Layer Architecture

1. Ingestion Layer: Real-time FHIR data capture via webhooks/streaming

2. Processing Layer: Schema validation, tokenization, and normalization

3. Embedding Layer: BioBERT vectorization with domain-specific models

4. Storage Layer: Vector database with metadata indexing

5. Blockchain Layer: Hyperledger Fabric for audit and consent

6. Aggregation Layer: Model Context Protocol server for query orchestration

7. Presentation Layer: React Native UI with OASIS/USCDI displays

1.1 Configure FHIR Subscription Endpoints:

• Set up webhook listeners for FHIR resource notifications (Patient, Observation, MedicationRequest, Condition, etc.)
• Implement TLS 1.2+ encryption with mutual authentication for QHIN connections
• Configure API keys or OAuth 2.0 for authorization

1.2 Deploy Streaming Infrastructure:

• Install Apache Kafka cluster with topics per resource type (e.g., "fhir.observation", "fhir.patient")
• Configure retention policies and replication factors for durability
• Set up Kafka Connect for integration with existing HL7 interfaces if needed

1.3 Schema Loader Implementation:

• Load official HL7 FHIR JSON schemas for each resource type
• Parse schemas to create internal field maps with type definitions and cardinality
• Resolve $ref references to build complete resource structure definitions
• Validate incoming resources against schemas for data quality

Dynamic Schema Loading Process:

• Load Patient.schema.json, Observation.schema.json, and other resource schemas

• Build hierarchical field maps: Patient.name.given, Observation.component.code

• Create type registries for proper handling of Quantities, CodeableConcepts, References

• Cache parsed schemas in memory for performance

2.1 Build Schema-Driven Tokenizer:

• Create recursive parser that traverses FHIR JSON using schema definitions
• Generate nested token structures mirroring JSON hierarchy (not flattened lists)
• Preserve parent-child relationships: Patient → Contact → Telecom
• Handle arrays by tokenizing each element with its index position maintained

2.2 Implement Primitive Classification:

• Categorize resources by clinical domain: Demographics, Vitals, Labs, Medications, Conditions
• Apply LOINC/SNOMED mappings to determine sub-categories
• Route resources to specialized processing based on primitive type
• Tag resources with classification metadata for downstream filtering

2.3 Configure User-Defined Tags:

• Enable configuration for custom tags at resource or field level
• Support tags like: source_facility, data_sensitivity, cohort_membership
• Inject tags during tokenization to travel with data through pipeline

Input: Patient Resource with Nested Contacts

Patient.id: "123"

Patient.name[0].family: "Doe"

Patient.name[0].given: ["John", "A."]

Patient.contact[0].relationship.coding[0].code: "E"

Patient.contact[0].telecom[0].value: "555-1234"

Output: Hierarchical Token Structure

Maintains structure with tokens organized as nested objects, not flattened strings. Each contact remains grouped with its telecom and relationship data. Arrays preserve element boundaries.

Validation Checkpoints:

• Verify all schema-defined fields are captured in tokens

• Confirm array indices are preserved for ordered data

• Test that deeply nested structures (3+ levels) maintain integrity

• Validate classification accuracy against known test resources

3.1 Quantity Normalization (UCUM Standards):

• Convert all mass measurements to grams (5.4 mg → 0.0054 g)
• Standardize volume to liters (500 mL → 0.5 L)
• Normalize time units to seconds for durations
• Apply UCUM library for complex unit conversions (mmHg, mEq/L)
• Preserve original units in metadata for display purposes

3.2 Temporal Normalization:

• Convert all timestamps to UTC ISO 8601 format
• Standardize date formats to YYYY-MM-DD
• Calculate relative time markers for trending (days since admission)
• Handle partial dates with appropriate precision indicators

3.3 Coded Value Standardization:

• Map local codes to standard terminologies (LOINC, SNOMED, RxNorm)
• Normalize case for enumerated values (Final → final)
• Resolve synonyms to canonical forms
• Handle version differences in code systems

Blood Pressure Handling

• Separate systolic/diastolic components while maintaining pairing

• Normalize both to mmHg if provided in other units

• Preserve measurement position (sitting, standing, lying)

Laboratory Results

• Convert to standard reference range units per LOINC definitions

• Calculate normalized ratios for interpretability

• Handle both numeric and categorical results appropriately

Recursive Normalization Process:

1. Traverse token structure depth-first

2. Apply type-specific normalizers at leaf nodes

3. Propagate normalized values up the hierarchy

4. Maintain original values in parallel for audit

5. Flag any normalization failures for manual review

4.1 Deploy Specialized Models:

• BioClinicalBERT: For clinical notes and narrative text (110M parameters)
• MedBERT: For structured EHR data and coded values
• DistilBERT-Clinical: For high-volume resources requiring speed (66M parameters)
• SciBERT: For laboratory and research-oriented data

4.2 Configure Inference Pipeline:

• Set up GPU cluster with NVIDIA Triton for model serving
• Implement batch processing (10-50 resources per batch)
• Configure model routing based on resource classification
• Enable dynamic batching for optimal GPU utilization

4.3 Multi-Granularity Generation:

• Generate resource-level embeddings (entire Patient record)
• Create sub-resource embeddings (each Contact, each Observation)
• Produce component-level vectors (individual lab values, vital signs)
• Maintain linkage between embedding levels via metadata

Token Serialization for BERT Input

Structured Format: "Patient: id=123, name.family=Doe, name.given=John; contact[0].relationship=Emergency, contact[0].telecom=555-1234"

Hierarchical Markers: Include path indicators to preserve structure in text form

Context Windows: Manage 512-token limit through intelligent truncation

Special Tokens: Add [CLS] for classification, [SEP] for boundaries

Embedding Validation:

• Verify vector dimensions match model output (768 for BERT-base)

• Test semantic similarity on known related concepts

• Monitor embedding distribution for anomalies

• Validate that hierarchical context affects embedding values

• Benchmark inference latency per resource type

5.1 Core Metadata Tags:

• Identity Tags: patient_id, resource_id, resource_type, subtype
• Temporal Tags: observation_time, ingestion_time, last_updated
• Provenance Tags: source_system, facility_name, practitioner_id, device_id
• Hierarchical Tags: parent_resource, child_resources[], nesting_level
• Clinical Tags: encounter_id, episode_of_care, care_team_id

5.2 Access Control Metadata:

• Consent Flags: consent_level (full/partial/restricted), consent_expiry
• Sensitivity Markers: contains_mental_health, contains_substance_abuse
• Regulatory Tags: HIPAA_restricted, 42CFR_protected
• Role-Based Access: allowed_roles[], denied_roles[]

5.3 Research & Analytics Tags:

• Cohort Membership: study_enrollment[], research_protocol_id
• Quality Indicators: data_quality_score, completeness_percentage
• CHI Factors: risk_indicators[], comorbidity_flags[]
• Population Segments: age_group, diagnosis_groups[], risk_tier

Blockchain Implementation Steps

Network Setup:

1. Deploy Fabric network with healthcare consortium members

2. Configure channels for data integrity and consent management

3. Install chaincode for hash recording and consent validation

4. Set up Certificate Authority for identity management

Transaction Flow:

1. Compute SHA-256 hash of normalized FHIR resource

2. Create transaction with hash, timestamp, and metadata

3. Submit to Fabric network for endorsement

4. Receive transaction ID upon block confirmation

5. Attach ledger_tx_id and ledger_hash to vector metadata

{
  "vector_id": "Observation/ABC123",
  "vector": [0.123, -0.045, ...],
  "metadata": {
    "patient_id": "12345",
    "resource_type": "Observation",
    "subtype": "vital_sign",
    "parent": "Encounter/789",
    "timestamp": "2025-08-28T19:30:00Z",
    "source": "Hospital_ABC",
    "consent_level": "full_access",
    "ledger_tx": "fab-tx-890abc",
    "ledger_hash": "SHA256:123abc...",
    "chi_factors": ["hypertension", "obesity"],
    "access_roles": ["physician", "nurse"]
  }
}
        

6.1 Vector Database Options:

• Milvus (Recommended): Open-source, distributed, supports billions of vectors
• Weaviate: GraphQL interface, built-in modules for healthcare
• Qdrant: Rust-based for performance, excellent filtering capabilities
• Pinecone: Managed service option for reduced operational overhead

6.2 Index Configuration:

• Deploy HNSW (Hierarchical Navigable Small World) index for similarity search
• Configure index parameters: M=16, ef_construction=200 for accuracy/speed balance
• Create compound indexes on metadata fields: patient_id + resource_type
• Enable vector quantization for memory optimization if needed

6.3 Collection Structure:

• Create separate collections for resource types or unified with type filtering
• Define schema with vector dimension (768 for BERT) and metadata fields
• Configure sharding strategy for horizontal scaling
• Set up replication factor of 3 for high availability

Parent-Child Linking Strategy

Forward References: Each parent stores array of child vector IDs

Backward References: Each child stores parent vector ID

Relationship Types: Distinguish contains, references, derives_from

Graph Traversal: Enable multi-hop queries through relationship chains

Optimization Strategies:

• Batch insertions in groups of 1000 vectors for throughput

• Use async writes with eventual consistency for non-critical data

• Implement connection pooling with 10-50 concurrent connections

• Cache frequently accessed vectors in Redis layer

• Partition data by date for time-based queries

Access Control Implementation:

• Enable TLS for all client connections

• Implement API key authentication per client application

• Configure row-level security based on metadata tags

• Encrypt vectors at rest using AES-256

• Audit log all query operations with user identity

7.1 API Endpoint Design:

• GET /patient/{id}/summary - Complete patient overview with CHI score
• GET /patient/{id}/section/{oasis_section} - OASIS-specific data retrieval
• GET /patient/{id}/uscdi/{category} - USCDI-compliant data classes
• GET /population/cohort/{cohort_id} - Population analytics
• POST /query/semantic - Free-text semantic search across records

7.2 Query Orchestration Logic:

• Parse incoming requests to identify data requirements
• Check user authorization via Fabric blockchain consent records
• Construct optimized vector database queries with filters
• Aggregate results from multiple granularity levels
• Transform raw vectors back to structured FHIR elements

7.3 Data Transformation Pipeline:

• Map FHIR resources to OASIS assessment sections
• Align data elements with USCDI required fields
• Calculate derived metrics (trends, aggregates, risk scores)
• Format responses for mobile UI consumption

Automated Section Population

Section A (Demographics): Patient resource → Name, DOB, Address

Section B/C (Sensory/Cognitive): Observations + Assessments → Cognitive scores

Section D (Mood): PHQ-9/GAD-7 Observations → Depression/Anxiety indicators

Section F (ADL/Functional): PT/OT assessments → Mobility status

Section M/N (Medications): MedicationRequests → Active med list with risks

Section O (Treatments): Procedures + CarePlans → Interventions

Caching Strategy:

• Cache patient demographics (TTL: 1 hour)

• Cache CHI scores (TTL: 5 minutes)

• Cache OASIS section data (TTL: 15 minutes)

• Implement cache invalidation on data updates

• Use Redis for distributed cache across MCP instances

{
  "patient_id": "12345",
  "chi": {
    "score": 78,
    "risk_level": "High",
    "trend": "Increasing",
    "factors": ["Heart Failure", "Recent Weight Gain"]
  },
  "oasis_sections": {
    "F": {
      "ambulation": "Requires Assistance",
      "adl_score": 4
    }
  },
  "uscdi": {
    "medications": [...],
    "problems": [...]
  }
}
        

8.1 Node Creation Strategy:

• Resource Nodes: Patient, Encounter, Observation, Condition, Medication
• Concept Nodes: LOINC codes, SNOMED concepts, ICD diagnoses
• Organization Nodes: Facilities, departments, care teams
• Temporal Nodes: Episodes of care, admission periods
• Derived Nodes: Risk cohorts, CHI tiers, quality measures

8.2 Edge Definition:

• HAS_OBSERVATION: Patient → Observation
• CONTAINS: Encounter → multiple Observations
• ADMINISTERED: MedicationAdministration → Patient
• DIAGNOSED_WITH: Patient → Condition
• IS_A: Concept → Parent Concept (ontology hierarchy)
• REFERENCES: Observation → another Observation

8.3 Property Attachment:

• Node properties: timestamps, status, identifiers
• Edge properties: relationship strength, temporal validity
• Graph metadata: version, last_updated, data_source

Terminology Mapping Pipeline

LOINC Integration:

• Load LOINC hierarchy (class → subclass → specific tests)

• Map Observation codes to LOINC concept nodes

• Create IS_A relationships for concept subsumption

SNOMED CT Mapping:

• Import SNOMED concept hierarchy

• Link Conditions to SNOMED disorder concepts

• Connect Procedures to SNOMED procedure hierarchy

RxNorm Medications:

• Map drug codes to RxNorm concepts

• Include ingredient, brand name, and class relationships

Technology Stack:

• Neo4j for property graph storage and Cypher queries

• GraphQL API layer for flexible querying

• Bulk loader for initial graph population from vectors

• Change data capture for real-time updates

• Graph algorithms library for centrality, clustering

Clinical Decision Support Query

"Find all patients with similar presentation to Patient X who improved with Treatment Y"

1. Vector similarity search for similar clinical profiles

2. Graph traversal to find their treatment paths

3. Outcome analysis through connected observations

4. Return ranked treatment recommendations

9.1 Core Screen Components:

• Patient Dashboard: CHI score banner, alert indicators, summary metrics
• OASIS Section Views: Tabbed interface for sections A-O with auto-population
• USCDI Data Browser: Categorized view of standard data elements
• Population Analytics: Sortable patient lists with risk stratification
• Trend Visualizations: CHI history, vital sign graphs, lab trends

9.2 State Management Architecture:

• Redux store for global application state
• Separate slices for patient data, UI state, cache management
• Redux-Persist for offline capability
• WebSocket connections for real-time updates

9.3 Data Synchronization:

• Poll MCP server every 30 seconds for updates
• WebSocket subscription for critical alerts
• Optimistic UI updates with rollback on failure
• Background sync when app returns to foreground

End-to-End Data Flow

1. Ingestion: FHIR resource arrives from QHIN → Kafka

2. Processing: Schema validation → Tokenization → Normalization

3. Embedding: BioBERT vectorization → Multi-granularity generation

4. Storage: Vector DB insertion → Blockchain audit logging

5. Aggregation: MCP server queries → OASIS/USCDI formatting

6. Presentation: React Native rendering → User interaction

Mobile Optimization Techniques:

• Lazy loading of OASIS sections

• Virtual scrolling for large patient lists

• Image optimization for medical imaging thumbnails

• Debounced search with type-ahead

• Memoization of expensive computations

✓ HIPAA-compliant infrastructure (BAA agreements, encryption)

✓ Load testing completed (1000+ concurrent users)

✓ Disaster recovery plan tested (RPO: 1 hour, RTO: 4 hours)

✓ Security audit passed (penetration testing, OWASP compliance)

✓ Clinical validation completed (95% accuracy on test cohort)

✓ User training materials prepared

✓ Rollback procedures documented