which investigates AI applications in health services.
In my teaching, I emphasize the importance of data literacy and computational skills, equipping students with the tools they need to navigate and analyze complex healthcare data. I am currently developing a course focused on data science in health services research, which will incorporate data science methodologies and foster a learning environment where students can engage with real-world data and develop critical thinking skills.