110 S Paca, 6th Fl., Suite 200
Education and Training
- University of Maryland, BS. Microbiology, 1983
- American University, MS, Computer Science, 1986
- University of Maryland Baltimore County, PhD, Computer Science, 1997
- George Washington University School of Medicine & Health Sciences, MD, 2005
- Residency, University of Maryland School of Medicine, Internal Medicine, 2008
- Fellowship (Practice Pathway), University of Maryland School of Medicine, Department of Emergency Medicine, Clinical Informatics, 2013
Michael Grasso is an Assistant Professor of Internal Medicine, Emergency Medicine, and Computer Science at the University of Maryland School of Medicine. 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, and is a Fellow of the American College of Physicians.
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 other sources. 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 this work includes several chronic diseases and mental health conditions. 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.
Biomedical Informatics, Big Data Analytics, Data Mining, Predictive Modeling, Clinical Decision Support, Chronic Disease Management, Emergency Medicine, Internal Medicine, Safety and Quality, Pain Management, Opioid Abuse
Grasso MA, Dezman ZD, Comer AC, Jerrard DA. The decline in hydrocodone/acetaminophen prescriptions in emergency departments in the Veterans Health Administration between 2009 to 2015. West J Emerg Med. 2016;17(4):396-403. Available online at http://escholarship.org/uc/item/29d2w30f/.
Grasso MA, Comer AC, DiRenzo DD, Yesha Y, Rishe ND. Using big data to evaluate the association between periodontal disease and rheumatoid arthritis. AMIA Annu Symp Proc. 2015 Nov 14-18; San Francisco, CA.
Payne E, Carlisle A, Desai S, Grasso MA. Using "Big Data" analytics and health care informatics to advance personalized health. APHA 143rd Annual Meeting and Expo. 2015 Oct 31-Nov 4; Chicago, IL.
Grasso MA, Cotter B, Jerrard DA. The impact of a follow-up clinic on unscheduled return emergency department visits. American College of Emergency Medicine, Research Forum. 2015 Oct 26-27; Boston, MA.
Grasso MA, Lemkin DL, Bond MC. Subspecialty training in clinical informatics: Prerequisite activities for potential applicants. EM Resident. 2015 Feb;42(1). http://www.emresident.org/subspecialty-training-in-clinical-informatics/.
Bochare A, Gangopadhyay A, Yesha Ye, Joshi A, Yesha Ya, Grasso MA, Brady M, Rishe N. Integrating domain knowledge in supervised machine learning to assess the risk of breast cancer. International J of Medical Engineering and Informatics, 2014;6(2):87-99.
Grasso MA, Dalvi D, Das S, Gately M, Korolev V, Yesha Y. Genetic information for chronic disease prediction. IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM). 2011 Nov;:997.
A complete list of publications can be found at NCBI.
My research focuses on innovative applications of biomedical informatics that expand the scope of clinical medicine, have a strong theoretical basis in computer science, and are of strategic importance to the University of Maryland Medical System. The clinical focus for this work includes several chronic diseases and mental health conditions, as well as patient safety and quality improvement in emergency medicine.
Knowledge Representation and Reasoning with Big Data - I am developing new methods for knowledge representation and reasoning that are optimized for very large clinical repositories, and which can be correlated with genomic and environmental data. My specific approach is to enhance machine learning algorithms with semantic analysis, domain information, and deep learning. My clinical focus is on coronary artery disease, diabetes, rheumatoid arthritis, chronic kidney disease, chronic pain, addiction, and mental illness. This work will lead to new approaches to quantify clinical risk and enable clinical decision support. It can be applied to disease prediction, critical event prediction, and treatment efficacy prediction. I am 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 I are augmenting with clinical and genomic data from several other sources.
Patient Safety and Quality Measures in Emergency Medicine - Patient safety and quality is one of the nation's most important health care challenges. The widely-cited report by the Institute of Medicine estimates that as many as 44,000 to 98,000 people die in U.S. hospitals each year as the result of lapses in patient safety. This is especially important in the emergency department, where health care teams are challenged to rapidly diagnose and treat multiple patients, many of whom present with potentially life-threatening illness. I am conducting research in resource utilitzation 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.