Healthcare Data Integration Hub
Real-time integration across HIS, EHR, IoT, and enterprise systems
Real-time Data Pipeline Architecture
Azure Event Hub
HL7 FHIR Listener
Validate
Entity Resolution
Maintenance
Utilization
Healthcare Integration Protocols
Healthcare Interoperability Expertise
FHIR Resources Supported
HIS/EMR Systems Experience
- InterSystems TrakCare & HealthShare
- Epic MyChart & Hyperspace
- Oracle Cerner Millennium
- MEDITECH Expanse
- Allscripts Professional EHR
- Custom HMIS (Sanitas, eHospital)
Compliance & Security
- HIPAA compliant data handling
- DPDP Act (India) adherence
- NABH digital health standards
- ABDM/NDHM integration ready
- End-to-end TLS 1.3 encryption
- PHI tokenization & masking
Entity Field Mappings
Detailed field-level mappings showing how data flows from source systems to the unified data model.
Data Quality Dimensions
Data Quality Issues
Overall Quality Score
Resolution Stats
Live Integration Events
| Time | System | Event | Records | Status |
|---|
Active Integration Challenges
Healthcare-Specific Data Challenges We Handle
Real integration issues that require deep healthcare domain expertise to detect and resolve:
HL7/FHIR Message Handling
Medical Equipment Data Issues
IoT & Telemetry Challenges
Maintenance & Vendor Data Issues
AI-Powered Resolution Engine
Machine learning models trained on healthcare data patterns to auto-detect and resolve common integration issues:
Domain Expertise in Action
Generic ETL tools can't understand that "CT Scanner" in one system and "Computed Tomography Unit" in another refer to the same GMDN category (10614). We maintain healthcare equipment ontology mappings.
We know that AERB-regulated equipment (X-ray, CT, Nuclear Medicine) requires different compliance tracking than non-radiation devices. Our integration flags regulatory category automatically.
A ventilator showing "idle" at 3 AM is normal. The same reading at 3 PM in ICU is concerning. We correlate device telemetry with patient census and clinical schedules for intelligent alerting.