Precision Medicine Data Integration Core
- Institution
- Johns Hopkins University, MD
- Principal investigator
- Clifton O Bingham
- Funding
- $185K
- Duration
- 2016–2026
Moonbase score
Still being scored
Abstract
Core B Precision Medicine Data Integration PROJECT SUMMARY Rheumatic diseases are heterogeneous in phenotypes, kinetics of events and trajectory, and response to therapy. Defining relatively homogenous disease subgroups can foster identification of patients who share underlying pathophysiologic mechanisms, risks of complications and response to distinct therapeutic strategies – thereby laying a critical foundation for developing precision medicine approaches to the rheumatic diseases. As approaches that utilize multiple clinical and biologic characteristics to subgroup patients will likely improve our predictive capability, it is critical to harness tools and technologies that allow aggregation and harmonization of multiple streams data. Johns Hopkins Medicine has built a system for big data capture, aggregation and harmonization. This tool, known as the Precision Medicine Analytics Platform, captures high quality longitudinal data including deeper phenotypic data at the point of care, granular results from diagnostic systems and measurements from outside the health care environment. In this proposal, we seek to bring these tools and technologies to our rheumatology subspecialty centers in Lupus, Vasculitis, Rheumatoid Arthritis, Psoriatic Arthritis, Scleroderma, Myositis, Sjogren’s syndrome, Lyme, and Cancer Immune-Related Adverse Events to accelerate research in the rheumatic diseases. The Specific Aims of the Precision Medicine Data Integration Core are to: 1) facilitate and accelerate research study implementation, while ensuring stringent research ethics and data security practices; and 2) transform data collection methods to harness institutional “big data,” improve efficiency and enable remote study progress. The Core is led by experts in research program building, research ethics and data security practices, and data capture and harmonization methods.
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