The Administrative Component of the """"""""Patient-centered Information Commons (""""""""PIC"""""""") will be responsible for managing the overall conduct of this project. The management plan, headed by an Executive Team that includes the co-PIs, Drs. Isaac Kohane and Shawn Murphy, and the Executive Director, Susanne Churchill, has proposed a model based on this group's ten year experience with the i2b2 U54 National Center for Biomedical Computing. This plan focuses on the development and sustenance of a truly interactive and collaborative working group that involves all of the interdisciplinary domains required by this big data science project. Regularly structured interactions, progress reporting and ongoing evaluation are proposed to insure that progress is monitored, challenges identified, and solutions devised to address bottlenecks. Leadership will rely on a number of advisory bodies, including the NIH Science Team, an internal Scientific Advisory Board with expertise in areas affecting but not directly proposed for our research (e.g., patient privacy), an External Advisory Committee to be configured after award, and very importantly, a Users'Group constituted from potential end users in the community. The Admin Team will be responsible for developing and maintaining a dissemination strategy for the open source tools and procedures emerging from our work. Significant effort will be devoted to assuring compliance with all financial, regulatory and reporting requirements. This team will be fully engaged in the DB2K Consortium activities, including participation in its advisory bodies, dedicated meetings and other activities still to be defined.

Public Health Relevance

Sound management is essential to the successful outcome of a large project such as this and necessary to justify return on federal investment. This group is fully commited to this end, a commitment justified by our previous experience with a similarly scaled project which has been widely adopted at the international level.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
1U54HG007963-01
Application #
8932076
Study Section
Special Emphasis Panel (ZRG1-BST-R (52))
Program Officer
Brooks, Lisa
Project Start
Project End
Budget Start
2014-07-01
Budget End
2015-06-30
Support Year
1
Fiscal Year
2014
Total Cost
$343,340
Indirect Cost
$22,883
Name
Harvard Medical School
Department
Type
DUNS #
047006379
City
Boston
State
MA
Country
United States
Zip Code
02115
Luo, Yuan; Szolovits, Peter (2016) Efficient Queries of Stand-off Annotations for Natural Language Processing on Electronic Medical Records. Biomed Inform Insights 8:29-38
Brown, Adam S; Patel, Chirag J (2016) MeSHDD: Literature-based drug-drug similarity for drug repositioning. J Am Med Inform Assoc :
McGinnis, Denise P; Brownstein, John S; Patel, Chirag J (2016) Environment-Wide Association Study of Blood Pressure in the National Health and Nutrition Examination Survey (1999-2012). Sci Rep 6:30373
Patel, Chirag J; Manrai, Arjun K; Corona, Erik et al. (2016) Systematic correlation of environmental exposure and physiological and self-reported behaviour factors with leukocyte telomere length. Int J Epidemiol :
Patel, Chirag J; Pho, Nam; McDuffie, Michael et al. (2016) A database of human exposomes and phenomes from the US National Health and Nutrition Examination Survey. Sci Data 3:160096
Brown, Adam S; Kong, Sek Won; Kohane, Isaac S et al. (2016) ksRepo: a generalized platform for computational drug repositioning. BMC Bioinformatics 17:78
Leppert, John; Patel, Chirag (2016) Perspective: Beyond the genome. Nature 537:S105
Manrai, Arjun K; Wang, Brice L; Patel, Chirag J et al. (2016) REPRODUCIBLE AND SHAREABLE QUANTIFICATIONS OF PATHOGENICITY. Pac Symp Biocomput 21:231-42
Hoogendoorn, Mark; Szolovits, Peter; Moons, Leon M G et al. (2016) Utilizing uncoded consultation notes from electronic medical records for predictive modeling of colorectal cancer. Artif Intell Med 69:53-61
Li, Junlong; Zhao, Lihui; Tian, Lu et al. (2016) A predictive enrichment procedure to identify potential responders to a new therapy for randomized, comparative controlled clinical studies. Biometrics 72:877-87

Showing the most recent 10 out of 14 publications