pSCANNER: patient-centered Scalable National Network for Effectiveness Research JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION Ohno-Machado, L., Agha, Z., Bell, D. S., Dahm, L., Day, M. E., Doctor, J. N., Gabriel, D., Kahlon, M. K., Kim, K. K., Hogarth, M., Matheny, M. E., Meeker, D., Nebeker, J. R., pSCANNER Team 2014; 21 (4): 621-626


This article describes the patient-centered Scalable National Network for Effectiveness Research (pSCANNER), which is part of the recently formed PCORnet, a national network composed of learning healthcare systems and patient-powered research networks funded by the Patient Centered Outcomes Research Institute (PCORI). It is designed to be a stakeholder-governed federated network that uses a distributed architecture to integrate data from three existing networks covering over 21 million patients in all 50 states: (1) VA Informatics and Computing Infrastructure (VINCI), with data from Veteran Health Administration's 151 inpatient and 909 ambulatory care and community-based outpatient clinics; (2) the University of California Research exchange (UC-ReX) network, with data from UC Davis, Irvine, Los Angeles, San Francisco, and San Diego; and (3) SCANNER, a consortium of UCSD, Tennessee VA, and three federally qualified health systems in the Los Angeles area supplemented with claims and health information exchange data, led by the University of Southern California. Initial use cases will focus on three conditions: (1) congestive heart failure; (2) Kawasaki disease; (3) obesity. Stakeholders, such as patients, clinicians, and health service researchers, will be engaged to prioritize research questions to be answered through the network. We will use a privacy-preserving distributed computation model with synchronous and asynchronous modes. The distributed system will be based on a common data model that allows the construction and evaluation of distributed multivariate models for a variety of statistical analyses.

View details for DOI 10.1136/amiajnl-2014-002751

View details for Web of Science ID 000337660600012

View details for PubMedID 24780722

View details for PubMedCentralID PMC4078293