From Research Standards to the Electronic Medical Record
While NHLBI focuses on making existing research data interoperable, ODSS is working on a complementary problem: getting research-grade standards into the clinical systems where new data is generated daily.
Susan Gregurick, NIH’s associate director for data science and director of ODSS, described a push to map NIH research standards into the United States Core Data for Interoperability (USCDI) — the interoperability standard that electronic medical record systems use for accreditation. Gregurick described efforts to map NIH research standards into clinical interoperability frameworks, noting work that began in oncology and is expanding to other disease areas, including a cardiovascular partnership with NHLBI.
The implication: When cardiovascular phenotypes appear in a patient encounter — even outside a formal study — EMR systems can capture them in a format researchers can use.
"The impact for that sort of cross-agency collaboration is really huge," Gregurick said. "I think that that's almost apart from AI, but it's going to be something that drives AI in the future."
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Curating Data for Machines With Common Data Elements
Underpinning much of this work is the National Library of Medicine, the world's largest biomedical research library. Lisa Federer, acting director of NLM's Office of Strategic Initiatives, described the library as providing "the substrate for future work in AI" through assets such as Medical Subject Headings (MeSH) and Common Data Elements, which standardize how research data is described and collected across NIH.
But what's shifting, Federer said, is who consumes the data.
"We're not just thinking about humans. We're thinking about how machines are consuming data as well," Federer said. "You do have to consider not just a human consuming that, but how is agentic AI going to be consuming this information?"
How NLM curates data "for machine consumption is different from how we would curate it for human consumption," Federer said, a recognition that AI agents querying NIH resources don't read context clues the way a researcher does.
This distinction makes standardizing Common Data Elements all the more important, especially as NLM has noted an uptick in the number of bots and other AI agents crawling their digital repositories.
What's at Stake for NIH and the Future of Health Research
NIH invested nearly $400 million last year in AI-related research grants, according to Gregurick. But the less visible investment — the pipelines, ontologies and standards that make data usable across institutional boundaries — may matter more.
"When you're able to [connect] that data with the real-world data, with other data that exists out in the research space, or in the health data fabric across the nation, or even internationally, you just increase the power of that data to be able to do more," Ladwa said, "ultimately to help those affected by diseases and disorders."
Without interoperable standards, 70 years of health data stays locked in the formats it was born in. With the standards, it becomes the foundation for a new generation of AI-driven medical research.
