We propose to create a massively scalable toolkit to enable large, multi-center Patient-centered Information Commons (PIC) at local, regional and, national scale, where the focus is the alignment of all available biomedical data per individual. Such a Commons is a prerequisite for conducting the large-N, Big Data, longitudinal studies essential for understanding causation in the Precision Medicine (1) framework while simultaneously addressing key complexities of Patient Centric Outcome Research studies required under ACA (Affordable Care Act). Our proposal is solidly grounded in our experience over the last 25 in harnessing clinical care data to the research enterprise. In creating PIC we propose to focus on: 1. Enable the identification and retrieval of all data that pertain to individal health by creating a data sharing architecture that is capacious enough for all relevant data types and that enables patient and institutional autonomy to be respected. 2. Test fully-scaled implementations of the proposed architecture early in the development process, with the active involvement of a committed user community that seeks to use allowed us to refine our designs to facilitate subsequent robust dissemination and adoption. 3. Provide commodity workflows that can be used to 'clean'and complete the often noisy and sparse data gathered in the course of observational studies. 4. Embrace decentralization while enabling the construction of a nationally or regionally-scaled patient-centered information commons. 5. Encourage the selection of standards through the tools that enable the construction of patient-centered information commons. 6. Employ diagnostic classification and prognostication as figures of merit to measure how well a patient-centered information commons adds the understanding of patient populations. In addition to the research and development agenda we have also taken on the development of educational opportunities for end user community to become more familiar with the methods and challenges of data science.

Public Health Relevance

Large populations of individuals characterized by many different and complementary types of data, for example genetic, environmental, imaging, behavioral and clinical findings will allow significant progress in our ability to accurate classif individuals as to their disease or disease risk and provide more precise predictions of their disease course. The proposed toolkit enables such chacterization at the local and national scale.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
1U54HG007963-01
Application #
8775065
Study Section
Special Emphasis Panel ()
Program Officer
Brooks, Lisa
Project Start
2014-09-29
Project End
2018-08-31
Budget Start
2014-09-29
Budget End
2015-04-30
Support Year
1
Fiscal Year
2014
Total Cost
$2,082,841
Indirect Cost
$418,530
Name
Harvard Medical School
Department
Miscellaneous
Type
Schools of Medicine
DUNS #
047006379
City
Boston
State
MA
Country
United States
Zip Code
02115
Yu, Zhi; Kim, Seoyoung C; Vanni, Kathleen et al. (2018) Association between inflammation and systolic blood pressure in RA compared to patients without RA. Arthritis Res Ther 20:107
Neykov, Matey; Hejblum, Boris P; Sinnott, Jennifer A (2018) Kernel machine score test for pathway analysis in the presence of semi-competing risks. Stat Methods Med Res 27:1099-1114
Agniel, Denis; Kohane, Isaac S; Weber, Griffin M (2018) Biases in electronic health record data due to processes within the healthcare system: retrospective observational study. BMJ 361:k1479
Can, Anil; Castro, Victor M; Dligach, Dmitriy et al. (2018) Lipid-Lowering Agents and High HDL (High-Density Lipoprotein) Are Inversely Associated With Intracranial Aneurysm Rupture. Stroke 49:1148-1154
Boag, Willie; Doss, Dustin; Naumann, Tristan et al. (2018) What's in a Note? Unpacking Predictive Value in Clinical Note Representations. AMIA Jt Summits Transl Sci Proc 2017:26-34
Xia, Yin; Cai, Tianxi; Cai, T Tony (2018) Multiple Testing of Submatrices of a Precision Matrix with Applications to Identification of Between Pathway Interactions. J Am Stat Assoc 113:328-339
Brown, Adam S; Patel, Chirag J (2018) A review of validation strategies for computational drug repositioning. Brief Bioinform 19:174-177
Xia, Yin; Cai, Tianxi; Cai, T Tony (2018) Two-Sample Tests for High-Dimensional Linear Regression with an Application to Detecting Interactions. Stat Sin 28:63-92
Kerpedjiev, Peter; Abdennur, Nezar; Lekschas, Fritz et al. (2018) HiGlass: web-based visual exploration and analysis of genome interaction maps. Genome Biol 19:125
Kothari, Cartik; Wack, Maxime; Hassen-Khodja, Claire et al. (2018) Phelan-McDermid syndrome data network: Integrating patient reported outcomes with clinical notes and curated genetic reports. Am J Med Genet B Neuropsychiatr Genet 177:613-624

Showing the most recent 10 out of 60 publications