Case Study: Healthcare Agent Evaluation
This document maps this repository to Applied AI / LLM Agent Evaluation roles, specifically those focused on healthcare applications.
Role Alignment
This repo includes patterns relevant to roles where you:
- Design & execute large-scale eval plans for LLM agents
- Create high-fidelity unit and E2E evals with expert-determined ground truth
- Run experiments across models, prompts, and tools
- Maintain post-deployment sampling and governance for production agents
Repository Mapping
Multi-Agent Architecture
Location: ai-monitor/AGENT_ARCHITECTURE.md and ai-monitor/INTELLIGENCE_SYSTEM.md
Relevance: Demonstrates multi-agent system design with specialized agents:
- Priority Scoring Agent
- Change Detection Agent
- Summarization Agent
- Trend Analysis Agent
Healthcare Application: Similar architecture could be used for:
- Clinical decision support agents
- Patient data extraction agents
- Medication recommendation agents
- Risk assessment agents
Automated Monitoring & Sampling
Location: ai-monitor/
Relevance:
- Weekly automated data collection and analysis
- Multi-source data aggregation (GitHub, Hugging Face, arXiv, etc.)
- Deduplication and quality filtering
- Historical tracking and trend analysis
Healthcare Application:
- Continuous monitoring of clinical agent performance
- Sampling production requests for expert review
- Tracking quality metrics over time
- Detecting drift in agent behavior
RAG + Data QA
Location: excel-csv-chat-RAG/ and ai-30day-sprint/p1-csv-chat/
Relevance:
- RAG pipeline for structured data (CSV/Excel)
- Natural language Q&A over tabular data
- Data parsing and chunking strategies
Healthcare Application:
- Q&A over patient records
- Clinical guideline retrieval
- Medical knowledge base search
- Structured data extraction from unstructured notes
Evaluation Framework
Location: evals/
Relevance:
- A/B testing framework for models and prompts
- Correctness, safety, and reliability metrics
- Configuration-driven evaluation pipelines
- Post-deployment monitoring patterns
Healthcare Application:
- Comparing different clinical decision support models
- Evaluating safety of medication recommendations
- Measuring consistency of patient data extraction
- Monitoring latency for time-critical workflows
Evaluation Patterns
1. High-Throughput Evaluation
Example: evals/run_ab_test_models_prompts.py
Run thousands of test cases across multiple models and prompts to identify regressions and improvements.
Healthcare Use Case:
- Test clinical decision support across 1000+ patient scenarios
- Compare GPT-4 vs Claude vs open-source models
- Evaluate different prompt engineering strategies
2. Expert-Determined Ground Truth
Example: evals/canvas_style_clinical_eval.md
For critical applications, use domain experts (clinicians) to establish ground truth outcomes.
Healthcare Use Case:
- Expert review of agent recommendations for medication dosing
- Clinical accuracy validation by board-certified physicians
- Safety review of high-risk recommendations
3. Post-Deployment Monitoring
Example: ai-monitor/ weekly automation
Continuous sampling and analysis of production agent outputs.
Healthcare Use Case:
- Sample 2-5% of clinical agent decisions for expert review
- Track correctness, safety, and latency metrics
- Alert on significant changes in performance
- Maintain audit logs for regulatory compliance
4. Safety Evaluation
Example: evals/metrics/safety_flags.py
Detect hallucinations, contradictions, and assess risk levels.
Healthcare Use Case:
- Flag potentially harmful medication recommendations
- Detect when agents generate unsupported clinical claims
- Assess risk level of agent outputs (low/medium/high/critical)
Implementation Examples
Unit Tests
Fast, deterministic checks on individual components:
# Example: Test tool calling accuracy
def test_clinical_tool_calling():
agent = ClinicalAgent()
result = agent.extract_patient_data("Patient note text...")
assert result['medications'] == expected_medications
E2E Tests
Full workflow validation with realistic scenarios:
# Example: Test complete clinical workflow
def test_patient_workflow():
# Extract patient data
patient_data = agent.extract_from_note(note_text)
# Generate recommendations
recommendations = agent.recommend_treatment(patient_data)
# Validate against clinical guidelines
assert validate_against_guidelines(recommendations)
A/B Testing
Compare different configurations:
# Example: Compare models for clinical accuracy
ab_test = ABTestRunner(config='clinical_eval_config.yaml')
results = ab_test.compare_models(
models=['gpt-4', 'claude-3-opus', 'medllm-7b'],
test_cases=clinical_scenarios,
metrics=['correctness', 'safety', 'latency']
)
Key Metrics
Correctness
- Exact match: Output matches expected ground truth exactly
- Semantic similarity: Meaning similarity using embeddings
- Clinical accuracy: Expert-reviewed correctness for medical decisions
Safety
- Hallucination detection: Identify unsupported information
- Contradiction detection: Flag outputs contradicting established facts
- Risk scoring: Assess potential harm from recommendations
Reliability
- Consistency: Same input produces similar outputs across runs
- Latency: Response time meets clinical workflow requirements
- Uptime: System availability for critical workflows
Next Steps
- Expand evaluation suite: Add more healthcare-specific test cases
- Integrate expert review: Set up workflow for clinician labeling
- Deploy monitoring: Implement production sampling and alerting
- Regulatory compliance: Add audit logging and governance features