Integrating multimodal datasets using the Data COUNTS framework across regional and national networks establishes a scalable infrastructure for translational science and predictive modeling.
Implementing standardized data models and federated pipelines connects local health systems with national networks to accelerate translational science and precision medicine.
Objectives/Goals: The NIH Office of Data Science and Strategy created Data COUNTS (Collect Once, Use Numerous TimeS) to transform real-world data into actionable insights for translational research. The NEurOinformatics Network (NEON) applies this principle with neurology and national cohort use cases. Methods/Study Population: UNMC and Nebraska Medicine are deploying Data COUNTS to standardize data capture, ensure interoperability, and maximize reuse across research, clinical care, and operations. Teams integrate electronic health records, neuroimaging, biomarkers, wearable sensors, and social drivers of health into federated pipelines. HL7 FHIR and the OMOP Common Data Model maintain interoperability and reproducibility. Use cases include neurological disorders (multiple sclerosis, Alzheimer’s disease, and Parkinson’s disease), cancer outcomes through SEER, diabetes, and rural health disparities. Integration with the National Clinical Cohort Collaborative (N3C) leverages anchor variables and harmonized pipelines to connect local and national datasets for generalizable analytics and collaborative science. Results/Anticipated Results: By linking with CTSA, IDeA-CTR, SEER, N3C, and other national health data, this initiative strengthens Learning Health System infrastructure, accelerates precision medicine, and advances population health. Beginning at NM and UNMC, the model will extend to regional systems, especially in rural and underserved areas. Use cases in neurology, cancer, diabetes, and rural health demonstrate local impact, while SEER and N3C provide scalable pathways for harmonization and national integration. Discussion/Significance of Impact: Nebraska’s leading health system generates multimodal datasets that, connected with SEER and N3C through Data COUNTS principles, enable predictive modeling, explainable AI, and digital twins. These use cases operationalize a scalable framework for translational science.
Lienemann et al. (Wed,) conducted a other in Translational science and health informatics. Data COUNTS framework was evaluated. Integrating multimodal datasets using the Data COUNTS framework across regional and national networks establishes a scalable infrastructure for translational science and predictive modeling.
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