Adam Wright, PhD, FACMI, FAMIA, FIAHSI

Adam Wright, PhD, FACMI, FAMIA, FIAHSI is Professor of Biomedical Informatics and Medicine at Vanderbilt University Medical Center and serves as the director of the Vanderbilt Clinical Informatics Center (VCLIC).

He has led NIH, AHRQ and ONC-funded projects on clinical problem lists, malfunctions in clinical decision support systems, approaches for sharing clinical decision support nationally and adverse event detection using machine learning.  Dr. Wright has over 130 peer-reviewed journal publications, and nearly 100 additional publications, including abstracts, presentations in scientific meetings, books and book chapters. He is also a committed teacher, directing and lecturing in local, national and international courses on biomedical informatics, and teaching medical students.

Dr. Wright directs clinical decision support operations at VUMC, and previously served as clinical lead for clinical decision support and clinical informatics at Partners HealthCare in Boston.  He is active nationally, serving as a board member of the American Medical Informatics Association (AMIA), an Associate Editor for Applied Clinical Informatics, and an Editorial Board Member for Methods of Information in Medicine and the Journal of the American Medical Informatics Association. Dr. Wright is also a founding member and director of research for the Clinical Informatics Research Collaborative.

Dr. Wright’s work has been recognized through numerous awards, including the American Medical Informatics Association New Investigator Award in 2010, election to the American College of Medical Informatics in 2015, the Early Career Achievement Award from Oregon Health and Science University, election to the inaugural class of the Fellowship of the American Medical Informatics Association in 2018, and election to the International Academy of Health Science Informatics in 2019.

Research Interests: Electronic health records, clinical decision support, machine learning. Current research focuses include using data to make clinical decision support systems more effective and to detect malfunctions; using machine learning to predict and detect adverse events; driving high-quality care and eliminating medical errors through advanced information technology.