Research Areas
Building evidence through an interactive community testbed where clinicians submit examples and evaluate AI vs deterministic vs hybrid approaches. We identify strengths and weaknesses through real community input, then release the data and findings openly.
How Our Community Research Platform Works
Rather than technologists building technology then asking clinicians to work with it, we're building a collaborative environment where NHS professionals drive the development process. Clinicians are integrated early to test, compare, and evaluate different approaches using real examples— gaining input from stakeholders across the NHS community to build technology that actually works for healthcare.
Submit Examples
Clinicians upload anonymized clinical text examples through our secure members platform.
Compare Methods
Each example is processed by AI models, deterministic rules, and hybrid approaches in parallel.
Evaluate Results
Community members review outputs, identify strengths/weaknesses, and rate effectiveness.
Refine Approaches
Based on feedback, we adjust methods and test variations to improve performance.
Release Data
Aggregated findings, performance metrics, and evidence are published openly.
Share Knowledge
Research documents, blogs, videos, and curated content based on real evidence.
Current Research Areas
Our research focuses on building clinically grounded AI tools through systematic investigation of real-world NHS challenges. These are the areas where we're currently developing evidence-based solutions.
Clinical NLP Pipelines
Converting free-text into FHIR-aligned structured dataResearch Focus
- FHIR Mapping: Converting clinical notes to structured FHIR resources
- Entity Recognition: Identifying medications, conditions, procedures
- Temporal Extraction: Timeline and sequence analysis from narrative text
- Quality Validation: Ensuring accuracy of extracted structured data
- Integration Patterns: Seamless NHS system integration approaches
Current Challenges
- Clinical Terminology: Handling abbreviations and local NHS variations
- Context Ambiguity: Interpreting negations and uncertainty in clinical text
- Data Quality: Managing inconsistent documentation practices
- Performance Scale: Processing large volumes of clinical notes efficiently
- Validation Complexity: Ensuring clinical accuracy across specialties
Retrieval-Augmented Generation (RAG)
Clinician-facing decision supportResearch Focus
- Clinical Knowledge Base: Integrating NICE guidelines and evidence
- Contextual Retrieval: Finding relevant information for specific cases
- Safety Integration: Ensuring recommendations follow clinical protocols
- Explainable Results: Providing clear reasoning for suggestions
- Workflow Integration: Seamless embedding in clinical decision processes
Current Challenges
- Information Currency: Keeping knowledge base updated with latest evidence
- Relevance Ranking: Prioritizing most applicable information for context
- Bias Mitigation: Preventing systematic biases in recommendations
- Clinical Validation: Ensuring suggestions align with best practices
- User Trust: Building confidence in AI-assisted decision making
Secure, Explainable AI Tooling
Clinical audit and workflow enhancementResearch Focus
- Audit Trail Systems: Comprehensive logging of AI decision processes
- Explainability Frameworks: Clear reasoning for clinical recommendations
- Security Architecture: NHS-grade data protection and access controls
- Workflow Analysis: Identifying optimization opportunities in clinical processes
- Compliance Monitoring: Ensuring adherence to clinical protocols
Current Challenges
- Performance Impact: Balancing explainability with system speed
- Complexity Management: Making explanations accessible to clinicians
- Security Overhead: Maintaining performance with enhanced security
- Change Management: Integrating new tools into existing workflows
- Regulatory Alignment: Meeting evolving NHS and MHRA requirements
NHS-Aligned DevOps & Cloud Infrastructure
Blueprints with governance guardrailsResearch Focus
- Governance Templates: Pre-configured compliance and security frameworks
- Deployment Patterns: Standardized architectures for NHS environments
- Monitoring Integration: Built-in observability and audit capabilities
- Scalability Design: Handling variable NHS trust workloads
- Cost Optimization: Efficient resource utilization for public sector
Current Challenges
- Trust Variations: Accommodating different NHS trust requirements
- Legacy Integration: Working with existing NHS technical infrastructure
- Skills Gap: Limited DevOps expertise in healthcare settings
- Regulatory Complexity: Navigating multiple compliance frameworks
- Change Resistance: Managing organizational transformation challenges
Graph-Based NHS Data Model
FHIR compliant data relationshipsResearch Focus
- FHIR Graph Mapping: Representing FHIR resources as connected data
- Relationship Modeling: Patient, provider, and care pathway connections
- Query Optimization: Efficient traversal of complex healthcare data
- Interoperability: Cross-system data integration and exchange
- Clinical Insights: Pattern recognition across patient populations
Current Challenges
- Data Standardization: Harmonizing diverse NHS data formats
- Performance Scaling: Managing large graph databases efficiently
- Privacy Complexity: Maintaining patient privacy in connected data
- Integration Burden: Connecting with existing NHS systems
- Maintenance Overhead: Keeping graph models accurate and current
Lightweight Accelerator Modules
Data interoperability, scheduling, and protocol executionResearch Focus
- Data Interoperability: Seamless exchange between NHS systems
- Scheduling Optimization: Efficient resource and appointment management
- Protocol Execution: Automated clinical pathway implementation
- Microservice Architecture: Modular, scalable component design
- Performance Tuning: Lightweight, high-speed processing modules
Current Challenges
- Integration Complexity: Connecting diverse NHS technical environments
- Protocol Variability: Accommodating different clinical pathways
- Resource Constraints: Working within NHS IT infrastructure limits
- Validation Requirements: Ensuring clinical safety in automated processes
- Adoption Barriers: Overcoming organizational and technical inertia
Clinician-Technologist Collaboration Framework
Healthcare AI requires deep partnership between clinical experts and technology developers. Our research explores collaborative methodologies that ensure clinical relevance, safety, and practical implementation in NHS environments.
Clinical Domain Expertise
Practicing clinicians bring essential understanding of workflow realities, patient safety requirements, and clinical decision-making processes.
- Real-world workflow analysis
- Clinical safety requirements
- User experience validation
- Outcome measure definition
Technical Implementation
Technology specialists provide computational expertise, system architecture knowledge, and implementation capabilities for scalable NHS solutions.
- System architecture design
- Performance optimization
- Security implementation
- Integration frameworks
Collaborative Development
Joint development processes ensure solutions meet both clinical needs and technical standards while maintaining evidence-based validation throughout.
- Co-design methodologies
- Iterative validation cycles
- Cross-disciplinary review
- Shared accountability frameworks
Personal Information Removal: Methodologies & Testing
Protecting patient privacy while maintaining clinical utility is fundamental to NHS AI systems. Our research systematically evaluates different approaches to PII removal, identifying optimal methodologies for various clinical contexts.
De-identification Approaches
Primary MethodsRule-Based De-identification
Pattern matching and regex-based removal of known PII formats.
- Fast processing speed
- Predictable behavior
- Limited adaptability
- Misses novel patterns
Machine Learning De-identification
NER models trained specifically for healthcare PII recognition.
- Adapts to new patterns
- Context-aware removal
- Requires training data
- Black box decisions
Hybrid Approaches
Combining rule-based and ML methods for comprehensive coverage.
- Highest accuracy
- Comprehensive coverage
- Increased complexity
- Higher computational cost
Privacy-Preserving Techniques
Advanced MethodsDifferential Privacy
Mathematical framework adding controlled noise to protect individual privacy.
- Formal privacy guarantees
- Quantifiable protection
- Reduces data utility
- Complex parameter tuning
Synthetic Data Generation
Creating artificial datasets that preserve statistical properties without real PII.
- No real patient data exposed
- Maintains statistical patterns
- Quality varies by domain
- Validation complexity
Federated Learning
Training models without centralizing sensitive data across NHS trusts.
- Data stays local
- Multi-site collaboration
- Communication overhead
- Heterogeneity challenges
PII Removal Testing & Validation Framework
Our systematic testing approach evaluates both privacy protection and clinical utility preservation across different NHS use cases and data types.
Privacy Assessment
- Re-identification risk analysis
- Linkage attack testing
- Information leakage measurement
- Compliance validation (GDPR, Data Protection Act)
Utility Preservation
- Clinical outcome prediction accuracy
- Statistical analysis validity
- Machine learning model performance
- Clinical workflow integration
Operational Validation
- Processing speed benchmarks
- Scalability testing
- Error rate monitoring
- Clinician acceptance evaluation
Automated Pre-Clinical Testing: Research & Development
Developing automated testing frameworks to catch obvious clinical errors before deployment, ensure regulatory compliance, and validate that code follows established medical protocols and safety requirements.
Clinical Error Detection
Automated systems to identify obvious clinical errors before they reach patient-facing environments, reducing risk and improving safety outcomes.
Research Focus Areas:
- Medical Logic Validation: Detecting impossible or dangerous clinical combinations
- Dosage Range Checking: Automatic validation against pediatric/adult safety limits
- Contraindication Detection: Identifying drug interactions and medical conflicts
- Temporal Logic Errors: Catching timeline inconsistencies in treatment plans
Implementation Challenges:
- False positive rates in complex clinical scenarios
- Integration with existing NHS clinical systems
- Performance impact on real-time applications
- Maintaining accuracy across different medical specialties
Regulatory Compliance Validation
Automated verification that code implementations follow NHS requirements, MHRA regulations, and established clinical protocols.
Compliance Testing Areas:
- MHRA Software Regulations: Automated checks for medical device software compliance
- NHS Digital Standards: Validation against DCB and technical standards
- NICE Guidelines: Ensuring recommendations align with approved pathways
- Data Protection: GDPR and NHS data handling requirement verification
Regulatory Challenges:
- Keeping pace with evolving regulatory landscape
- Interpreting complex guideline requirements programmatically
- Balancing automation with clinical judgment requirements
- Managing different regional NHS trust variations
Testing Methodologies
Validation Approaches:
- Rule-based clinical logic validation
- Machine learning anomaly detection
- Formal verification methods
- Hybrid human-AI review processes
Effectiveness Metrics:
Integration Patterns
Implementation Strategies:
- CI/CD pipeline integration
- Real-time API validation layers
- Pre-deployment safety gates
- Continuous monitoring systems
Integration Benefits:
- Early error detection in development
- Reduced manual testing overhead
- Consistent application of safety rules
- Audit trail for regulatory compliance
Research Validation
Study Design:
- Multi-site NHS trust testing
- Retrospective error analysis
- Clinician feedback integration
- Real-world performance monitoring
Current Research Status:
- Proof of concept development
- NHS partnership discussions
- Ethics committee submissions
- Preliminary testing protocols
Research Content & Knowledge Sharing
Our research findings are published openly to advance the field of healthcare AI. Members gain early access to research content, testing methodologies, and validation frameworks.
Research Publications
Peer-reviewed papers on model comparisons, PII removal techniques, pre-clinical testing, and collaborative development methodologies.
- Comparative analysis reports
- Methodology validation studies
- Clinical implementation guides
- Pre-clinical testing frameworks
Testing Protocols
Standardized testing methodologies for evaluating AI models, PII removal techniques, and automated pre-clinical validation systems.
- Validation test suites
- Performance benchmarks
- Safety assessment protocols
- Regulatory compliance checklists
Learning Resources
Educational materials for NHS teams implementing AI solutions, covering technical, clinical, governance, and safety testing perspectives.
- Implementation workshops
- Technical training modules
- Governance guidance
- Safety testing case studies
Contribute to NHS AI Research
Join our research community to help identify optimal approaches for clinical AI implementation. Share your insights, access our findings, and contribute to evidence-based NHS transformation.
All research findings are published openly. We believe transparency and peer review are essential for building safe, effective healthcare AI.