AI for Reliable and Equitable Real World Evidence Generation in Medicine

Dr. George Hripcsak, MD, MS, is interested in the clinical information stored in electronic health records and in the development of next-generation health record systems. Health record data are sparse, irregularly sampled, complex, and biased. Using nonlinear time series analysis methods borrowed from statistical physics, machine learning, knowledge engineering, and natural language processing, he is developing the methods necessary to support clinical research and patient safety initiatives using health record data. Dr. Hripcsak also has a long track record of developing, implementing, and studying informatics interventions to improve health care. He has published frequently on evaluation in biomedical informatics, leveraging his training in biostatistics. Dr. Hripcsak is Vivian Beaumont Allen Professor at Columbia University’s Department of Biomedical Informatics. He is a board-certified internist with degrees in chemistry, medicine, and biostatistics. He leads the Observational Health Data Sciences and Informatics (OHDSI) coordinating center; OHDSI is an international network with thousands of collaborators and health records on almost one billion patients. In precision medicine, he serves as a PI on Columbia’s eMERGE grant, Columbia’s regional recruitment center for the All of Us Research Program, and Columbia’s role on the All of Us Data and Research Center. He co-chaired the Meaningful Use Workgroup of U.S. Department of Health and Human Services’s Office of the National Coordinator of Health Information Technology. Dr. Hripcsak is a member of the National Academy of Medicine, the American College of Medical Informatics, the International Academy of Health Sciences Informatics, and the New York Academy of Medicine. He was awarded the 2022 Morris F. Collen Award of Excellence by the American College of Medical Informatics. He has over 500 publications.