A modern, scalable healthcare platform built with Node.js/TypeScript, implementing FHIR R4 standards for pediatric care management. This demo showcases enterprise-grade healthcare software development best practices and modern FHIR implementation patterns.

• FHIR R4 Compliance: Full implementation of FHIR resources for Patient, Observation, and Appointment
• Pediatric-Focused: Specialized data models and workflows for children with complex medical needs
• Care Complexity Tracking: Built-in support for care complexity levels (low, moderate, high, critical)
• Vital Signs Management: Age-appropriate reference ranges for pediatric patients
• Appointment Scheduling: Comprehensive appointment management with telehealth support

• TypeScript: Full type safety across the entire codebase
• Scalable Architecture: Microservices-ready design with clean separation of concerns
• AWS Native: Designed for AWS cloud deployment with Terraform IaC
• Production Ready: Comprehensive logging, monitoring, and error handling
• Security First: HIPAA-compliant design patterns and security best practices

• Node.js 18+
• Docker and Docker Compose
• AWS CLI (for deployment)
• Terraform (for infrastructure)

1. Clone and Install

   git clone <repository-url>
   cd fhir-demo-node
   npm install

2. Environment Setup

   cp .env.example .env
   # Edit .env with your configuration

3. Start Development Server

   npm run dev

4. With Docker Compose

   docker-compose up -d

The API will be available at http://localhost:3000/api/v1

• GET /api/v1/health - Service health check
• GET /api/v1/health/ready - Readiness probe
• GET /api/v1/health/live - Liveness probe

• POST /api/v1/patients - Create patient
• GET /api/v1/patients - List patients (with pagination and search)
• GET /api/v1/patients/:id - Get patient details
• PUT /api/v1/patients/:id - Update patient
• DELETE /api/v1/patients/:id - Delete patient
• GET /api/v1/patients/:id/fhir - Get raw FHIR Patient resource

• POST /api/v1/observations - Create observation
• GET /api/v1/observations/:id - Get observation
• PUT /api/v1/observations/:id - Update observation
• GET /api/v1/observations/patient/:patientId - Get patient observations
• GET /api/v1/observations/patient/:patientId/vital-signs - Get vital signs
• GET /api/v1/observations/patient/:patientId/vital-signs/latest - Latest vital signs

• POST /api/v1/appointments - Create appointment
• GET /api/v1/appointments/:id - Get appointment
• PUT /api/v1/appointments/:id - Update appointment
• POST /api/v1/appointments/:id/cancel - Cancel appointment
• POST /api/v1/appointments/:id/check-in - Check-in patient
• POST /api/v1/appointments/:id/complete - Complete appointment
• GET /api/v1/appointments/patient/:patientId - Patient appointments
• GET /api/v1/appointments/upcoming - Upcoming appointments

This platform is architected according to the Johns Hopkins Health Informatics Stack framework, demonstrating enterprise-grade healthcare system design:

• Frontend (Planned): React-based clinical dashboards and patient portals
• API Documentation: Comprehensive API docs for developer experience
• Health Endpoints: Real-time system monitoring and status interfaces

• Clinical Applications:
  • Patient management system with pediatric specialization
  • Appointment scheduling with telehealth support
  • Vital signs tracking with age-appropriate ranges
• Care Coordination: Emergency contact management and care team workflows
• Workflow Engine: Appointment state management (booked → checked-in → fulfilled)

• Clinical Decision Support:
  • Pediatric vital signs reference ranges with automated interpretation
  • Care complexity scoring (low/moderate/high/critical)
  • Age-based health metrics analysis
• Population Health: Patient cohort management and search capabilities
• Quality Metrics: Comprehensive audit logging for quality improvement

• FHIR R4 Implementation: Full compliance with HL7 FHIR standards
  • Patient resources with US Core profiles
  • Observation resources with LOINC coding
  • Appointment resources with SNOMED specialty codes
• API Gateway: RESTful APIs with standardized response formats
• External Integration Ready: Designed for integration with existing EHR systems

• Data Storage:
  • DynamoDB with optimized healthcare data models
  • Global Secondary Indexes for efficient patient queries
  • FHIR-compliant resource storage
• Data Governance:
  • HIPAA-ready security patterns
  • Data minimization for privacy protection
  • Comprehensive audit trails with correlation IDs
• Master Data Management: Unified patient identity with medical record numbers

• Cloud Infrastructure:
  • AWS ECS Fargate for serverless container orchestration
  • Application Load Balancer with health checks
  • Auto-scaling based on clinical demand
• Security Services:
  • API rate limiting and request validation
  • Security headers and CORS policies
  • Input sanitization and error handling
• Monitoring & Observability:
  • CloudWatch metrics and alarms
  • Structured logging with healthcare-specific correlation

• Network Security:
  • VPC with public/private subnet architecture
  • Security groups with least-privilege access
  • HTTPS-ready with security best practices
• Connectivity:
  • Internet Gateway for public access
  • Private networking for secure database access
  • Load balancer for high availability

• Backend: Node.js 18+, TypeScript, Express.js
• Validation: Joi for request validation with healthcare-specific rules
• Logging: Winston with structured logging and audit trails
• Security: Helmet, CORS, rate limiting, HIPAA compliance patterns
• Infrastructure: AWS (ECS Fargate, DynamoDB, ALB, CloudWatch)
• IaC: Terraform for complete infrastructure management
• CI/CD: GitHub Actions with automated testing and deployment
• Standards: FHIR R4, LOINC, SNOMED CT, US Core profiles

• FHIR Version: R4 (4.0.1)
• Resources: Patient, Observation, Appointment
• Extensions: Custom pediatric care complexity extensions
• Profiles: US Core Patient profiles where applicable
• Standards: LOINC codes for observations, SNOMED for specialties

graph TB
    subgraph "Internet & CDN"
        INTERNET[Internet Traffic]
        ROUTE53[Route 53 DNS]
    end
    
    subgraph "Load Balancing & Security"
        ALB[Application Load Balancer]
        WAF[Web Application Firewall]
        CERT[SSL/TLS Certificates]
    end
    
    subgraph "VPC: Healthcare Network"
        subgraph "Public Subnets (Multi-AZ)"
            ALB_PUB[ALB Endpoints]
            NAT[NAT Gateway]
        end
        
        subgraph "Private Subnets (Multi-AZ)"
            ECS["ECS Fargate Tasks<br/>FHIR API Containers"]
            LAMBDA["Lambda Functions<br/>Clinical Processing"]
        end
    end
    
    subgraph "Data Layer"
        DYNAMO["DynamoDB Tables<br/>- Patients<br/>- Observations<br/>- Appointments"]
        S3["S3 Buckets<br/>Clinical Documents"]
        RDS["RDS Future<br/>Relational Clinical Data"]
    end
    
    subgraph "Monitoring & Security"
        CLOUDWATCH["CloudWatch<br/>Metrics & Logs"]
        SECRETS["Secrets Manager<br/>API Keys & Certificates"]
        IAM["IAM Roles<br/>Least Privilege Access"]
    end
    
    subgraph "Development & Deployment"
        ECR["ECR Container Registry"]
        CODEBUILD["CodeBuild<br/>CI/CD Pipeline"]
        TERRAFORM["Terraform State<br/>Infrastructure as Code"]
    end

    %% Traffic Flow
    INTERNET --> ROUTE53
    ROUTE53 --> ALB
    ALB --> WAF
    WAF --> ALB_PUB
    ALB_PUB --> ECS
    ECS --> DYNAMO
    ECS --> S3
    ECS --> CLOUDWATCH
    
    %% Security & Access
    ECS --> SECRETS
    ECS --> IAM
    LAMBDA --> DYNAMO
    
    %% Deployment Flow
    CODEBUILD --> ECR
    ECR --> ECS
    TERRAFORM --> ECS
    
    %% Monitoring
    ALB --> CLOUDWATCH
    DYNAMO --> CLOUDWATCH
    
    %% Styling
    classDef internet fill:#1565C0,stroke:#0D47A1,stroke-width:2px,color:#FFFFFF
    classDef security fill:#D32F2F,stroke:#B71C1C,stroke-width:2px,color:#FFFFFF
    classDef compute fill:#388E3C,stroke:#1B5E20,stroke-width:2px,color:#FFFFFF
    classDef data fill:#F57C00,stroke:#E65100,stroke-width:2px,color:#FFFFFF
    classDef monitor fill:#7B1FA2,stroke:#4A148C,stroke-width:2px,color:#FFFFFF
    classDef deploy fill:#455A64,stroke:#263238,stroke-width:2px,color:#FFFFFF

    class INTERNET,ROUTE53 internet
    class ALB,WAF,CERT,IAM,SECRETS security
    class ECS,LAMBDA compute
    class DYNAMO,S3,RDS data
    class CLOUDWATCH monitor
    class ECR,CODEBUILD,TERRAFORM deploy

• Compute: ECS Fargate for serverless containers
• Database: DynamoDB with GSI for efficient queries
• Load Balancing: Application Load Balancer with health checks
• Monitoring: CloudWatch with custom metrics and alarms
• Scaling: Auto-scaling based on CPU and memory utilization

The platform currently uses DynamoDB as the primary database, optimized for:

• Real-time FHIR operations: Sub-millisecond response times for clinical workflows
• Flexible schema: Perfect for FHIR's JSON-based resource structure
• High throughput: Handles concurrent clinical users without performance degradation
• Global Secondary Indexes: Efficient patient and observation queries

The architecture includes RDS (Amazon Relational Database Service) for future enhancement:

Why RDS for Healthcare Analytics?

-- Example: Complex pediatric care analytics requiring SQL
SELECT p.patient_id, p.age, p.care_complexity,
       COUNT(o.observation_id) as vital_count,
       AVG(CASE WHEN o.code = '8867-4' THEN o.value END) as avg_heart_rate,
       COUNT(a.appointment_id) as appointment_count
FROM patients p 
LEFT JOIN observations o ON p.patient_id = o.patient_id
LEFT JOIN appointments a ON p.patient_id = a.patient_id
WHERE p.care_complexity IN ('high', 'critical')
  AND o.effective_date >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY p.patient_id, p.age, p.care_complexity
HAVING COUNT(o.observation_id) > 10;

Enterprise Healthcare Use Cases:

• Clinical Quality Metrics: Hospital scorecard calculations requiring complex joins
• Population Health Analytics: Multi-dimensional patient cohort analysis
• Research Capabilities: Longitudinal studies across thousands of patients
• Regulatory Reporting: CMS quality measures and public health reporting
• Financial Analytics: Cost-per-episode and value-based care calculations
• Clinical Decision Support: Complex rule engines with multi-table dependencies

Hybrid Architecture Benefits:

┌─────────────────────┐    ┌─────────────────────┐
│   DynamoDB          │    │   RDS PostgreSQL    │
│                     │    │                     │
│ • FHIR Resources    │◄──►│ • Clinical Analytics│
│ • Real-time Access  │    │ • Complex Reporting │
│ • Flexible Schema   │    │ • Data Relationships│
│ • High Performance  │    │ • SQL Capabilities  │
└─────────────────────┘    └─────────────────────┘

Industry Standard Pattern: This hybrid approach reflects real-world enterprise healthcare architecture:

• Epic, Cerner, AllScripts: Use both NoSQL and relational databases
• FHIR + SQL: Common pattern in modern healthcare data platforms
• Data Lake Architecture: Separates operational and analytical workloads
• Regulatory Compliance: Often requires SQL for complex audit queries

The platform includes sophisticated Global Secondary Indexes for efficient healthcare queries:

1. Medical Record Number Index (Patients Table):

global_secondary_index {
  name            = "medical-record-number-index"
  hash_key        = "medical_record_number"
  projection_type = "ALL"
}

// Enables instant patient lookup by MRN:
const patient = await dynamodb.query({
  IndexName: 'medical-record-number-index',
  KeyConditionExpression: 'medical_record_number = :mrn',
  ExpressionAttributeValues: { ':mrn': 'MRN-12345' }
});
// Result: Sub-millisecond clinical workflows

2. Patient-DateTime Index (Observations & Appointments):

// Efficient time-series queries for vital signs:
const vitalSigns = await dynamodb.query({
  IndexName: 'patient-id-datetime-index',
  KeyConditionExpression: 'patient_id = :pid AND effective_date_time BETWEEN :start AND :end',
  ExpressionAttributeValues: {
    ':pid': 'patient-123',
    ':start': '2024-01-01T00:00:00Z',
    ':end': '2024-12-31T23:59:59Z'
  }
});
// Result: Instant clinical trend analysis

Healthcare Query Benefits:

• Clinical Efficiency: Instant patient lookup during emergencies
• Trend Analysis: Real-time vital signs monitoring over time periods
• Appointment Management: Efficient scheduling and calendar queries
• Performance: Sub-millisecond response times for critical care workflows

Future Enhancement: Complete Clinical Audit Trail

Healthcare requires comprehensive audit trails for regulatory compliance. Event sourcing provides:

Current State Storage (what we have now):

{
  "patientId": "123",
  "name": "Emma Johnson", 
  "condition": "stable",
  "lastUpdated": "2024-01-15T10:00:00Z"
}

Event Sourcing Approach (future enhancement):

[
  {
    "eventType": "PatientAdmitted",
    "patientId": "123",
    "data": { "name": "Emma Johnson", "age": 5 },
    "timestamp": "2024-01-15T08:00:00Z",
    "userId": "dr-smith"
  },
  {
    "eventType": "VitalSignsRecorded", 
    "patientId": "123",
    "data": { "heartRate": 95, "temperature": 98.6 },
    "timestamp": "2024-01-15T09:30:00Z",
    "userId": "nurse-jones"
  },
  {
    "eventType": "ConditionAssessed",
    "patientId": "123", 
    "data": { "condition": "stable", "notes": "Normal pediatric parameters" },
    "timestamp": "2024-01-15T10:00:00Z",
    "userId": "dr-smith"
  }
]

Healthcare Benefits of Event Sourcing:

• HIPAA Compliance: Complete audit trail of all patient data changes
• Legal Protection: Immutable record of clinical decisions and timing
• Clinical Research: Historical analysis for treatment effectiveness
• Quality Assurance: Track care quality metrics over time
• Time Travel: Reconstruct patient state at any point in history
• Blame-Free Analysis: Understand what happened without changing history

Implementation Strategy:

• Phase 1 (Current): DynamoDB for operational FHIR data and real-time clinical workflows
• Phase 2 (Future): Add RDS for clinical data warehouse, advanced analytics, and research capabilities
• Phase 3 (Event Sourcing): Implement event streams for complete clinical audit trails and compliance
• Phase 4 (Advanced): Data lake with automated ETL pipelines for population health insights

This design demonstrates enterprise healthcare architecture thinking - recognizing that different data access patterns require different database technologies, which is essential for scalable pediatric care platforms in modern healthcare environments.

• Input Validation: Comprehensive request validation with Joi
• Rate Limiting: Configurable rate limiting to prevent abuse
• Security Headers: Helmet.js for security headers
• CORS: Configurable CORS policies
• Health Checks: Multiple health check endpoints
• Audit Logging: Structured logging for all operations
• Error Handling: Secure error responses without information leakage

# Run all tests
npm test

# Run tests in watch mode
npm run test:watch

# Run linting
npm run lint

# Type checking
npm run typecheck

1. Configure AWS Credentials

   aws configure

2. Deploy Infrastructure

   cd terraform
   terraform init
   terraform plan
   terraform apply

3. Build and Push Docker Image

   # This is automated via GitHub Actions
   docker build -t fhir-pediatric-care .

• Health Checks: Built-in health endpoints for load balancer checks
• Metrics: CloudWatch metrics for performance monitoring
• Logs: Structured JSON logging with correlation IDs
• Alarms: CPU and memory utilization alarms
• Auto-scaling: Automatic scaling based on load

This platform demonstrates comprehensive coverage of the Johns Hopkins Health Informatics Stack layers:

graph TB
    subgraph "Layer 7: User Interface & Experience"
        UI[React Clinical Dashboards]
        API_DOCS[API Documentation]
        HEALTH[Health Status Interfaces]
    end
    
    subgraph "Layer 6: Applications & Workflows"
        PATIENT[Patient Management System]
        APPT[Appointment Scheduling]
        VITALS[Vital Signs Tracking]
        CARE[Care Coordination Workflows]
    end
    
    subgraph "Layer 5: Analytics & Decision Support"
        CDS[Clinical Decision Support]
        ANALYTICS[Pediatric Analytics Engine]
        QUALITY[Quality Metrics & Reporting]
        POPULATION[Population Health Management]
    end
    
    subgraph "Layer 4: Integration & Interoperability"
        FHIR[FHIR R4 APIs]
        REST[RESTful Interfaces]
        HL7[HL7 Integration Layer]
        EHR[EHR Integration Ready]
    end
    
    subgraph "Layer 3: Data Management & Governance"
        DYNAMO[DynamoDB Healthcare Storage]
        AUDIT[Audit & Compliance Logging]
        MDM[Master Data Management]
        GOVERN[Data Governance Policies]
    end
    
    subgraph "Layer 2: Infrastructure & Platform Services"
        ECS[ECS Fargate Containers]
        MONITOR[CloudWatch Monitoring]
        SECURITY[Security Services]
        SCALING[Auto-Scaling Platform]
    end
    
    subgraph "Layer 1: Network & Connectivity"
        VPC[VPC Network Architecture]
        ALB[Application Load Balancer]
        IGW[Internet Gateway]
        SUBNET[Private/Public Subnets]
    end

    %% Connections between layers
    UI --> PATIENT
    API_DOCS --> FHIR
    PATIENT --> CDS
    APPT --> ANALYTICS
    VITALS --> CDS
    CDS --> FHIR
    ANALYTICS --> REST
    FHIR --> DYNAMO
    REST --> AUDIT
    DYNAMO --> ECS
    AUDIT --> MONITOR
    ECS --> VPC
    MONITOR --> ALB
    ALB --> IGW

    %% Styling
    classDef layer7 fill:#0D47A1,stroke:#01579B,stroke-width:2px,color:#FFFFFF
    classDef layer6 fill:#4A148C,stroke:#6A1B9A,stroke-width:2px,color:#FFFFFF
    classDef layer5 fill:#1B5E20,stroke:#2E7D32,stroke-width:2px,color:#FFFFFF
    classDef layer4 fill:#E65100,stroke:#F57C00,stroke-width:2px,color:#FFFFFF
    classDef layer3 fill:#B71C1C,stroke:#C62828,stroke-width:2px,color:#FFFFFF
    classDef layer2 fill:#1565C0,stroke:#1976D2,stroke-width:2px,color:#FFFFFF
    classDef layer1 fill:#455A64,stroke:#546E7A,stroke-width:2px,color:#FFFFFF

    class UI,API_DOCS,HEALTH layer7
    class PATIENT,APPT,VITALS,CARE layer6
    class CDS,ANALYTICS,QUALITY,POPULATION layer5
    class FHIR,REST,HL7,EHR layer4
    class DYNAMO,AUDIT,MDM,GOVERN layer3
    class ECS,MONITOR,SECURITY,SCALING layer2
    class VPC,ALB,IGW,SUBNET layer1

graph LR
    subgraph "Clinical Data Sources"
        CLINICIAN[Clinician Input]
        DEVICES[Medical Devices]
        EXTERNAL[External EHR Systems]
    end
    
    subgraph "FHIR Processing Layer"
        VALIDATE[Input Validation]
        TRANSFORM[Data Transformation]
        ENRICH[Clinical Enrichment]
    end
    
    subgraph "FHIR Resources"
        PATIENT_R["Patient Resource<br/>US Core Profile"]
        OBS_R["Observation Resource<br/>LOINC Codes"]
        APPT_R["Appointment Resource<br/>SNOMED Specialties"]
    end
    
    subgraph "Clinical Decision Support"
        PEDIATRIC[Pediatric Rules Engine]
        COMPLEXITY[Care Complexity Scoring]
        ALERTS[Clinical Alerts]
    end
    
    subgraph "Data Persistence"
        DYNAMO_P[DynamoDB Patient Table]
        DYNAMO_O[DynamoDB Observations]
        DYNAMO_A[DynamoDB Appointments]
    end
    
    subgraph "API Consumers"
        DASHBOARD[Clinical Dashboards]
        MOBILE[Mobile Applications]
        INTEGRATION[EHR Integrations]
    end

    %% Flow connections
    CLINICIAN --> VALIDATE
    DEVICES --> VALIDATE
    EXTERNAL --> VALIDATE
    
    VALIDATE --> TRANSFORM
    TRANSFORM --> ENRICH
    
    ENRICH --> PATIENT_R
    ENRICH --> OBS_R
    ENRICH --> APPT_R
    
    PATIENT_R --> PEDIATRIC
    OBS_R --> COMPLEXITY
    APPT_R --> ALERTS
    
    PATIENT_R --> DYNAMO_P
    OBS_R --> DYNAMO_O
    APPT_R --> DYNAMO_A
    
    DYNAMO_P --> DASHBOARD
    DYNAMO_O --> MOBILE
    DYNAMO_A --> INTEGRATION
    
    PEDIATRIC --> DASHBOARD
    COMPLEXITY --> MOBILE
    ALERTS --> INTEGRATION

    %% Styling
    classDef source fill:#1565C0,stroke:#0D47A1,stroke-width:2px,color:#FFFFFF
    classDef processing fill:#2E7D32,stroke:#1B5E20,stroke-width:2px,color:#FFFFFF
    classDef resource fill:#E65100,stroke:#D84315,stroke-width:2px,color:#FFFFFF
    classDef decision fill:#7B1FA2,stroke:#4A148C,stroke-width:2px,color:#FFFFFF
    classDef storage fill:#455A64,stroke:#263238,stroke-width:2px,color:#FFFFFF
    classDef consumer fill:#1976D2,stroke:#1565C0,stroke-width:2px,color:#FFFFFF

    class CLINICIAN,DEVICES,EXTERNAL source
    class VALIDATE,TRANSFORM,ENRICH processing
    class PATIENT_R,OBS_R,APPT_R resource
    class PEDIATRIC,COMPLEXITY,ALERTS decision
    class DYNAMO_P,DYNAMO_O,DYNAMO_A storage
    class DASHBOARD,MOBILE,INTEGRATION consumer

sequenceDiagram
    participant C as Clinician
    participant API as FHIR API
    participant CDS as Clinical Decision Support
    participant DB as DynamoDB
    participant ALERT as Alert System

    Note over C,ALERT: Pediatric Patient Visit Workflow

    C->>API: Create Patient (age 5, care complexity: moderate)
    API->>DB: Store Patient Resource
    API->>CDS: Evaluate pediatric rules
    CDS-->>API: Care recommendations
    API-->>C: Patient created + care guidance

    C->>API: Record vital signs (HR: 120 bpm)
    API->>CDS: Check pediatric reference ranges
    CDS->>CDS: Age 5: Normal range 80-130 bpm
    CDS-->>API: Normal interpretation
    API->>DB: Store Observation with interpretation
    API-->>C: Vital signs recorded (Normal)

    C->>API: Schedule follow-up appointment
    API->>CDS: Check care complexity requirements
    CDS->>CDS: Moderate complexity = 3 month follow-up
    CDS-->>API: Recommended timing
    API->>DB: Store Appointment
    API-->>C: Appointment scheduled

    Note over C,ALERT: Critical Value Scenario
    C->>API: Record high fever (39.5°C)
    API->>CDS: Evaluate critical threshold
    CDS->>ALERT: Trigger pediatric fever alert
    ALERT-->>C: Immediate clinical alert
    CDS-->>API: Critical interpretation
    API->>DB: Store with critical flag
    API-->>C: Critical value recorded + alert

Clinical Standards:

• HL7 FHIR R4: Complete implementation with Patient, Observation, and Appointment resources
• LOINC Codes: Standardized observation codes (e.g., 8867-4 for Heart Rate)
• SNOMED CT: Medical terminology for specialties and procedures
• US Core Profiles: Adherence to US healthcare implementation guides

Data Governance:

• HIPAA Ready: Security patterns for protected health information
• Data Minimization: Limited exposure of sensitive patient data
• Audit Trails: Comprehensive logging with correlation IDs for compliance
• Access Controls: Role-based security patterns (ready for implementation)

Interoperability:

• FHIR Bundle Support: Ready for bulk data operations
• RESTful CRUD: Standard healthcare data operations
• JSON Format: Modern data exchange format
• API Versioning: Backward-compatible API evolution

Quality & Safety:

• Clinical Decision Support: Automated pediatric vital signs interpretation
• Error Handling: Safe error responses protecting patient information
• Input Validation: Healthcare-specific data validation rules
• Monitoring: Real-time system health and performance metrics

Microservices Design:

• Service Separation: Patient, Observation, and Appointment services
• API Gateway Pattern: Centralized API management and security
• Event-Driven Architecture: Ready for clinical workflow automation
• Scalable Infrastructure: Auto-scaling based on clinical demand

Security & Privacy:

• Defense in Depth: Multiple security layers from network to application
• Encryption Ready: HTTPS/TLS configuration for data in transit
• Audit Logging: Healthcare-specific audit trail requirements
• Data Classification: Structured approach to sensitive health data

Clinical Workflow Support:

• Care Coordination: Emergency contact and care team management
• Appointment Lifecycle: Complete workflow from booking to completion
• Clinical Documentation: Structured data for clinical decision-making
• Population Health: Patient cohort management and analytics

This implementation demonstrates deep understanding of health informatics principles and provides a foundation for enterprise healthcare system development.

This demo specifically showcases capabilities relevant to modern pediatric healthcare organizations:

1. Pediatric Focus: Age-appropriate vital sign ranges and care complexity tracking
2. Scalable Design: Built for high-volume, multi-tenant healthcare environments
3. Modern Stack: Uses technologies mentioned in the job description (Node.js, TypeScript, AWS, Terraform)
4. Healthcare Standards: Demonstrates deep understanding of FHIR and healthcare interoperability
5. Production Ready: Includes monitoring, logging, security, and deployment automation
6. Team Collaboration: Clean code, comprehensive documentation, and maintainable architecture

This is a demonstration project, but it follows enterprise development practices:

1. Fork the repository
2. Create a feature branch
3. Make changes with tests
4. Submit a pull request
5. CI/CD pipeline will validate changes

MIT License - This is a demonstration project showcasing modern healthcare software development practices.

Note: This is a portfolio demonstration project showcasing enterprise-grade healthcare software development skills and FHIR implementation expertise for pediatric care platforms.