Biosketch
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.
He also works with the EPIC clinical repository from the 14 member
hospitals within the University of Maryland Medical System and 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.
The clinical focus for his work includes several chronic diseases, opioid misuse and addiction,
and online consumer health information. 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.
Shorter Biosketch
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 earned his medical degree from
George Washington University and a PhD in Computer Science from
the University of Maryland Baltimore County. He completed
residency and fellowship training at the University of Maryland. He works clinically
in the Department of Emergency Medicine at the Baltimore VA
Medical Center and the University of Maryland Medical Center. He
is Director of the University of Maryland Clinical Informatics
Group, is board certified in Clinical Informatics, is a
Fellow of the American College of Physicians, and is a Fellow of
the American Medical Informatics Association. He has authored
more than 50 refereed publications, and received more than two
million dollars in grant and contract funding. He has over 20
years of experience in Clinical Informatics with an emphasis on
clinical decision support, clinical data mining, and
software engineering. He works the the national clinical
respository from the Veterans Health Administration and the EPIC
clinical repository from the 14 member hospitals within the
University of Maryland Medical System. His research interests include
algorithm development for disease
prediction and management, decision support and process
improvement for opioid misuse and addiction, online consumer
health information, and quality improvement
in Emergency Medicine.