This course will be led by Drs. Daniel Masys in collaboration with Drs. Christopher Chepken, and Matthew Dunbar. The focus will be clinical research data management that complies with international Good Clinical Practice (GCP) standards as published by the International Congress on Harmonization, E6 Consolidated Guidance.
Dr. Daniel R. Masys is an Affiliate (volunteer) Professor of Biomedical and Health Informatics, joining the Department of Biomedical Informatics and Medical Education after retiring as Professor and Chair of the Department of Biomedical Informatics and Professor of Medicine at the Vanderbilt University School of Medicine in 2011. An honors graduate of Princeton University and the Ohio State University College of Medicine, he completed postgraduate training in Internal Medicine, Hematology and Medical Oncology at the University of California, San Diego, and the Naval Regional Medical Center, San Diego. He served as Chief of the International Cancer Research Data Bank of the National Cancer Institute, National Institutes of Health, and was also Director of the Lister Hill National Center for Biomedical Communications, which is a computer research and development division of the National Library of Medicine. He also served as Director of Biomedical Informatics at the University of California, San Diego School of Medicine, Director of the UCSD Human Research Protections Program, and Professor of Medicine. Dr. Masys is an elected member of the Institute of Medicine of the National Academy of Sciences. He is a Diplomate of the American Board of Internal Medicine in Medicine, Hematology, and Medical Oncology. He is a Fellow of the American College of Physicians, and Fellow and Past President of the American College of Medical Informatics. Dr. Masys' professional interests include development of informatics infrastructure for conducting clinical and translational research, genome-phenome correlation using phenotype data derived from electronic medical records data, and approaches to incorporating genomic data effectively into clinical systems.