Federal Big Data Analytics in the Health Domain: An Ontological Approach to Data Interoperability

Abstract “Big data” in the health domain occupies a critical position on the federal policy and research agenda, with emphasis on leveraging large, complex data sets to manage population health, drive down disease rates, and control costs. The complexity of big data analytics requires new rules and algorithms to effect the interoperability of data derived from multiple sources. Accordingly, a lexicon and ontology-based approach to data interoperability is offered as a practical and adaptable framework to address challenges of data interoperability presented by big health data analytics and related issues. The use of ontologies as descriptive, heuristic, and normative instruments is presented as means for facilitating data interoperability by ensuring semantic congruity and syntactic conformance within and across large and complex data sets. A framework is provided for an ontological approach to health data interoperability, focusing on the importance of standards and considering implications for practice and policy in relevant federal agencies.