MD Clinical Informatics (Practice Pathway) | University of Maryland School of Me --
Residency Training:
Internal Medicine | University of Maryland --
Medical School:
Medicine | George Washington University
Michael Grasso is an Assistant Professor of Internal Medicine and Emergency Medicine at the University of Maryland School of Medicine, and an Adjunct Assistant Professor of Computer Science at the University of Maryland Baltimore County. He practices Emergency Medicine through the University of Maryland School of Medicine. He is also board certified in Clinical Informatics and is Director of the Clinical Informatics Group at the University of Maryland School of Medicine.He earned a medical degree from the George Washington University and a PhD in Computer Science from the University of Maryland Baltimore County. He completed residency training at the University of Maryland School of Medicine. He is a member of the Upsilon Pi Epsilon Honor Society in the Computing Sciences, the Kane-King-Dodec Medical Honor Society, the William Beaumont Medical Research Honor Society, is a Fellow of the American College of Physicians (FACP), and is a Fellow of the American Medical Informatics Association (FAMIA).
He has been awarded more than $2,000,000 in grant and contract funding from the National Institutes of Health, the Food and Drug Administration, the National Institute of Standards and Technology, the National National Aeronautics and Space Administration, and the Department of Defense. He has authored more than 50 refereed publications, and has more than 20 years of experience in Clinical Informatics and Scientific Computing with an emphasis on software engineering, clinical decision support, and clinical data mining. His research focuses on big data analytics applied to clinical data. He is currently working with the national clinical repository from the Veterans Health Administration, which contains data on more than 35 million patients from roughly 150 medical centers and 800 outpatient clinics, and which he is augmenting with clinical data from the Maryland Emergency Medicine Network. He is developing new methods for knowledge representation and reasoning that are optimized for very large clinical repositories, and which can be applied to disease prediction, critical event prediction, and treatment efficacy prediction. He is also conducting research in resource utilization and recidivism in emergency medicine, with a focus on co-morbidities, key risk factors, adverse drug events, chronic pain, suicidality, addiction, utilization patterns, and clinical workflow.
