Overview
HANA’s Self-correcting Models represent an advanced framework for autonomous error detection, correction, and continuous improvement in clinical voice AI. Built on our service architecture, these models employ multi-layered validation, real-time feedback loops, and adaptive learning mechanisms to achieve high reliability and accuracy in production environments. The system combines constrained generation, reference-free evaluation, and continuous optimization to create conversations that not only detect errors but actively improve performance over time.Core Self-Correction Architecture
Multi-Layered Validation System
Immediate Validation (per-utterance):- Template compliance checking for response format and content
- Entity grounding verification against patient EHR data
- Confidence scoring based on model internal states
- Real-time safety filtering for clinically inappropriate content
- Semantic consistency analysis across conversation turns
- Cross-reference validation against known patient data and protocol constraints
- Context coherence checking for multi-turn clinical dialogues
- Domain-specific rule validation (e.g., medication names, ICD codes, assessment scoring)
- Comprehensive clinical accuracy verification using LLM-as-judge evaluation
- Full transcript analysis for protocol compliance
- Long-term context validation across patient’s conversation history
- Quality assurance against evaluation datasets and clinical standards
Candidate Verification Framework
Generation and Verification Pipeline:- Conversation engine generates candidate responses for each turn
- Reasoning engine evaluates and ranks candidates against protocol constraints, selecting the optimal response
- Verification is optimized for minimal latency impact on the conversation flow
- Self-calibration adjusts correction thresholds based on conversation context and historical accuracy
- Immediate corrections for detected errors during response generation (block and regenerate)
- Mid-conversation corrections through clarification and re-asking (soft redirect)
- Post-conversation corrections flagged for clinical team review
- Predictive corrections based on known error patterns in similar conversations
Continuous Learning Mechanisms
Feedback Loop Architecture:- Real-time correction logging for pattern identification across production conversations
- Error taxonomy classification for systematic improvement
- Performance regression detection and automatic rollback for model updates
- A/B testing framework for correction strategy optimization
- Error pattern mining from production conversations
- Correction effectiveness analysis across different clinical protocols
- Model weight adjustments based on correction success rates
- Dynamic threshold tuning for optimal precision-recall balance
Self-Correction Implementation
Implementation Architecture
Real-Time Correction Pipeline: Stream Processing:- Immediate error detection during response generation
- Context-aware correction maintaining natural conversation flow
- Memory-efficient correction processing per active session
- Concurrent correction across all active conversation sessions
- Multi-factor scoring combining confidence, context, and historical data
- Threshold-based correction triggering with adaptive boundaries
- Cost-benefit analysis for correction implementation (is re-asking worth the disruption?)
- Patient experience optimization minimizing correction-related conversation friction
Batch Correction Framework
Historical Data Processing:- Retroactive quality analysis for completed conversations
- Large-scale pattern identification across conversation corpus
- Data migration support during model updates
- Quality metric recalculation after protocol changes
- Parallel analysis processing across multiple compute nodes
- Checkpoint-based recovery for long-running analysis jobs
- Resource scheduling to minimize impact on live conversation services
- Progress tracking and reporting for operational visibility
Error Detection Mechanisms
Statistical Anomaly Detection: Confidence Score Analysis:- Low confidence detection using model uncertainty quantification
- Confidence calibration ensuring scores reflect actual accuracy
- Ensemble disagreement as indicator of potential errors
- Temporal consistency checking across related outputs
- Known error pattern matching using curated clinical error databases
- Linguistic anomaly detection for unnatural conversation patterns
- Factual inconsistency detection using patient data validation
- Reasoning chain verification for multi-step clinical logic
- Medication name validation against pharmaceutical databases
- Drug interaction checking when multiple medications discussed
- Medical terminology verification against authoritative sources (SNOMED CT, RxNorm)
- Clinical guideline compliance checking for protocol-defined recommendations
- PHQ-9, GAD-7, AUDIT-C scoring validation against administration rules
- Threshold-based alert verification for screening instrument results
- Response mapping validation (patient language → assessment score category)
Correction Strategies
Immediate Correction Approaches: Response-Level Correction:- Real-time response replacement during generation when safety or grounding check fails
- Alternative phrasing generation for unclear or ambiguous utterances
- Template fallback for responses that fail multiple validation checks
- Graceful acknowledgment when system cannot generate a valid response
- Soft redirect: naturally steer conversation back on track without calling attention to the correction
- Explicit clarification: directly address ambiguity or inconsistency with the patient
- Graceful deferral: hand off to human staff when system cannot resolve the issue
- Targeted error identification for specific clinical data extraction issues
- Comprehensive quality assessment for entire conversation transcripts
- Pattern identification across conversations for systematic protocol improvements
- Clinical team notification for conversations requiring human review
- Protocol updates based on recurring error patterns
- Template expansion incorporating correction insights
- Training data augmentation with corrected conversation examples
- Evaluation metric refinement based on correction effectiveness
Performance Monitoring
Quality Metrics
Correction Effectiveness:- Error detection rate (sensitivity) across different error types
- False positive rate (specificity) for correction triggers
- Correction accuracy measuring improvement quality
- Patient experience impact of corrections vs. uncorrected conversations
- Correction latency impact on conversation response time
- Throughput impact of correction processing on overall system
- Resource utilization for correction infrastructure
- Cost per correction for operational efficiency analysis
Operational Dashboards
Real-Time Monitoring:- Live error detection rates across all active conversation sessions
- Correction queue status and processing latency
- Quality score distributions for current conversations
- System health indicators for correction services
- Error trend analysis over time and across different clinical protocols
- Correction success patterns for strategy optimization
- Model performance evolution showing improvement trajectory
- Cost-benefit analysis of correction infrastructure investment
Quality Assurance Framework
Validation Protocols: Multi-Stage Verification:- Automated validation using evaluation models
- Human clinical review for flagged conversations
- Peer review processes for edge cases and complex corrections
- Patient feedback integration for real-world quality assessment
- Pre-deployment validation for model and protocol updates
- A/B testing protocols for new correction strategies
- Rollback procedures for correction strategy failures
- Performance regression testing ensuring corrections don’t degrade quality
- Complete correction history with timestamps and rationale
- Decision audit logs for correction trigger events
- Performance audit reports for regulatory compliance
- Patient consent tracking for conversation recording and analysis
- Healthcare compliance (HIPAA) for all correction operations involving PHI
- Privacy compliance (GDPR) for personal information handling
- AI safety compliance ensuring fair and unbiased corrections
- SOC 2 Type II audit coverage for correction infrastructure
Integration Guidelines
For Application Teams
Implementation Steps:- Enable correction APIs in agent configuration
- Set quality thresholds appropriate for clinical protocol requirements
- Implement feedback collection for clinician-reported issues
- Monitor correction impact on patient experience metrics
- Graceful correction handling maintaining natural conversation flow
- Patient transparency about AI nature without over-explaining corrections
- Performance monitoring for correction-related latency
- Fallback strategies for correction system unavailability
For Platform Teams
Infrastructure Management:- Deploy correction services across all environments (staging, production)
- Configure monitoring for correction system health
- Establish SLAs for correction response times
- Implement scaling policies for correction infrastructure
- Resource allocation for correction processing workloads
- Data pipeline management for correction training and evaluation
- Security protocols for correction system access (PHI handling)
- Disaster recovery for correction service failures