George Hripcsak, MD, MS
Vivian Beaumont Allen Professor and Chair of Biomedical Informatics
Columbia University Medical Center
George Hripcsak, MD, MS, is Vivian Beaumont Allen Professor and Chair of Columbia University’s Department of Biomedical Informatics and Director of Medical Informatics Services for NewYork-Presbyterian Hospital/Columbia Campus. He is a board-certified internist with degrees in chemistry, medicine, and biostatistics. Dr. Hripcsak’s current research focus is on the clinical information stored in electronic health records and on the development of next-generation health record systems. Using nonlinear time series analysis, machine learning, knowledge engineering, and natural language processing, he is developing the methods necessary to support clinical research and patient safety initiatives. For his work in precision medicine, he serves as a PI on Columbia’s eMERGE grant, as a PI on Columbia’s regional recruitment center for the All of Us precision medicine program, and as site PI for Columbia’s role on the All of Us Data and Research Center. He co-chaired the Meaningful Use Workgroup of U.S. Department of Health and Human Services’s Office of the National Coordinator of Health Information Technology; it defines the criteria by which health care providers collect incentives for using electronic health records. He led the effort to create the Arden Syntax, a language for representing health knowledge that has become a national standard. Dr. Hripcsak is a fellow of the National Academy of Medicine, the American College of Medical Informatics, and the New York Academy of Medicine, and he chaired the U.S. National Library of Medicine’s Biomedical Library and Informatics Review Committee. He has published over 350 papers.
Dr. Hripcsak serves as PI–with co-PI David Madigan–of OHDSI’s Coordinating Center, which is based at Columbia University. His recent pharmacovigilance research has included medication-wide association studies, treatment pathways, large-scale observational studies, and next-generation phenotyping to better exploit electronic health record data for observational research.
Weng C, Li Y, Ryan P, Zhang Y, Liu F, Gao J, Bigger JT, Hripcsak G. A Distribution-based Method for Assessing The Differences between Clinical Trial Target Populations and Patient Populations in Electronic Health Records. Appl Clin Inform. 2014 May 7;5(2):463-79. doi: 10.4338/ACI-2013-12-RA-0105. eCollection 2014. PMID: 25024761 [PubMed – in process]
MR Boland, NP Tatonetti, G Hripcsak. CAESAR: a Classification Approach for Extracting Severity Automatically from Electronic Health Records. Intelligent Systems for Molecular Biology Phenotype Day. 2014; Boston, MA.
Ryan PB, Madigan D, Stang PE, Schuemie MJ, Hripcsak G. Medication-wide association studies. CPT: Pharmacometrics & Systems Pharmacology 2013;2,e76;doi:10.1038/psp.2013.52. PMC4026636
Vilar S, Uriarte E, Santana L, Lorberbaum T, Hripcsak G, Friedman C, Tatonetti NP. Similarity-based modeling in large-scale prediction of drug-drug interactions. Nature Protocols, accepted for publication.
Pivovarov R, Albers DJ, Sepulveda JL, Elhadad N. Identifying and mitigating biases in EHR laboratory tests. J Biomed Inform. 2014 Apr 13. pii: S1532-0464(14)00084-7. doi: 10.1016/j.jbi.2014.03.016.
Albers DJ, Elhadad N, Tabak E, Perotte A, Hripcsak G. Dynamical phenotyping: using temporal analysis of clinically collected physiologic data to stratify populations. PLOS ONE 9(6): e96443. doi: 10.1371/journal.pone.0096443.
Hripcsak G, Albers DJ. Correlating electronic health record concepts with health care process events. J Am Med Inform Assoc 2013 Dec;20(e2):e311-8. doi: 10.1136/amiajnl-2013-001922. PMC3861922
Weiskopf NG, Hripcsak G, Swaminathan S, Weng C. Defining and measuring completeness of electronic health records for secondary use. J Am Med Inform Assoc 2013 Oct;46(5):830-6. doi: 10.1016/j.jbi.2013.06.010. PMC3810243
Boland MR, Hripcsak G, Shen Y, Chung WK, Weng C. Defining a comprehensive verotype using electronic health records for personalized medicine. J Am Med Inform Assoc 2013 Dec;20(e2):e232-8. doi: 10.1136/amiajnl-2013-001932. PMC3861934
Perotte A, Hripcsak G. Temporal properties of diagnosis code time series in aggregate. IEEE Journal of Biomedical and Health Informatics 2013;17(2):477-83. doi: 10.1109/JBHI.2013.2244610. PMC4030411
Hripcsak G, Albers DJ. Next-generation phenotyping of electronic health records. J Am Med Inform Assoc 2013;20:117–21. doi:10.1136/amiajnl-2012-001145. PMC3555337
Albers DJ, Hripcsak G, Schmidt M. Population physiology: leveraging electronic health record data to understand human endocrine dynamics. PLOS ONE 2012;7(12):e48058. doi:10.1371/journal.pone.0048058.
Albers DJ, Hripcsak G. Using time-delayed mutual information to discover and interpret temporal correlation structure in complex populations. Chaos 2012 January 24;22:013111. doi:10.1063/1.3675621.
Hripcsak G, Albers DJ, Perotte A. Exploiting time in electronic health record correlations. J Am Med Inform Assoc 2011;18:Suppl 1 i109-i115. Published Online First 2011 Nov 23. doi:10.1136/amiajnl-2011-000463.
Harpaz R, Perez H, Chase HS, Rabadan R, Hripcsak G, Friedman C. Biclustering of adverse drug events in FDA’s spontaneous reporting system. Clin Pharmacol Ther 2011;89:243-50. PMC3282185
Albers DJ, Hripcsak G. A statistical dynamics approach to the study of human health data: resolving population scale diurnal variation in laboratory data. Physics Letters A 2010;374:1159-64. PMC2882798
Wang X, Chase HS, Li J, Hripcsak G, Friedman C. Integrating heterogeneous knowledge sources to acquire executable drug-related knowledge. AMIA Annu Symp Proc. 2010;2010:852-6. PMC3041361
Wang X, Hripcsak G, Markatou M, Friedman C. Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study. J Am Med Inform Assoc 2009;16:328-37. PMC2732239