The COVID-19 pandemic raises many difficult questions, such as what are the key risk factors, what are the best prognostic indicators, and which drugs are the most viable candidates for patients. To address these and many other questions, the National Center for Data to Health (CD2H) and NCATS have created a national, centralized, secure Enclave for COVID-19 clinical data in the United States in partnership with the distributed clinical data networks PCORnet, OHDSI, ACT/i2b2, and TriNetX. The cloud-based Enclave supports machine learning and other informatics methods that require a large row-level dataset. In this presentation, we will provide an overview of the current functionality and efforts to characterize the cohort. We invite the community to leverage these data and work collaboratively together to reveal mechanisms and best clinical practices to improve COVID-19 patient outcomes and transform how we perform research as a nation.
Learning Objectives
- Understand the informatics and regulatory challenges of establishing a large-scale collection of centralized EHR data.
- Articulate the common data models used as source data for N3C integration, and how they are harmonized in the project.
- Describe the N3C data enclave, both from a security/confidentially projection perspective and as a rich analytic environment.
- Understand the scope of questions answerable with the N3C data and Machine Learning methods.
Speakers
Melissa A. Haendel, Ph.D.
Director of the Center for Data to Health (CD2H) at Oregon Health & Science University, and the Director of Translational Data Science at Oregon State University.
Dr. Christopher Chute
Bloomberg Distinguished Professor of Health Informatics, Professor of Medicine, Public Health, and Nursing at Johns Hopkins University, and Chief Research Information Officer for Johns Hopkins Medicine, Co-lead, N3C, Co-PI, Center for Data to Health
Ms. Anita Walden
Assistant Director of the National Center for Data to Health at Oregon Health & Science University
Sponsored content. The views and opinions expressed in this content or by commenters are those of the author and do not necessarily reflect the official policy or position of HIMSS or its affiliates.
Sponsored by Palantir, a HIMSS20 Artificial Intelligence / Machine Learning Circle Sponsor
