A landmark federal interagency collaboration to promote data science in health care: Million Veteran Program-Computational Health Analytics for Medical Precision to Improve Outcomes Now.

Autor:	Justice AC; VA Connecticut Healthcare System, West Haven, CT 06516, United States.; Yale School of Medicine and Public Health, Yale University, New Haven, CT 06510, United States., McMahon B; Los Alamos National Laboratory, Los Alamos, NM 87545, United States., Madduri R; Argonne National Laboratory, Argonne, IL 60439, United States., Crivelli S; Lawrence Berkeley National Laboratory, Berkeley, CA 94720, United States., Damrauer S; Penn Heart and Vascular Center, University of Pennsylvania, Philadelphia, PA 19104, United States., Cho K; VA Boston Healthcare System, Boston, MA 02130, United States., Ramoni R; Department of Veteran's Affairs, Office of Research and Development, Veteran's Health Administration, Washington, DC 20571, United States., Muralidhar S; Department of Veteran's Affairs, Million Veteran Program, Veteran's Health Administration, Washington, DC 20420, United States.
Jazyk:	angličtina
Zdroj:	JAMIA open [JAMIA Open] 2024 Nov 06; Vol. 7 (4), pp. ooae126. Date of Electronic Publication: 2024 Nov 06 (Print Publication: 2024).
DOI:	10.1093/jamiaopen/ooae126
Abstrakt:	Objectives: In 2016, the Department of Veterans Affairs (VA) and the Department of Energy (DOE) established an Interagency Agreement (IAA), the Million Veteran Program-Computational Health Analytics for Medical Precision to Improve Outcomes Now (MVP-CHAMPION) research collaboration. Materials and Methods: Oversight fell under the VA Office of Research Development (VA ORD) and DOE headquarters. An Executive Committee and 2 senior scientific liaisons work with VA and DOE leadership to optimize efforts in the service of shared scientific goals. The program supported centralized data management and genomic analysis including creation of a scalable approach to cataloging phenotypes. Cross-cutting methods including natural language processing, image processing, and reusable code were developed. Results: The 79.6 million dollar collaboration has supported centralized data management and genomic analysis including a scalable approach to cataloging phenotypes and launched over 10 collaborative scientific projects in health conditions highly prevalent in veterans. A ground-breaking analysis on the Summit and Andes supercomputers at the Oak Ridge National Laboratory (ORNL) of the genetic underpinnings of over 2000 health conditions across 44 million genetic variants which resulted in the identification of 38 270 independent genetic variants associating with one or more health traits. Of these, over 2000 identified associations were unique to non-European ancestry. Cross-cutting methods have advanced state-of-the-art artificial intelligence (AI) including large language natural language processing and a system biology study focused on opioid addiction awarded the 2018 Gordon Bell Prize for outstanding achievement in high-performance computing. The collaboration has completed work in prostate cancer, suicide prevention, and cardiovascular disease, and cross-cutting data science. Predictive models developed in these projects are being tested for application in clinical management. Discussion: Eight new projects were launched in 2023, taking advantage of the momentum generated by the previous collaboration. A major challenge has been limitations in the scope of appropriated funds at DOE which cannot currently be used for health research. Conclusion: Extensive multidisciplinary interactions take time to establish and are essential to continued progress. New funding models for maintaining high-performance computing infrastructure at the ORNL and for supporting continued collaboration by joint VA-DOE research teams are needed. Competing Interests: None declared. (Published by Oxford University Press on behalf of the American Medical Informatics Association 2024.)
Databáze:	MEDLINE
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