Steven Horng, MD

Clinical Informatics Fellowship Director, Division of Clinical Informatics

Associate Director, Division of Emergency Medicine Informatics, Beth Israel Deaconess Medical Center

Steven Horng, MD MMSC, is a faculty member of the Division of Clinical Informatics, Center for Healthcare Delivery Science, and the Department of Emergency Medicine. His research focuses on translational clinical informatics, specifically the translation of new computer science techniques to direct patient care. He has a broad background and formal training in computer science, emergency medicine, biomedical informatics, and research. In addition to being a practicing emergency physician, most of his time is spent on research spanning the translational lifecycle of computer science from theory development to direct clinical care intervention. His research focuses on 1) theoretical development of new machine learning approaches to electronic health record data, 2) translating advances in machine learning to direct clinical care 3) advancing machine learning in healthcare through open data and open science. He is the Program Director for the BIDMC Clinical Informatics Fellowship; a multidisciplinary ACGME accredited postdoctoral training program, where he oversees all training in Biomedical Informatics at the hospital, a specialty dedicated to integrating technology into clinical care. The fellowship is also a part of the Boston-Area Informatics Research Training Program and an NIH-sponsored training program in data science, where he serves as faculty. He is the course director for the Harvard Master’s Course BMI.720 in Clinical Informatics and guest lectures frequently at MIT in deployable machine learning. He is the Clinical Lead for Machine Learning at BIDMC, where he oversees the integration of machine learning into clinical care across all medical specialties. He has received funding from the National Science Foundation, Department of Defense, and the Massachusetts Life Sciences Center. He has over 50 publications in machine learning in healthcare. 


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