Implement Clinical Data Dashboard (KD-61)

docs/clinical-dashboard-architecture.md

@@ -0,0 +1,116 @@
+# Clinical Data Dashboard Architecture
+
+## System Overview and Purpose
+
+The Clinical Data Dashboard is designed to provide clinicians with comprehensive decision support by offering seamless access to both historical and real-time patient data. The system consolidates critical clinical information, enabling timely and informed medical interventions. By integrating varied data streams, it ensures that clinicians have an up-to-date view of patient status, facilitating improved clinical outcomes.
+
+This implementation fulfills requirement **KXREC6PEB45DQVN8N8T7JWY770HBFF8**.
+
+---
+
+## Component Architecture Diagram Description
+
+The dashboard architecture consists of the following major components:
+
+- **Backend API**: Serves as the central data source, aggregating data from various clinical repositories and systems.
+- **WebSocket Server**: Provides real-time data streaming to the UI components.
+- **Anomaly Detection Engine (Parent Component)**: Processes incoming clinical data to flag deviations and alerts, integrating tightly with the dashboard.
+- **UI Components**: Interactive dashboard modules that display historical trends, real-time vitals, alerts, and patient summaries.
+- **Authentication and Authorization Module**: Secures access ensuring only permitted users can access sensitive clinical data.
+
+The components are connected via secure, standardized APIs with a dedicated WebSocket channel facilitating real-time updates. Communication flows from backend systems through the API and real-time channels into the UI and interplay with the Anomaly Detection Engine. 
+
+---
+
+## Data Flow from Backend API to UI Components
+
+1. **Data Retrieval**: The Backend API queries databases and external services to fetch historical patient data.
+2. **Data Normalization**: Retrieved data is standardized for consistent rendering.
+3. **UI Consumption**: The UI components consume RESTful API responses to populate charts, tables, and summary views on load.
+4. **Real-Time Data Injection**: A persistent WebSocket connection streams live patient telemetry and alerts directly into reactive UI components for immediate updates.
+5. **Anomaly Data Integration**: The Anomaly Detection Engine processes both historical and live data streams, pushing flags or warnings back to UI components for display.
+
+---
+
+## Real-Time Update Mechanism
+
+The dashboard uses a **WebSocket-based real-time update mechanism**:
+
+- Upon UI initialization, the client establishes a secure WebSocket connection to the backend.
+- The backend pushes event-driven updates for vital signs, alerts, and system messages in real time.
+- To handle intermittent connectivity or server downtime, a fallback **polling strategy** periodically requests critical updates every 15 seconds.
+- WebSocket disconnections trigger immediate alerts in the UI to inform users of potential data latency.
+
+---
+
+## Security Architecture
+
+To mitigate risks of unauthorized access and ensure data integrity:
+
+- **Authentication**: Uses OAuth 2.0 with multi-factor authentication for all clinician access.
+- **Authorization**: Role-based access control (RBAC) restricts patient data to only authorized users based on clinical roles.
+- **Data Encryption**: All data in transit utilizes TLS 1.2+; sensitive data is encrypted at rest.
+- **Session Management**: Implement idle session timeouts and context-based access reviews.
+- **Audit Logging**: All access and data modification events are logged and monitored for anomaly detection.
+- **Vulnerability Management**: Regular penetration testing and security patching for backend and frontend layers.
+
+These strategies align with controls for risk items **KXREC0XP3JDJKEA9YRVHNVAMF70YJJ1**, **KXREC74BEX3AQ999FRA2V4DVJ6D17BK**, and **KXREC32DGQNY4Z88C9B64B8BVSKA5D7**.
+
+---
+
+## Performance Considerations and Optimization Strategies
+
+To ensure responsive user experience and system scalability:
+
+- **Efficient Data Queries**: Use indexed queries and caching layers to reduce API response times.
+- **Data Pagination and Lazy Loading**: Limit the volume of data transferred and rendered at once to avoid UI lag.
+- **WebSocket Compression**: Enable message compression to reduce bandwidth usage.
+- **Client-Side Throttling**: Buffer and consolidate UI updates during high-frequency events.
+- **Load Balancing**: Distribute backend and WebSocket server load to prevent bottlenecks.
+- **Monitoring and Alerts**: Continuous performance monitoring with automated alerting to identify and resolve slowdowns proactively.
+
+---
+
+## Integration with Anomaly Detection Engine
+
+The Clinical Data Dashboard functions as a child component under the Anomaly Detection Engine framework:
+
+- Receives processed anomaly alerts via well-defined API endpoints and WebSocket channels.
+- Visualizes anomaly indicators alongside clinical data for rapid clinician awareness.
+- Contributes filtered and tagged clinical data feeds to the engine for enhanced processing accuracy.
+- Synchronizes state with anomaly detection lifecycle events to maintain contextual integrity.
+
+This integration ensures a cohesive and efficient clinical decision support ecosystem.
+
+---
+
+## Compliance with Medical Device Software Standards
+
+The system conforms to relevant medical device software standards, including:
+
+- **IEC 62304**: Adheres to software lifecycle processes for medical device software.
+- **ISO 14971**: Implements risk management throughout development and deployment.
+- **HIPAA**: Ensures patient data confidentiality and compliance with health information privacy standards.
+- **FDA Guidance**: Aligns with FDA software as a medical device (SaMD) requirements.
+
+All development and operational practices incorporate traceability matrices, documentation, and validation per these standards.
+
+---
+
+## Risk Mitigation Strategies
+
+To address potential system downtime and data access risks:
+
+- **Redundancy**: Deploy backend APIs and WebSocket servers in a high-availability configuration with automatic failover.
+- **Data Backup and Recovery**: Implement frequent backups with tested recovery procedures.
+- **Graceful Degradation**: UI components provide cached historical data and informative messages when real-time feeds are unavailable.
+- **Error Handling**: Robust error detection and retry mechanisms on client and server sides.
+- **Incident Response**: Defined operational procedures for outage detection, user notification, and swift restoration.
+- **Access Control Monitoring**: Proactive audits to minimize unauthorized data exposure.
+
+These measures align with controls for risk items **KXREC0XP3JDJKEA9YRVHNVAMF70YJJ1**, **KXREC74BEX3AQ999FRA2V4DVJ6D17BK**, and **KXREC32DGQNY4Z88C9B64B8BVSKA5D7**.
+
+---
+
+*Document Version: 1.0*  
+*Last Updated: 2024-06*