CareFrame Technical Overview

System Architecture

CareFrame is a comprehensive learning health system designed to address the fragmentation in clinical research workflows. Built with a modular architecture, it integrates study planning, literature analysis, data collection, statistical testing, and evidence sharing into a unified platform.

Technology Stack

• Frontend: PyQt6 for a responsive, desktop-native user interface
• Backend: Python-based processing with domain-specific modules
• Data Storage: CouchDB for document storage and flexible schema
• Analytics: Integrated statistical libraries (NumPy, Pandas, Matplotlib)
• Networking: WebSockets for real-time communication between instances
• Evidence Storage: Custom blockchain implementation with Proof of Authority

Core System Modules

1. Research Planning - Visual workflow for hypothesis formulation
2. Literature Integration - Semantic search and evidence extraction
3. Data Processing - Collection, cleaning, and transformation
4. Statistical Analysis - Testing with automatic assumption verification
5. Evidence Blockchain - Immutable storage with validation controls
6. Network Exchange - Pub/sub communication between instances

Core Research Workflow

CareFrame implements a complete research workflow aligned with established scientific methods:

1. Hypothesis Formulation

The Research Planning module provides a visual canvas for defining research objectives and corresponding hypotheses. Researchers can:

• Create hierarchical research objectives
• Define testable hypotheses linked to objectives
• Generate hypothesis candidates based on research questions
• Visualize relationships between research elements

Technical Implementation: Graphical node-based system using PyQt6 with customizable node types, user-defined relationships, and hierarchical organization.

2. Literature Evidence Collection

The Literature Search module enables systematic literature review with:

• Advanced search syntax for PubMed and other databases
• Semantic paper ranking by relevance to hypotheses
• Claim extraction from publications
• Evidence mapping to specific hypotheses

Technical Implementation: Integrated API connections to literature sources, local embedding-based ranking, and context-aware claim extraction.

3. Data Management

The Data Processing modules support:

• Connection to various data sources (CSV, SQL, APIs)
• Data cleaning and transformation workflows
• Schema definition and validation
• Data reshaping and joining operations

Technical Implementation: Pandas-based data processing with custom UI wrappers, persistent transformation pipelines, and schema validation.

4. Statistical Testing

The Analysis modules provide:

• Statistical test selection based on data characteristics
• Automatic assumption checking for validity
• Interactive visualization of results
• Significance interpretation and reporting

Technical Implementation: Integration with statistical packages, decision trees for test selection, and automated checks for statistical assumptions.

5. Evidence Validation

The Evidence Blockchain secures research findings through:

• Immutable storage of hypotheses and evidence
• Team-based validation using Proof of Authority
• Transparent validation requirements
• Evidence linking across research phases

Technical Implementation: Custom blockchain with configurable PoA consensus, validator management, and evidence transaction types.

6. Knowledge Distribution

The Network module enables multi-center collaboration through:

• Pub/sub architecture for selective sharing
• Topic-based message routing
• Realtime updates across instances
• Evidence synchronization between teams

Technical Implementation: WebSocket-based messaging system with topic subscription, message filtering, and binary data support.

Data Flow

The data flow through CareFrame follows a logical progression:

1. Research questions are formalized as objectives and hypotheses
2. Literature evidence is collected and linked to hypotheses
3. Raw data is imported, processed, and prepared for analysis
4. Statistical tests are run with appropriate assumption checks
5. Results are interpreted and linked to hypotheses
6. Evidence is stored immutably in the blockchain
7. Validated knowledge is shared across the network

This integrated workflow eliminates traditional silos between research phases and ensures consistency throughout the research lifecycle.

Security & Validation

CareFrame employs several mechanisms to ensure data integrity:

• User Access Controls: Role-based access to system features
• Validator Teams: Designated experts who validate evidence
• Proof of Authority: Consensus mechanism requiring trusted validators
• Immutable Storage: Blockchain-based evidence records
• Digital Signatures: Cryptographic verification of evidence source
• Audit Trails: Complete history of data transformations

Technical Extensibility

CareFrame is designed for extensibility in several dimensions:

• Module System: Each major feature is isolated in its own module
• Plugin Architecture: Custom statistical tests can be added
• Data Connectors: Support for new data sources can be implemented
• Network Protocol: Extensible messaging format for new data types
• UI Theming: Customizable appearance through XML-based themes

Future Enhancements: Agentic System

While not fully implemented, CareFrame includes foundational elements for an agentic workflow automation system:

• Agent Interface: Already integrated UI for LLM-based assistance
• Task Timeline: Framework for tracking multi-stage research tasks
• Model Integration: Connections to local and remote LLM services
• Code Generation: Infrastructure for creating statistical code

These components will enable future automation of routine research tasks such as hypothesis generation, literature review, statistical test selection, and assumption checking, allowing researchers to focus on scientific interpretation rather than technical implementation.

System Requirements

• Operating System: Linux, macOS, or Windows
• Memory: 8GB minimum, 16GB recommended
• Storage: 1GB for application, plus space for research data
• Optional Services: CouchDB for data storage, Ollama for local LLM processing

Integration Points

CareFrame can integrate with external systems through:

• Data Import/Export: Standard formats (CSV, JSON, SQL)
• API Connections: REST interfaces for external data sources
• Network Protocol: WebSocket connections to other instances
• Literature APIs: PubMed, Semantic Scholar, and other services

Deployment Models

CareFrame supports several deployment models:

• Single Researcher: Standalone installation for individual research
• Research Team: Networked instances with shared validator pool
• Multi-center Collaboration: Federated deployment with centralized validation
• Institutional: Enterprise deployment with role-based access control

Conclusion

CareFrame provides a comprehensive technical foundation for implementing learning health systems that bridge the gap between research and practice. By integrating all phases of the research lifecycle, it enables a more efficient, transparent, and collaborative approach to clinical evidence generation and knowledge sharing.