Semantic Enrichment of Streaming Healthcare Data
This addresses the high cost and inefficiency of healthcare data integration for patients, hospitals, and insurers, but it is incremental as it builds on existing standards.
The paper tackles the problem of interoperability in healthcare data by combining FHIR and RDF standards to integrate and query disparate data sources in real-time, demonstrating through simulations that client applications can use the data without knowledge of underlying sources.
In the past decade, the healthcare industry has made significant advances in the digitization of patient information. However, a lack of interoperability among healthcare systems still imposes a high cost to patients, hospitals, and insurers. Currently, most systems pass messages using idiosyncratic messaging standards that require specialized knowledge to interpret. This increases the cost of systems integration and often puts more advanced uses of data out of reach. In this project, we demonstrate how two open standards, FHIR and RDF, can be combined both to integrate data from disparate sources in real-time and make that data queryable and susceptible to automated inference. To validate the effectiveness of the semantic engine, we perform simulations of real-time data feeds and demonstrate how they can be combined and used by client-side applications with no knowledge of the underlying sources.