Data Management

Learn about data ingestion, storage, analytics, and export in the Peregrine platform

FHIR Data Model

FHIR R4 Compliance

Peregrine uses FHIR R4 as the core data model, ensuring interoperability with modern healthcare systems.

Supported Resources

• Patient
• Encounter
• Condition
• Medication & MedicationRequest
• Observation
• Procedure
• AllergyIntolerance
• DiagnosticReport

Extensions

• US Core Profile support
• Custom extensions for federal use
• SDOH data elements
• Military service history
• Care coordination metadata

Example FHIR Resource

{
  "resourceType": "Patient",
  "id": "example-123",
  "meta": {
    "profile": ["http://hl7.org/fhir/us/core/StructureDefinition/us-core-patient"]
  },
  "identifier": [{
    "system": "http://va.gov/systems/patient-id",
    "value": "123456789"
  }],
  "name": [{
    "use": "official",
    "family": "Smith",
    "given": ["John", "Michael"]
  }],
  "gender": "male",
  "birthDate": "1945-07-15",
  "address": [{
    "use": "home",
    "line": ["123 Main St"],
    "city": "Arlington",
    "state": "VA",
    "postalCode": "22202"
  }],
  "extension": [{
    "url": "http://peregrine.health/fhir/StructureDefinition/military-service",
    "valueCodeableConcept": {
      "coding": [{
        "system": "http://va.gov/terminology/service-branch",
        "code": "army",
        "display": "United States Army"
      }]
    }
  }]
}

Data Ingestion Pipelines

Ingestion Methods

Real-time Streaming

Ingest data in real-time using Apache Kafka or AWS Kinesis

POST /api/v1/ingest/stream

Batch Upload

Process large datasets through scheduled batch jobs

POST /api/v1/ingest/batch

Direct Integration

Connect directly to EHR systems via HL7 or FHIR interfaces

Configured via Integration Portal

Data Pipeline Configuration

# pipeline-config.yaml
name: ehr-data-pipeline
source:
  type: fhir-server
  endpoint: https://ehr.hospital.gov/fhir
  auth:
    type: oauth2
    clientId: ${CLIENT_ID}
    
transformations:
  - deidentify:
      method: safe-harbor
      retain: [age, gender, zipcode3]
      
  - validate:
      schema: us-core-r4
      strictMode: true
      
  - enrich:
      - add-risk-scores
      - calculate-quality-measures
      
destination:
  type: peregrine-datastore
  format: fhir-ndjson
  compression: gzip
  
monitoring:
  alerts:
    - type: error-rate
      threshold: 0.01
    - type: latency
      threshold: 5000ms

Analytics & Queries

Query Language

Use our SQL-like query language optimized for healthcare data analytics.

Population Query Example

SELECT 
  patient.id,
  patient.age,
  COUNT(DISTINCT condition.code) as condition_count,
  RISK_SCORE(patient.id, 'readmission') as risk_score
FROM patients
JOIN conditions ON patient.id = condition.patient_id
WHERE 
  condition.code IN ('E11.9', 'I10') -- Diabetes and Hypertension
  AND patient.age >= 65
GROUP BY patient.id, patient.age
HAVING risk_score > 0.7
ORDER BY risk_score DESC

Pre-built Analytics

Clinical Analytics

• Disease prevalence analysis
• Medication adherence tracking
• Care gap identification
• Readmission risk prediction

Operational Analytics

• Resource utilization
• Appointment no-show rates
• Provider productivity
• Cost analysis

Data Export & Migration

Export Formats

FHIR

JSON, XML, NDJSON

Tabular

CSV, Parquet, Excel

Compliant

De-identified datasets

Migration Tools

Comprehensive tools for migrating data to and from Peregrine.

Migration Process

1
Schema Mapping
Map source data to FHIR resources
2
Validation
Verify data integrity and completeness
3
Incremental Sync
Keep data synchronized during transition
4
Cutover
Switch to Peregrine as source of truth

Data Governance & Security

Access Control

• Role-based access control (RBAC)
• Attribute-based policies
• Data masking and redaction
• Audit logging for all access

Compliance

• HIPAA compliant storage
• Encryption at rest and in transit
• Data retention policies
• Right to deletion support