Healthcare interoperability—the ability of health systems to exchange and use information—remains a persistent challenge. Despite decades of effort and billions invested, healthcare data remains fragmented. Patients get the same test twice, providers lack complete information, and care coordination suffers.
This guide provides a framework for understanding and addressing healthcare interoperability.
Understanding Interoperability
Interoperability Levels
Degrees of connection:
Technical interoperability: Systems can exchange data.
Syntactic interoperability: Data format is understood.
Semantic interoperability: Data meaning is shared.
Organizational interoperability: Workflows and policies align.
Why Interoperability Matters
Value of connected healthcare:
Clinical quality: Complete information for decisions.
Patient safety: Avoiding errors from missing information.
Care coordination: Connecting care across settings.
Efficiency: Reducing duplicate effort.
Population health: Data for public health and research.
Current Landscape
Standards Environment
Key interoperability standards:
HL7 v2: Ubiquitous but aging standard.
HL7 FHIR: Modern, API-based standard.
CCDA: Clinical document format.
USCDI: US Core Data for Interoperability.
IHE profiles: Integration specifications.
Network Landscape
How healthcare connects:
Point-to-point: Direct connections between systems.
Health information exchanges (HIE): Regional intermediaries.
National networks: TEFCA, CommonWell, Carequality.
EHR vendor networks: Epic Care Everywhere, etc.
Implementation Approaches
FHIR Implementation
Modern interoperability approach:
API-based: RESTful interfaces.
Resource-oriented: Standard data resources.
Implementation guides: Profiles for use cases.
App ecosystem: Third-party application access.
Exchange Network Participation
Joining exchange networks:
Network selection: Which networks to join.
Connection implementation: Technical integration.
Governance compliance: Meeting network requirements.
Use case enablement: What exchange supports.
Custom Integration
Building direct connections:
Point-to-point integration: Direct interface development.
Integration engine: Middleware for connections.
Legacy system challenge: Connecting older systems.
Maintenance burden: Ongoing support.
Regulatory Context
Current Requirements
Regulatory drivers:
21st Century Cures Act: Information blocking prohibition.
ONC Final Rule: Data access requirements.
CMS Interoperability Rules: Payer access requirements.
State regulations: Varying state requirements.
Compliance Considerations
Meeting requirements:
Information blocking: Avoiding prohibited practices.
Patient access: Enabling app access.
Payer data exchange: Required payer connections.
Documentation: Demonstrating compliance.
Implementation Challenges
Technical Challenges
Technology barriers:
Legacy systems: Old systems with limited capability.
Data quality: Inconsistent, incomplete data.
Semantic gaps: Same terms, different meanings.
Security concerns: Protecting sensitive data.
Organizational Challenges
Non-technical barriers:
Incentive misalignment: Competitive concerns.
Governance complexity: Multi-party coordination.
Resource constraints: Competing priorities.
Change resistance: Cultural and workflow challenges.
Key Takeaways
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FHIR is the future: Modern architecture direction.
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Networks matter more than standards: Standards enable; networks deliver.
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Semantic interoperability is hardest: Technical connection is insufficient.
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Regulation is accelerating change: Compliance driving investment.
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Patient access is priority: Consumer-facing interoperability first.
Frequently Asked Questions
Should we focus on FHIR or traditional HL7? FHIR for new development; maintain HL7 v2 for legacy connections.
Which networks should we join? At minimum, TEFCA-aligned networks. Evaluate regional HIEs for local exchange.
How do we handle information blocking compliance? Review practices against exceptions, document decisions, enable access.
What about EHR vendor interoperability? Leverage vendor networks; supplement with independent connections.
How do we improve data quality for exchange? Data governance, validation, cleanup initiatives. Quality at source is ideal.
What resources do we need? Integration engineers, clinical informatics, project management. Ongoing support commitment.