FAIR enough: Building an academic data ecosystem to make real-world data available for translational research

Isabella Chu; Rebecca Miller; Ian Mathews; Ayin Vala; Lesley Sept; Ruth O’Hara; David H. Rehkopf

doi:10.1017/cts.2024.530

Journal of Clinical and Translational Science (Jan 2024)

FAIR enough: Building an academic data ecosystem to make real-world data available for translational research

Isabella Chu,
Rebecca Miller,
Ian Mathews,
Ayin Vala,
Lesley Sept,
Ruth O’Hara,
David H. Rehkopf

Affiliations

Isabella Chu: ORCiD; The Stanford Center for Population Health Sciences, Stanford School of Medicine, Stanford University, Palo Alto, CA, USA
Rebecca Miller: The Stanford Center for Population Health Sciences, Stanford School of Medicine, Stanford University, Palo Alto, CA, USA
Ian Mathews: Redivis, Inc, Oakland, CA, USA
Ayin Vala: The Stanford Center for Population Health Sciences, Stanford School of Medicine, Stanford University, Palo Alto, CA, USA
Lesley Sept: The Stanford Center for Population Health Sciences, Stanford School of Medicine, Stanford University, Palo Alto, CA, USA
Ruth O’Hara: Psychiatry and Behavioral Sciences, Stanford School of Medicine, Stanford University, Stanford, CA, USA Veterans Administration Palo Alto Health Care System, Sierra Pacific Mental Illness, Research, Education, and Clinical Center, Palo Alto, CA, USA
David H. Rehkopf: ORCiD; The Stanford Center for Population Health Sciences, Stanford School of Medicine, Stanford University, Palo Alto, CA, USA Department of Epidemiology and Population Health, Stanford School of Medicine, Stanford University, Stanford, CA, USA Department of Medicine, Division of Primary Care and Population Health, Stanford School of Medicine, Stanford University, Stanford, CA, USA Department of Pediatrics, Stanford School of Medicine, Stanford University, Stanford, CA, USA Department of Health Policy, Stanford School of Medicine, Stanford University, Stanford, CA, USA Department of Sociology, Stanford University, Stanford, CA, USA

DOI: https://doi.org/10.1017/cts.2024.530
Journal volume & issue: Vol. 8

Abstract

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The Stanford Population Health Sciences Data Ecosystem was created to facilitate the use of large datasets containing health records from hundreds of millions of individuals. This necessitated technical solutions optimized for an academic medical center to manage and share high-risk data at scale. Through collaboration with internal and external partners, we have built a Data Ecosystem to host, curate, and share data with hundreds of users in a secure and compliant manner. This platform has enabled us to host unique data assets and serve the needs of researchers across Stanford University, and the technology and approach were designed to be replicable and portable to other institutions. We have found, however, that though these technological advances are necessary, they are not sufficient. Challenges around making data Findable, Accessible, Interoperable, and Reusable remain. Our experience has demonstrated that there is a high demand for access to real-world data, and that if the appropriate tools and structures are in place, translational research can be advanced considerably. Together, technological solutions, management structures, and education to support researcher, data science, and community collaborations offer more impactful processes over the long-term for supporting translational research with real-world data.

Published in Journal of Clinical and Translational Science

ISSN: 2059-8661 (Online)
Publisher: Cambridge University Press
Country of publisher: United Kingdom
LCC subjects: Medicine
Website: https://www.cambridge.org/core/journals/journal-of-clinical-and-translational-science

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