Healthcare IT systems need to exchange patient data with Electronic Health Records (EHRs), but EHR integration is notoriously complex.

Here are patterns that actually work in production healthcare systems.

1. Integration Standards

Healthcare uses specific standards:

HL7 v2: Legacy messaging standard still widely used. Message-based, requires parsing and transformation.

HL7 FHIR: Modern RESTful API standard. More developer-friendly, better for modern applications.

DICOM: For medical imaging data. Requires specialized handling and storage.

X12: For administrative transactions (claims, eligibility, referrals).

IHE Profiles: Integration profiles that define how standards should be used together.

2. FHIR Implementation

FHIR (Fast Healthcare Interoperability Resources) is becoming the standard:

Resources: FHIR defines resources (Patient, Observation, Medication) that represent healthcare concepts.

RESTful APIs: FHIR uses standard HTTP methods, making it familiar to web developers.

Search and filtering: Rich search capabilities for finding patients, observations, and other resources.

Extensions: Ability to extend FHIR resources for organization-specific needs while maintaining compatibility.

Bundles: Group multiple resources together for efficient batch operations.

3. Security and Privacy

Healthcare data requires strict security:

HIPAA compliance: Encrypt data in transit (TLS) and at rest, implement access controls, and maintain audit logs.

Authentication: Strong authentication (OAuth 2.0, SAML) for API access.

Authorization: Role-based access control aligned with clinical roles and responsibilities.

Audit logging: Log all access to patient data for compliance and security monitoring.

Data minimization: Only request and store data that’s necessary for the use case.

4. Common Integration Patterns

Real-world EHR integrations use these patterns:

Point-to-point: Direct integration between two systems. Simple but doesn’t scale.

Integration engine: Central hub that routes messages between multiple systems. More scalable and maintainable.

API gateway: Gateway that provides unified API to multiple EHR systems, handling differences internally.

Event-driven: Publish-subscribe model where systems publish events (admissions, discharges) that others consume.

5. Data Synchronization

Keep data in sync between systems:

Real-time sync: Immediate updates when data changes in EHR. Best for critical data but more complex.

Batch sync: Periodic synchronization of data. Simpler but data may be stale.

Hybrid approach: Real-time for critical updates, batch for less time-sensitive data.

Conflict resolution: Handle cases where same data is updated in multiple systems.

Data quality: Validate and clean data during synchronization to maintain quality.

6. Error Handling and Resilience

EHR systems can be unreliable:

Retry logic: Automatic retries with exponential backoff for transient failures.

Circuit breakers: Stop calling failing EHR systems to prevent cascading failures.

Fallback mechanisms: Graceful degradation when EHR systems are unavailable.

Monitoring and alerting: Detect integration failures quickly and alert appropriate teams.

Manual intervention: Processes for handling errors that can’t be automatically resolved.

7. Testing Strategies

Test EHR integrations thoroughly:

Sandbox environments: Use EHR vendor sandboxes for development and testing.

Mock services: Mock EHR APIs for unit and integration testing.

Test data: Use realistic but anonymized test data that represents real-world scenarios.

End-to-end testing: Test complete workflows from patient registration through care delivery.

Compliance testing: Validate that integrations meet HIPAA and other regulatory requirements.

EHR integration is complex, but with the right patterns and careful implementation, healthcare IT teams can build reliable, secure integrations that improve patient care and operational efficiency. At NsisongLabs, we’ve integrated with Epic, Cerner, and other major EHR systems, and we’ve learned that success comes from understanding both the technical standards and the clinical workflows they support.