How NIH Is Translating 70 Years of Health Data To Speak the Same Language

12 Petabytes and a Converter Box

At the center of the effort is BioData Catalyst, a cloud-based ecosystem developed by NHLBI in collaboration with NLM and ODSS. 

“[NIH has] over 12 petabytes of data that’s all multimodal — everything from genomics, clinical imaging, sleep, sensor data, all different modalities,” said Sweta Ladwa, chief of the Scientific Solutions Delivery Branch at the Information Technology and Applications Center (ITAC) at NHLBI. That data spans long-running studies such as the Trans-Omics for Precision Medicine (TOPMed) program, which tracks approximately 180,000 individuals.

But access alone doesn’t make data AI-ready. The more difficult problem is interoperability — ensuring a cardiovascular variable from, say, a 1990s Framingham Heart Study cohort means the same thing as one from a recent pulmonary fibrosis study. NHLBI has built a linked data modeling language (LinkML) pipeline to solve this — what Ladwa called a “converter box approach, where you can plug in the data from the source and it will put it in that [format] for your analysis.” The pipeline maps data across multiple standards, including LOINC (Logical Observation Identifiers Names and Codes), FHIR (Fast Healthcare Interoperability Resources) and HPO (Human Phenotype Ontology).

NHLBI pairs the automated mapping with clinical validation. “We have pulmonologists who we’re working with to really clinically determine the concepts,” Ladwa said, “because we want to make sure that this fancy hypertensive medication is the same as this other one. They’re all in the same class, and they’re all the same ontological concept.” She added that the AI-assisted mapping uses “publicly available metadata” rather than patient data.

READ MORE: Healthcare interoperability improves care and patient experiences.

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. As part of this process, Gregurick cited 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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