FHIRViz: Multi-Agent Platform for FHIR Visualization to Advance Healthcare Analytics

Abstract

The shift to electronic health records (EHRs) has enhanced patient care and research, but data sharing and complex clinical terminology remain challenges. The Fast Healthcare Interoperability Resource (FHIR) addresses interoperability issues, though extracting insights from FHIR data is still difficult. Traditional analytics often miss critical clinical context, and managing FHIR data requires advanced skills that are in short supply. This study presents FHIRViz, a novel analytics tool that integrates FHIR data with a semantic layer via a knowledge graph. It employs a large language model (LLM) system to extract insights and visualize them effectively. A retrieval vector store improves performance by saving successful generations for fine-tuning. FHIRViz translates clinical queries into actionable insights with high accuracy. Results show FHIRViz with GPT-4 achieving 92.62% accuracy, while Gemini 1.5 Pro reaches 89.34%, demonstrating the tool’s potential in overcoming healthcare data analytics challenges.