Semantic Enrichment of Streaming Healthcare Data

TL;DR

This paper presents a method to enhance healthcare data interoperability by combining FHIR and RDF standards, enabling real-time data integration, querying, and automated inference without requiring source-specific knowledge.

Contribution

It introduces a novel approach that merges FHIR and RDF standards for real-time healthcare data integration and semantic querying.

Findings

Successful simulation of real-time data feeds

Data can be combined and queried without source-specific knowledge

Enhanced interoperability and automated inference demonstrated

Abstract

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.