George Hripcsak, MD, MS 
Vivian Beaumont Allen Professor of Biomedical Informatics, Columbia University
Chair, Department of Biomedical Informatics, Columbia University
Director, Medical Informatics Services, NewYork-Presbyterian
Hospital/Columbia
Senior Informatics Advisor, New York City Dept of
Health and Mental Hygiene
Co-chair, Meaningful Use Workgroup, HIT Policy Committee of the Office of the National
Coordinator of Health Information Technology
Biography
George Hripcsak, MD, MS, is
Vivian Beaumont Allen Professor and Chair of Columbia University’s Department
of Biomedical Informatics, Director of Medical Informatics Services for NewYork-Presbyterian Hospital, and Senior Informatics
Advisor at the New York City Department of Health and Mental Hygiene. Dr.
Hripcsak is a board-certified internist with degrees in chemistry, medicine,
and biostatistics. He led the effort to create the Arden Syntax, a language for
representing health knowledge that has become a national standard. Dr. Hripcsak’s current research focus is on the clinical
information stored in electronic health records. Using data mining techniques
such as machine learning and natural language processing, he is developing the
methods necessary to support clinical research and patient safety initiatives.
As Director of Medical Informatics Services, he oversees a 7000-user,
2.5-million-patient clinical information system and data repository. He is
currently co-chair of the Meaningful Use Workgroup of
HHS’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. Dr. Hripcsak was elected fellow of the
American College of Medical Informatics in 1995 and served on the Board of
Directors of the American Medical Informatics Association (AMIA). As chair of
the AMIA Standards Committee, he coordinated the medical-informatics community
response to the Department of Health and Human Services for the
health-informatics standards rules under the Health Insurance Portability and
Accountability Act of 1996. Dr. Hripcsak chaired the National Library of
Medicine’s Biomedical Library and Informatics Review Committee, and he is a
fellow of the American College of Medical Informatics and the New York Academy
of Medicine. He has published over 200 papers.
What Is Informatics?
Biomedical Informatics is the
study of information and computation in biology and health. Its researchers
study and manage information, study behavior related to decisions, and develop
computational methods and use them to generate knowledge.
The Columbia University Department of
Biomedical Informatics is among the oldest in the nation. Our goals are
discovery and impact: to discover new information methods, to augment the
biomedical knowledge base, and to improve the health of the population. Our 30
faculty members and 60 students work in a highly collaborative environment,
applying informatics from the atomic level to global populations. Our areas of
application include:
* CLINICAL CARE, such as
designing clinical information systems and mining the electronic health record
* BIOLOGY, including systems
biology, structural biology, and virology, in partnership with the Center for
Computational Biology and Bioinformatics
* PUBLIC HEALTH, such as
designing systems to promote and protect the health of communities, improving
public health systems, and deploying information technology internationally
* TRANSLATIONAL RESEARCH,
including integrating biological and clinical knowledge and facilitating
multidisciplinary science
Research
My
research focuses on understanding and using the clinical information stored in the
electronic health record. This theme has several components:
1. Data mining and knowledge discovery (see “Discovering and
applying knowledge in clinical databases” project web site). Machine
learning and visualization are examples of techniques to uncover knowledge from
vast clinical databases. My work focuses on testing and extending existing
discovery methods to improve their performance on clinical databases. Important
issues include training set size, data accuracy, data completeness, and
representation (e.g., how to accommodate diagnostic data, which is nominal with
many categories). Recent work includes the use of non-linear time series
analysis to characterize the electronic health record. Here is a study of serum
glucose, where predictability quantified as mutual information reveals the
diurnal variation of glucose (evidenced by the ridges). See [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] for a related study on creatinine.
2. Natural language processing. In most institutions,
the vast majority of the richly detailed clinical information is stored as
narrative text, which is not generally amenable to automated analysis. Natural
language processing can parse the narrative text, converting it to a structured
and coded format. See [Hripcsak G, Elhadad N, Chen C, Zhou L, Morrison FP. Using
empirical semantic correlation to interpret temporal assertions in clinical
texts. J Am Med Inform Assoc 2009;16:220-7] for a study of the degree to which the true time
of an event varies from what is stated in the patient record. It is illustrated
below, where 1 marks the time of the writing of the note, and 0 marks the
stated time.
3. Evaluation methodology. The complexity of clinical
data, the presence of inaccurate and missing values, and the large but
heterogeneous collection of patients conspire to make it difficult to draw
conclusions using traditional statistical methods. Bias that would not affect a
traditional randomized trial can overwhelm the true effect in a retrospective
study of the electronic medical record. Here is a short piece on measuring
agreement [Hripcsak G, Rothschild AS. Agreement, the F-measure, and reliability in information retrieval.
J Am Med Inform Assoc 2005;12:296-8].
5. Clinical demonstration. Demonstrating the
usefulness of the above methods is critical to gather support and to focus new
work in important areas. The methods can be applied to clinical research
(largely hypothesis refinement) and clinical care (by generating timely advice
and monitoring patient safety). Recent work has included syndromic
surveillance and pharmacovigilance. See [Hripcsak G, Soulakis ND, Li L, Morrison FP, Lai AM, Friedman C, Calman NS, Mostashari F. Syndromic surveillance using ambulatory electronic health
records. J Am Med Inform Assoc 2009;16:354-61] for a study of surveillance.
In
addition, I am studying next-generation electronic health records. Current
technology supports individual clinician tasks, such as documenting and
ordering, in a manner that is largely similar to that of traditional paper records.
Improved understanding of workflow, information needs, cognition, and the
science of collaboration can lead to improved systems that exploit human
abilities, facilitate teams, and disseminate expertise. Here is a recent
editorial [Shea S, Hripcsak G. Accelerating the use of
electronic health records in physician practices. NEJM 2010;362:192-5].
Service
As Director of Medical Informatics Services
for NewYork-Presbyterian Hospital/Columbia, I
overseeing the clinical data warehouse, terminology, WebCIS,
immunization, infection control, and physician outreach and collaborate on
clinician documentation, health information exchange, and patient portals.
·
WebCIS Web-based clinical
information system
As Co-chair of the Meaningful Use Workgroup
of the HIT Policy Committee of the Office of the National Coordinator of Health
Information Technology, I participate in the definition of "meaningful
use," by which the HITECH Act encourages the adoption of electronic health
records to promote improved quality and efficiency of health care.
Education
We
offer programs at all levels of informatics training, including PhDs, master's
degrees, postdoctoral fellowship, certificate training, and education for
students in medicine, nursing, dentistry, and public health. See www.dbmi.columbia.edu.
Further information
Publications
via Pubmed
Curriculum vitae
Contact:
George
Hripcsak, MD, MS
622 West 168th Street, VC-5
New York, NY 10032
Email:
hripcsak@columbia.edu