VA enlists Energy’s super powers for health research

The Department of Veterans Affairs is putting the Energy Department’s super powers to work on the health records of half a million veterans to advance both precision medicine treatments and exascale computing.

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Known as the VA-DOE Big Data Science Initiative, the partnership leverages Energy’s national lab system and the Million Veteran Program’s digital health and genomic data sources to help advance health care for veterans and others in areas such as suicide prevention, cancer and heart disease.  The advanced data analytics also help to inform next-generation supercomputing designs.

MVP, VA’s landmark genomics program, has already enrolled more than 560,000 veteran volunteers, who have provided DNA samples; completed surveys about their health, lifestyle and military experiences; and granted secure access to their electronic health records for research purposes.

Additionally, VA has “unparalleled health data trend information from some 24 million veterans who have used VA for health care over the past two decades,” VA Secretary Dr. David Shulkin said. The use of DOE’s high-performance computing capabilities will allow “thousands of researchers access to this unprecedented data resource over time in a secure environment.”

Along with data from MVP and VA’s electronic health records system, the new VA-DOE program will use health data from the Department of Defense and Centers for Medicare and Medicaid Services, as well as the Center for Disease Control and Prevention’s National Death Index.

Specific studies already being planned. One aims to build algorithms to generate highly tailored personalized risk scores for suicide, which could be used by VA clinicians and researchers to help predict which patients are at the highest risk and to evaluate prevention strategies. The researchers will work with VA’s Office of Suicide Prevention to enhance current algorithms already in use in VA.

Another project that focuses on prostate cancer will look for new ways to distinguish between malignant and nonlethal, slow-growing tumors. A third will explore what risk factors can best predict certain forms of cardiovascular disease so that individualized therapy and treatments can be developed for patients based on their individual risk factors.

About the Author

Susan Miller is executive editor at GCN.

Over a career spent in tech media, Miller has worked in editorial, print production and online, starting on the copy desk at IDG’s ComputerWorld, moving to print production for Federal Computer Week and later helping launch websites and email newsletter delivery for FCW. After a turn at Virginia’s Center for Innovative Technology, where she worked to promote technology-based economic development, she rejoined what was to become 1105 Media in 2004, eventually managing content and production for all the company's government-focused websites. Miller shifted back to editorial in 2012, when she began working with GCN.

Miller has a BA from West Chester University and an MA in English from the University of Delaware.

Connect with Susan at smiller@gcn.com or @sjaymiller.

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