Seamless real-time integration between disparate clinical systems.
The Challenge
The hospital’s LIS and Radiology Information System (RIS) were generating HL7 v2.xmessages (e.g., ORU^R01, ADT^A01), but the new EHR system used FHIR RESTAPIs and JSON-based interfaces. The hospital faced the following issues:
- Lack of interoperability between legacy HL7 systems and modern cloud platforms.
- Manual processes for data reconciliation and duplicate entry.
- No real-time visibility or tracking of message failures or delays.
- Delayed care due to message routing errors and transformation gaps.
Objectives
- Automate and streamline HL7 message flow.
- Enable real-time integration with modern cloud-based systems.
- Provide a scalable and configurable engine to support new HL7versions (e.g., FHIR).
- Improve patient care delivery through timely and accurate data availability..
Solution : HL7 Fluid Engine Implementation
A custom HL7 Fluid Engine was developed and deployed using a hybrid on-prem and cloud architecture.
Key Components:
- HL7 Listener- Receives MLLP-based HL7 v2 messages (ORU, ADT,ORM).
- Parser Module- Built using HAPI HL7 (Java) and Node.js HL7 Parser toextract and validate data.
- Transformation Engine- Translates HL7 v2 segments into FHIR-compatible JSON, enabling smooth integration with cloud APIs.
- Routing Logic- Dynamic routing based on message type, content(e.g., department, patient ID), and system availability.
- Error Handling & Alerts- Real-time logging, retry queues, andSMS/email alerts for failures or exceptions.
- Dashboard & Monitoring- Centralized UI to monitor message flow, status, latency, and system health.
Cloud Integration:
- Azure Functions for message transformation
- Azure Logic Apps for orchestrating FHIR API calls
- Azure Blob Storage for archival
- Power BI for reporting and monitoring KPIs
Results & Benefits
- 99.5% Message Success Rate.
- 80% Reduction in Manual Entry.
- Real-time Dashboards.
- FHIR Compatibility.
- Improved Patient Experience.