Healthcare HL7/FHIR Interoperability Modernization

Problem Statement

A multi-facility provider group needed a unified clinical workflow across cardiology diagnostics and core EHR systems, but data remained fragmented between GE MUSE and Epic EHR, creating delayed clinician decisions and duplicate manual entry.

Technical Architecture

We delivered a Java Spring Boot integration backbone with HL7 v2 parsers, FHIR resource mapping services, and event-driven synchronization using Kafka. Redis was introduced for low-latency session state and deduplication buffers, while PostgreSQL maintained auditable clinical message lineage.

Technology Stack: Java, Spring Boot, HL7 v2, FHIR, Kafka, Redis, PostgreSQL, AWS

Business Transformation

Clinical and diagnostic data began flowing bi-directionally between GE MUSE and Epic with consistent semantic mapping, enabling care teams to access synchronized patient records in near real time.

Quantifiable Outcomes

• 74% reduction in manual reconciliation between systems
• 52% faster cardiology report availability in EHR workflows
• 99.99% message delivery reliability across integration channels