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Rapid Response Data Discovery (R2D2) for COVID-19 Clinical Consultations 

Project Description

This grant to the University of California, San Diego will support the rapid deployment of a cross-institutional, EHR-based data warehouse that is capable of responding to complex cross-institutional queries to inform COVID-19 clinical management. The project will support the modification of an existing clinical data network called the pSCANNER (patient-centered SCAlable National Network for Effectiveness Research) which includes 12 hospital-based partnering health care systems with a designated Principal Investigator at each site. Ambulatory sites are not included. The network is coordinated by UCSD. Other member institutions include the national Veterans Affairs health system, University of Southern California and Cedars-Sinai Medical Center, San Mateo Medical Center, University of Colorado, University of Texas Health in Houston and UCLA, UCSD, UC Irvine, UCSF and UC Davis. The clinical data network is architected so that, instead of moving data to a central site, queries are distributed and run at each site and de-identified, and aggregated results are presented to the investigator. This approach minimizes the regulatory and privacy concerns associated with a central repository and allows investigators to virtually pool their data to answer impactful questions with greater certainty and speed.

Principal Investigator (PI): Lucila Ohno-Machado (UCSD)

Co-PIs: Michale Aratow (San Mateo Medical Center), Douglas S Bell (UCLA), Jason N Doctor (USC), Ludwig C Hinske (LMU), Katherine K Kim (UC Davis), Michael E Mathenty (VA), Daniella Meeker (USC), Mark J Pletcher (UCSF), Lisa M Schilling (University of Colorado Anschutz), Spencer SooHoo (Cedars Sinai), Hua Xu (UT Houston), Kai Zheng (UC Irvine)

Staff: Tyler Bath, Michele Day, Jihoon Kim, Paulina Paul, and Kai Post

Grant: Gordon and Betty Moore Foundation

Start Date: April 2020

Expected Duration: One year (extended)

 

Want to find out more?

Visit https://covid19questions.org and https://www.moore.org/grant-detail?grantId=GBMF9639