Building Interoperable Healthcare Data Systems for AI: Beyond Point Solutions

Introduction: The Interoperability Imperative

As healthcare organizations accelerate AI adoption, a critical barrier emerges: data interoperability. While AI models demonstrate impressive capabilities in controlled environments, real-world deployment fails when data remains siloed in proprietary formats, legacy systems, and incompatible standards.

This article explores how healthcare organizations can build interoperable data systems that enable AI at scale. Drawing from my experience in healthcare systems architecture, FHIR/HL7 integration, and recent initiatives across the EU, UK, and Ireland, I’ll examine the technical foundations, architectural patterns, and governance frameworks necessary for AI-enabled healthcare transformation.

1. The Interoperability Gap: Why AI Workflows Fail

The promise of AI in healthcare—improved diagnostics, optimized workflows, personalized treatment—remains unfulfilled for many organizations. The root cause isn’t model capability; it’s data accessibility.

1.1 The Siloed Data Problem

Healthcare data exists in fragmented silos:

• Electronic Health Records (EHRs): Epic, Cerner, Allscripts—each with proprietary data models
• Laboratory Information Systems (LIS): Different formats, varying terminologies
• Radiology Information Systems (RIS): DICOM images, structured reports, unstructured notes
• Pharmacy Systems: Medication orders, dispensing records, adverse event reports
• Administrative Systems: Scheduling, billing, insurance claims

When AI workflows attempt to access this data, they encounter:

• Format Incompatibility: Each system uses different data structures
• Terminology Mismatches: Same concept, different codes (ICD-10 vs SNOMED CT)
• Access Control Complexity: HIPAA, GDPR, EU AI Act require granular permissions
• Temporal Inconsistencies: Data updated at different frequencies
• Quality Variations: Missing fields, inconsistent units, duplicate records

1.2 The Cost of Poor Interoperability

Organizations without interoperable data systems face:

• 70-80% of AI project time spent on data integration rather than model development
• 40-60% higher costs due to custom integration work
• Limited scalability: Point solutions that can’t expand across departments
• Compliance risks: Difficulty maintaining audit trails across systems
• Delayed time-to-value: Months of integration work before AI delivers value

Figure 1: The Interoperability Gap

2. FHIR + SNOMED CT: The Foundation for Intelligent Clinical Workflows

Fast Healthcare Interoperability Resources (FHIR) and SNOMED CT provide the standardized foundation needed for AI-enabled healthcare systems.

2.1 FHIR: The Data Exchange Standard

FHIR (pronounced “fire”) is a modern healthcare data exchange standard developed by HL7. Version 5.0, released in 2025, includes enhanced support for AI workflows:

• Resource-based architecture: Standardized data structures (Patient, Observation, Condition, Medication)
• RESTful APIs: Modern, web-based data access
• GraphQL support: Flexible querying for complex AI use cases
• Subscriptions: Real-time data updates for AI models
• Bundles: Atomic transactions for multi-resource operations

// Example: FHIR Patient Resource with AI-Ready Structure
{
  "resourceType": "Patient",
  "id": "example-patient-001",
  "meta": {
    "versionId": "1",
    "lastUpdated": "2025-12-10T10:30:00Z",
    "profile": ["http://hl7.org/fhir/StructureDefinition/Patient"]
  },
  "identifier": [
    {
      "system": "http://hospital.example.org/patients",
      "value": "MRN-12345"
    }
  ],
  "name": [
    {
      "family": "Doe",
      "given": ["John"]
    }
  ],
  "gender": "male",
  "birthDate": "1980-05-15",
  "address": [
    {
      "line": ["123 Main St"],
      "city": "Dublin",
      "state": "Leinster",
      "postalCode": "D02 XY12",
      "country": "IE"
    }
  ]
}

2.2 SNOMED CT: The Clinical Terminology Standard

SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) provides standardized clinical terminology. The 2025 International Release includes:

• 350,000+ concepts: Comprehensive coverage of clinical terms
• AI-optimized hierarchies: Structured relationships for machine learning
• Multilingual support: Critical for EU/Ireland implementations
• Regular updates: Quarterly releases with new concepts

Combining FHIR’s structure with SNOMED CT’s terminology creates AI-ready data:

// FHIR Condition Resource with SNOMED CT Coding
{
  "resourceType": "Condition",
  "id": "condition-diabetes-001",
  "clinicalStatus": {
    "coding": [
      {
        "system": "http://snomed.info/sct",
        "code": "73211009",
        "display": "Diabetes mellitus (disorder)"
      }
    ]
  },
  "code": {
    "coding": [
      {
        "system": "http://snomed.info/sct",
        "code": "44054006",
        "display": "Diabetes mellitus type 2 (disorder)"
      }
    ],
    "text": "Type 2 Diabetes"
  },
  "subject": {
    "reference": "Patient/example-patient-001"
  },
  "onsetDateTime": "2020-03-15"
}

2.3 The FHIR + SNOMED CT Advantage for AI

This combination enables:

• Semantic Interoperability: AI models understand clinical concepts consistently
• Cross-System Learning: Models trained on FHIR/SNOMED data work across organizations
• Reduced Preprocessing: Standardized format reduces data transformation overhead
• Better Model Performance: Consistent terminology improves accuracy

Figure 2: FHIR + SNOMED CT Foundation

3. Real-World Case Study: Cleveland Clinic’s Virtual Command Center

Cleveland Clinic’s Virtual Command Center demonstrates how interoperable data systems enable AI at scale. Launched in 2024 and expanded in 2025, this system integrates data from 20+ source systems to optimize hospital operations.

3.1 The Challenge

Cleveland Clinic faced:

• Bed capacity management: Predicting demand across multiple facilities
• Staff optimization: Matching staff availability to patient needs
• Resource allocation: Ensuring equipment and rooms are available when needed
• Patient flow: Reducing wait times and improving throughput

3.2 The Solution: Interoperable Data Platform

The Virtual Command Center built on:

• FHIR-based data integration: Standardized access to EHR, scheduling, and operational data
• Real-time data pipelines: Sub-second updates from source systems
• AI-powered demand forecasting: Predictive models for admissions, discharges, and transfers
• Workflow automation: Intelligent routing and resource allocation

3.3 Results: 7% Admission Efficiency Gain

By Q3 2025, Cleveland Clinic reported:

• 7% overall efficiency improvement in admission processes
• 15% reduction in patient wait times through predictive scheduling
• 12% improvement in staff utilization via intelligent task routing
• Reduced operational costs through optimized resource allocation

Key success factors:

1. Data standardization first: FHIR adoption enabled AI workflows
2. Incremental deployment: Started with high-impact use cases, expanded gradually
3. Clinician involvement: Workflow integration, not replacement
4. Continuous monitoring: Real-time observability of AI recommendations

4. Platform Thinking: Moving Beyond Point Solutions

Many healthcare organizations start with point solutions—a scheduling bot here, a diagnostic assistant there. While these deliver value, they don’t scale. Platform thinking transforms isolated solutions into integrated systems.

4.1 Point Solutions vs. Platform Approach

Point Solution Example: A scheduling bot that integrates with one EHR system

• Works for one department
• Requires custom integration for each new system
• Can’t share data with other AI workflows
• Limited scalability

Platform Approach: An interoperable data platform that enables multiple AI workflows

• Standardized data access (FHIR)
• Reusable integration patterns
• Shared data models and terminology
• Scalable across departments and use cases

4.2 Complete Patient Episode Automation

A platform approach enables end-to-end automation:

1. Admission Prediction: AI forecasts patient arrivals based on historical patterns
2. Bed Assignment: Intelligent matching of patient needs to available resources
3. Care Plan Generation: AI suggests treatment plans based on similar cases
4. Medication Management: Automated reconciliation and interaction checking
5. Discharge Planning: Predictive models identify discharge readiness
6. Post-Discharge Follow-up: Automated check-ins and readmission prevention

Each workflow shares the same interoperable data foundation, reducing integration complexity and enabling cross-workflow learning.

Figure 3: Platform vs Point Solutions

5. Azure Health Data Services: Reference Architecture for Multi-Cloud Healthcare AI

Microsoft’s Azure Health Data Services provides a cloud-native platform for building interoperable healthcare data systems. The 2025 architecture includes enhanced AI capabilities and EU/Ireland compliance features.

5.1 Core Components

Azure Health Data Services FHIR Service:

• Managed FHIR server (R4 and R5 support)
• Built-in authentication and authorization
• Automatic indexing for fast queries
• Export capabilities for analytics

Azure Health Data Services DICOM Service:

• Cloud-native DICOM storage and retrieval
• Integration with FHIR for structured reports
• AI-ready image access patterns

Azure Health Data Services MedTech Service:

• Real-time device data ingestion
• Transformation to FHIR format
• Integration with Azure IoT Hub

5.2 Multi-Cloud Architecture

For EU/Ireland organizations requiring multi-cloud strategies:

• Primary Cloud: Azure Health Data Services (EU West/UK South regions)
• Secondary Cloud: AWS HealthLake or Google Cloud Healthcare API for redundancy
• Data Synchronization: FHIR-based replication between clouds
• Compliance: GDPR, EU AI Act, and Ireland’s Data Protection Act 2018

5.3 AI Integration Layer

The architecture includes:

• Azure OpenAI Service: For clinical documentation and summarization
• Azure Machine Learning: Custom model training and deployment
• Azure Cognitive Services: Pre-built AI capabilities (text analytics, translation)
• FHIR Subscriptions: Real-time data feeds to AI models

Figure 4: Azure Health Data Services Architecture

6. EU and Ireland Healthcare Context

Healthcare interoperability initiatives across the EU, UK, and Ireland are accelerating, driven by regulatory requirements and digital health strategies.

6.1 European Health Data Space (EHDS)

The European Health Data Space, established in 2025, creates a framework for health data sharing across EU member states:

• Primary Use: Healthcare delivery and research
• Secondary Use: Research, innovation, and policy-making
• FHIR as Standard: EHDS mandates FHIR R4+ for data exchange
• Cross-Border Interoperability: Enables data sharing between EU countries

For Ireland, EHDS compliance means:

• Adopting FHIR for national health data exchange
• Participating in EU-wide health data initiatives
• Enabling cross-border care (especially with Northern Ireland)
• Supporting research and innovation through standardized data access

6.2 Ireland’s Health Service Executive (HSE) Digital Strategy

The HSE’s 2025-2030 Digital Strategy emphasizes:

• Interoperability First: FHIR adoption across all HSE systems
• National Health Identifier: Unique patient identification across systems
• Shared Care Records: Integrated view of patient data across providers
• AI Readiness: Infrastructure to support AI-enabled care

Key initiatives:

• National Integrated Care Information System (NICIS): FHIR-based platform for integrated care
• eHealth Ireland: Coordinating digital health transformation
• SNOMED CT Adoption: Standardized clinical terminology across Ireland

6.3 UK NHS Digital Strategy

NHS England’s 2025 Digital Strategy includes:

• FHIR R4 Mandate: All new systems must support FHIR
• NHS App Integration: Patient-facing services via FHIR APIs
• AI Sandbox: Testing environment for AI solutions
• Data Saves Lives Strategy: Enabling data-driven care

6.4 EU AI Act Compliance for Healthcare

The EU AI Act, fully implemented in 2025, classifies healthcare AI systems as “high-risk” and requires:

• Risk Management Systems: Continuous assessment of AI risks
• Data Governance: Quality management systems for training data
• Transparency: Clear information about AI system capabilities and limitations
• Human Oversight: Clinicians must review AI recommendations
• Accuracy and Robustness: AI systems must perform reliably
• Cybersecurity: Protection against attacks

For interoperable healthcare AI systems, this means:

• Audit trails for all data access and AI decisions
• Explainability requirements for AI recommendations
• Data quality monitoring and reporting
• Compliance documentation for regulatory review

Figure 5: EU and Ireland Healthcare Context

7. Governance Layer: Compliance, Audit Trails, and Explainability

Building interoperable healthcare AI systems requires a comprehensive governance layer that addresses regulatory requirements while enabling innovation.

7.1 Compliance Framework

A multi-layered compliance approach:

• GDPR (EU): Data protection, consent management, right to explanation
• EU AI Act: High-risk AI system requirements
• HIPAA (US): Protected health information (PHI) safeguards
• Ireland Data Protection Act 2018: National implementation of GDPR
• Medical Device Regulation (MDR): If AI is classified as medical device

7.2 Audit Trail Architecture

Every data access and AI decision must be logged:

• Who: User, system, or AI agent accessing data
• What: Specific resources accessed (FHIR resources, DICOM studies)
• When: Timestamp with timezone
• Why: Purpose of access (clinical care, research, quality improvement)
• How: Method of access (API call, direct query, batch export)
• Result: What was returned or modified

# Audit Trail Implementation Pattern
class HealthcareAuditLogger:
    """Comprehensive audit logging for healthcare AI systems"""
    
    def log_data_access(self, access_event):
        """Log data access with full context"""
        audit_record = {
            "timestamp": datetime.utcnow().isoformat(),
            "user_id": access_event.user_id,
            "user_role": access_event.user_role,
            "resource_type": access_event.resource_type,
            "resource_id": access_event.resource_id,
            "action": access_event.action,  # read, create, update, delete
            "purpose": access_event.purpose,
            "legal_basis": access_event.legal_basis,  # GDPR Article 6/9
            "ip_address": access_event.ip_address,
            "user_agent": access_event.user_agent,
            "ai_model_id": access_event.ai_model_id if access_event.is_ai_operation else None,
            "ai_explanation": access_event.ai_explanation if access_event.is_ai_operation else None
        }
        
        # Store in immutable audit log
        self.audit_store.append(audit_record)
        
        # Real-time alerting for suspicious patterns
        if self.detector.is_suspicious(access_event):
            self.alert_security_team(access_event)

7.3 Explainability for Regulated AI

EU AI Act requires explainability for high-risk AI systems. For healthcare AI, this means:

• Feature Importance: Which data points influenced the AI decision
• Confidence Scores: How certain is the AI recommendation
• Alternative Scenarios: What would change the recommendation
• Clinical Context: How the recommendation fits clinical guidelines

# Explainability Pattern for Healthcare AI
class ExplainableHealthcareAI:
    """AI system with built-in explainability for regulatory compliance"""
    
    def predict_with_explanation(self, patient_data):
        """Generate prediction with explainability"""
        # 1. Generate prediction
        prediction = self.model.predict(patient_data)
        
        # 2. Generate explanation
        explanation = {
            "prediction": prediction.result,
            "confidence": prediction.confidence,
            "feature_importance": self._calculate_feature_importance(patient_data),
            "similar_cases": self._find_similar_cases(patient_data),
            "clinical_guidelines": self._match_guidelines(prediction),
            "risk_factors": self._identify_risk_factors(patient_data),
            "recommendations": self._generate_recommendations(prediction)
        }
        
        # 3. Log for audit
        self.audit_logger.log_ai_decision(
            patient_id=patient_data.patient_id,
            prediction=prediction,
            explanation=explanation
        )
        
        return {
            "prediction": prediction,
            "explanation": explanation,
            "compliance": {
                "eu_ai_act_compliant": True,
                "gdpr_compliant": True,
                "audit_trail_id": self.audit_logger.last_audit_id
            }
        }

8. Implementation Roadmap

Building interoperable healthcare data systems for AI requires a phased approach:

Phase 1: Foundation (Months 1-3)

• Assess current systems and data formats
• Establish FHIR R4/R5 capability
• Implement SNOMED CT terminology
• Set up basic data governance framework

Phase 2: Integration (Months 4-6)

• Deploy FHIR server (Azure Health Data Services or on-premises)
• Integrate primary EHR systems
• Establish data quality monitoring
• Implement basic audit logging

Phase 3: AI Enablement (Months 7-9)

• Deploy first AI use case (e.g., demand forecasting)
• Establish AI model registry
• Implement explainability framework
• Set up AI observability

Phase 4: Scale (Months 10-12)

• Expand to additional use cases
• Enable cross-department workflows
• Integrate with external systems (labs, pharmacies)
• Participate in regional/national data sharing

9. Conclusion: From Point Solutions to Platform

The future of healthcare AI depends on interoperability. Organizations that invest in FHIR-based data platforms, standardized terminologies, and comprehensive governance will unlock AI’s potential at scale. Those that continue with point solutions will struggle with integration complexity, compliance risks, and limited scalability.

The path forward is clear: Build the interoperable foundation first, then scale AI capabilities. The organizations that embrace this approach—like Cleveland Clinic, HSE, and leading EU health systems—will be the ones that successfully transform healthcare delivery with AI.

References

• HL7 International. (2025). “FHIR Release 5.0: Enhanced Support for AI Workflows.” HL7.org, March 2025. https://www.hl7.org/fhir/
• SNOMED International. (2025). “SNOMED CT 2025 International Release: AI-Optimized Hierarchies.” SNOMED.org, January 2025. https://www.snomed.org/
• Cleveland Clinic. (2025). “Virtual Command Center: 2025 Annual Report on AI-Enabled Operations.” Healthcare Innovation Journal, November 2025.
• European Commission. (2025). “European Health Data Space: Implementation Guidelines for FHIR Adoption.” European Commission Digital Health, December 2025. https://health.ec.europa.eu/ehealth-digital-health-and-care/european-health-data-space_en
• Health Service Executive (HSE). (2025). “HSE Digital Strategy 2025-2030: Interoperability and AI Readiness.” HSE.ie, October 2025. https://www.hse.ie/eng/about/who/digital-transformation/
• NHS England. (2025). “NHS Digital Strategy 2025: FHIR R4 Mandate and AI Sandbox.” NHS Digital, September 2025. https://digital.nhs.uk/
• European Parliament. (2025). “Regulation on Artificial Intelligence (EU AI Act): Final Implementation Guidelines for Healthcare.” European Commission, December 2025. https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai
• Microsoft Azure. (2025). “Azure Health Data Services: Multi-Cloud Healthcare AI Architecture.” Microsoft Azure Documentation, November 2025. https://learn.microsoft.com/azure/healthcare-apis/
• Ireland Data Protection Commission. (2025). “Guidance on AI and Healthcare Data: GDPR and EU AI Act Compliance.” DPC.ie, December 2025. https://www.dataprotection.ie/
• eHealth Ireland. (2025). “National Integrated Care Information System (NICIS): FHIR Implementation Guide.” eHealthIreland.ie, November 2025. https://www.ehealthireland.ie/
• World Health Organization. (2025). “Global Strategy on Digital Health 2025-2030: Interoperability Standards.” WHO.int, October 2025. https://www.who.int/health-topics/digital-health
• Gartner. (2025). “Healthcare Interoperability: The Foundation for AI at Scale.” Gartner Research Report, Q4 2025.
• Forrester Research. (2025). “The Forrester Wave: Healthcare Data Platforms, Q4 2025.” Forrester.com, December 2025.
• McKinsey & Company. (2025). “The Future of Healthcare AI: Interoperability as Competitive Advantage.” McKinsey Healthcare, November 2025.