The PREcision Medicine Initiative Enrollment and Retention (PREMIER) cohort will be assembled to reflect the diverse populations of California. Our goal is to enroll a minimum of 10,000 representative participants during a 12-month period. Participants will be consented using an NIH central IRB protocol and will share biospecimens, anthropometric data (e.g., waist circumference), survey responses, and electronic health records (EHRs) with the PMI Cohort Program (PMI-CP). Participants will be engaged through multiple media including non-proprietary applications developed by the PMI-CP. The inclusion of six large healthcare provider organizations throughout California in the PREMIER cohort will ensure that we meet our enrollment goals. To facilitate interactions with PMI-CP leadership, there will be a single program management office (PMO) at UCSD that will be charged with all communications to site PIs at each institution. Importantly, our large, diverse consortium of seasoned investigators has a long history of past collaboration and productive scientific interactions, creating substantial efficiencies in the use of common research resources, providing further assurance that we will fulfill our contractual obligations. This collaboration included the transformation and periodic updating of EHRs from all our patients (>10 million patients in our six initial participating institutions) into the Observational Medical Outcomes Partnership (OMOP) and PCORnet common data models (CDM). Data from PMI participants will be centralized at UCSD, which will submit these data to the Data and Research Support Center (DRSC). We expect to be in close contact with the Participant Technology Center (PTC) and the DRSC, as well as with NIH administration to ensure that PREMIER members are active participants in various PMI-CP Working Groups. We are ready to implement the NIH Central IRB protocol, and quickly adapt existing processes or adopt new ones to conform to the PMI-CP protocols.
| Bloss, Cinnamon S; Stoler, Justin; Schairer, Cynthia E et al. (2018) Characteristics Of Likely Precision Medicine Initiative Participants Drawn From A Large Blood Donor Population. Health Aff (Millwood) 37:786-792 |
| Butte, Atul J (2017) Big data opens a window onto wellness. Nat Biotechnol 35:720-721 |
| Butte, Atul J (2016) It takes a genome to understand a village: Population scale precision medicine. Proc Natl Acad Sci U S A 113:12344-12346 |
