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
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 and Director of Medical Informatics Services for
NewYork-Presbyterian Hospital. 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. 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