Abstract
Data federation offers a way to get data moving from multiple sources providing advantages in healthcare systems where medical data is often hard to reach because of regulations or the lack of reliable solutions that can integrate on top of protocols like FHIR, HL7, DICOM, among others. Given the increasing need for solutions that augment healthcare systems with artificial intelligence (AI), in fields like genomics, cancer treatment, and radiology, all of which will require solutions that can provide data at scale while being traceable, safe, and regulatory-compliant. This paper proposes an architectural solution that may provide the core capabilities to implement a data federation approach in a healthcare system to enable AI.
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