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2354

Pioneering the pathway with big data to eliminate hepatitis C viral infection (EHCV)

Published online by Cambridge University Press:  10 May 2018

Dawn A Fishbein
Affiliation:
Georgetown - Howard Universities, Washington, DC, USA
Ian Brooks
Affiliation:
Georgetown - Howard Universities, Washington, DC, USA
Emanuel Villa Baca
Affiliation:
Georgetown - Howard Universities, Washington, DC, USA
Ozgur Ozmen
Affiliation:
Georgetown - Howard Universities, Washington, DC, USA
Mallikarjun Shankar
Affiliation:
Georgetown - Howard Universities, Washington, DC, USA
Gil Weigand
Affiliation:
Georgetown - Howard Universities, Washington, DC, USA
Kristina Thiagarajan
Affiliation:
Georgetown - Howard Universities, Washington, DC, USA
Randy Estes
Affiliation:
Georgetown - Howard Universities, Washington, DC, USA
Alex Geboy
Affiliation:
Georgetown - Howard Universities, Washington, DC, USA
Hala Deeb
Affiliation:
Georgetown - Howard Universities, Washington, DC, USA
Mamta Jain
Affiliation:
Georgetown - Howard Universities, Washington, DC, USA
Lesley Miller
Affiliation:
Georgetown - Howard Universities, Washington, DC, USA
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Abstract

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OBJECTIVES/SPECIFIC AIMS: Hepatitis C viral (HCV) infections are rising significantly both in young adults and as newly diagnosed cases in “baby boomers.” New HCV therapeutics cure over 95% of cases, and a call has been made for elimination of the epidemic by 2030; yet major HCV cascade of care (CoC) barriers exist. We secured CTSA pilot funding to obtain preliminary data for an innovative clinical trial utilizing big data modeling toward HCV elimination. METHODS/STUDY POPULATION: Our pilot work has developed a coordinated, real-time clinical data management process across 3 major CTSA affiliated hospital systems (MedStar Health, Emory-Grady, and UT-Southwestern), and additional data will be obtained from a pragmatic clinical trial. Electronic medical records data will be mapped to the OHDSI model, securely transmitted to Oak Ridge National Laboratory, Knoxville, TN and exposed to integrated data, analytics, modeling and simulation (IDAMS). RESULTS/ANTICIPATED RESULTS: Our U01 CTSA application proposes that HCV-IDAMS will model modifications to the established HCV CoC at community and population levels and thus simulate future outcomes. As data volume increases, system knowledge will expand and recursive applications of IDAMS will increase the accuracy of our models. This will reveal real-world reactions contingent upon population dynamics and composition, geographies, and local applications of the HCV CoC. DISCUSSION/SIGNIFICANCE OF IMPACT: Only an innovative, integrated approach harnessing pragmatic clinical data, big data and supercomputing power can create a realistic model toward HCV elimination.

Type
Biomedical Informatics/Health Informatics
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Association for Clinical and Translational Science 2018