January 22, 2021 -- Xinzhi Zhang, MD, PhD, FACE, FRSM, Program Director, National Center for Data to Health (CD2H); Lead, Rural Health and Health Equity; Lead, Diversity and Re-entry Supplements; Clinical and Translational Science Awards (CTSA) Program; Division of Clinical Innovation; National Center for Advancing Translational Sciences, NIH. "Big Data, Small Populations - A Conversation on Translational Health Equity Research."
Abstract: With deliberate efforts, Big Data presents a dramatic opportunity for reducing health disparities but without active engagement, it risks further widening them. This conversation will focus on challenges and opportunities that Big Data science may offer to the reduction of health and health care disparities.
Bio: Xinzhi Zhang is a program director in the NCATS Division of Clinical Innovation, where he manages a portfolio of Clinical and Translational Science Awards (CTSA), including the CTSA National Center for Data to Health. He is also a lieutenant commander in the U.S. Public Health Service Commissioned Corps, an elite group of public health leaders who respond to national health crises.
Zhang joined NIH in 2012 as a program director in the National Institute on Minority Health and Health Disparities’ Division of Extramural Scientific Programs where he provided leadership for scientific program development and project management on minority health and health disparities research. Prior to that, Zhang had joined the National Center for Infectious Diseases’ Office of Surveillance at the Centers for Disease Control and Prevention (CDC) in 2003 as a Steven M. Teutsch Prevention Effectiveness Fellow. From 2005 to 2012, he was an epidemiologist in CDC’s National Center for Chronic Disease Prevention and Health Promotion’s Division of Diabetes Translation. Zhang has authored papers for inclusion in CDC’s Morbidity and Mortality Weekly Report, as well as book chapters, and he has had more than 60 articles published in peer-reviewed journals, including the Journal of the American Medical Association, the American Journal of Public Health and the American Journal of Preventive Medicine. Currently, he also serves as an associate editor of Health Equity(link is external).
Zhang received his M.D. from Peking Union Medical College in 1998 and his Ph.D. in health services administration from the University of Alabama at Birmingham in 2003.
January 15, 2021 -- Tsung-Ting (Tim) Kuo, PhD,
Assistant Professor, UCSD Health Department of Biomedical Informatics, University of California San Diego. "Decentralized Predictive Modeling on Blockchain."
Abstract: In this talk, Dr. Tsung-Ting Kuo will introduce decentralized predictive modeling, healthcare blockchain, and how these two technologies can be combined to preserve privacy for collaborative machine learning. He will also discuss other use cases of healthcare blockchain.
Bio: Dr. Tsung-Ting Kuo is an Assistant Professor of Medicine in University of California San Diego (UCSD) Health Department of Biomedical Informatics (DBMI). He earned his PhD from National Taiwan University (NTU) in the Institute of Networking and Multimedia. Prior to becoming a faculty member, he was a Postdoctoral Scholar at UCSD DBMI and received the UCSD Chancellor’s Outstanding Postdoctoral Scholar Award. He was a major contributor towards the UCSD DBMI team winning the Office of the National Coordinator for Health Information Technology (ONC) healthcare blockchain challenge, and also the NTU team winning the Association for Computing Machinery (ACM) Knowledge Discovery and Data Mining (KDD) Cup competition four times. He was awarded a NIH K99/R00 Pathway to Independence Award with an Administrative Supplement, as well as UCSD Academic Senate Health Science Research Grant and Travel Award, for blockchain-based biomedical, healthcare and genomic studies. His research focuses on blockchain technologies, machine learning, and natural language processing.
January 8, 2021 -- Julia Adler-Milstein, PhD, Professor of Medicine, Director of the Center for Clinical Informatics and Improvement Research, UCSF.
"Turning Digital Fumes into a Breath of Fresh Air."
Abstract: While EHR data is heavily used for clinical research, there is also significant potential for behavioral and social science research. In my talk, I will describe EHR access logs as a novel source of data that captures individual and team behaviors, and give examples of how such data can be applied to address policy- and practice-based questions related to user interface design, clinician burnout, and clinical workflow.
Bio: Dr. Julia Adler-Milstein is a Professor of Medicine and Director of the Center for Clinical Informatics and Improvement Research (CLIIR). Dr. Adler-Milstein is a leading researcher in health IT policy, with a specific focus on electronic health records and interoperability. She has examined policies and organizational strategies that enable effective use of electronic health records and promote interoperability. She is also an expert in EHR audit log data and its application to studying clinician behavior. Her research – used by researchers, health systems, and policymakers – identifies obstacles to progress and ways to overcome them.
She has published over 100 influential papers, testified before the US Senate Health, Education, Labor and Pensions Committee, is a member of the National Academy of Medicine, been named one of the top 10 influential women in health IT, and won numerous awards, including the New Investigator Award from the American Medical Informatics Association and the Alice S. Hersh New Investigator Award from AcademyHealth. She has served on an array of influential committees and boards, including the NHS National Advisory Group on Health Information Technology, the Health Care Advisory Board for Politico, and the Interoperability Committee of the National Quality Forum.
Dr. Adler-Milstein holds a PhD in Health Policy from Harvard and spent six years on the faculty at University of Michigan prior to joining UCSF as a Professor in the Department of Medicine and the inaugural director of the Center for Clinical Informatics and Improvement Research.