Vitals
AI-powered clinical decision support — analyze patient symptoms, medical history, and lab results to assist with diagnoses.
Project presentation
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Vitals · Agent Mesh
Patient Intake
Patient data collected via EHR integration, forms, or voice.
Technical Design
HIPAA-compliant clinical decision support system with multi-modal patient data analysis. AI agents specialize in different diagnostic aspects while maintaining strict data governance.
System Components
Clinical NLP Engine
Medical entity extraction from clinical notes
Diagnosis Agent
Differential diagnosis generation
Pharmacy Checker
Drug interaction screening
Research Retriever
Medical literature RAG system
Documentation Generator
Clinical summary and coding
Data Flow
Technology Choice
Azure AI Foundry + LangChain
Azure AI Foundry provides HIPAA-compliant LLM hosting. LangChain's RAG patterns enable accurate medical literature retrieval with proper citations.
Alternatives Considered
Google Cloud Healthcare API + LlamaIndex, AWS HealthLake + Custom ML
Core Frameworks
Azure AI Foundry
HIPAA-compliant LLM hosting
LangChain
Clinical workflow orchestration and RAG
Azure Health Data Services
FHIR data integration
FastAPI
Clinical decision support API
Implementation Plan
Clinical NLP
- Medical entity extraction
- Clinical coding
- Abbreviation handling
Diagnostic Intelligence
- Differential ranking
- Risk scoring
- Red flag detection
Safety Systems
- Drug interaction checking
- Allergy screening
- Audit trails
Clinical Integration
- EHR integration
- Clinical workflows
- Compliance validation
Key Milestones
Risk Mitigation
Key Features
Symptom Analysis
NLP-powered symptom extraction from clinical notes and patient-reported data with differential diagnosis ranking.
Lab Result Interpretation
Automated analysis of lab results against reference ranges with trend detection and anomaly flagging.
Drug Interaction Check
Real-time screening for drug-drug and drug-condition interactions across patient medication lists.
Treatment Recommendations
Evidence-based treatment suggestions using medical knowledge graphs and clinical guidelines.
Medical Literature RAG
RAG-powered retrieval from medical journals, clinical trials, and treatment protocols.
Patient Risk Scoring
ML-powered risk stratification for readmission, complications, and chronic disease progression.
How It Works
Patient Intake
Patient data collected via EHR integration, forms, or voice.
Clinical NLP
Extract medical entities and clinical concepts from patient data.
Risk Assessment
Calculate patient risk scores and flag high-priority conditions.
Differential Diagnosis
Generate ranked differential diagnoses with supporting evidence.
Medication Review
Screen current and proposed medications for safety.
Literature Retrieval
Fetch relevant clinical evidence and guidelines for complex cases.
Clinical Summary
Generate comprehensive clinical summary with recommendations.
Continuous Learning
Learn from outcomes to improve diagnostic accuracy over time.
Multi-Agent Architecture
Clinical NLP Agent
Extracts medical entities, symptoms, and clinical concepts from unstructured notes.
- Named entity recognition
- Symptom extraction
- Clinical coding
- Abbreviation expansion
- Multi-language support
Diagnosis Agent
Generates differential diagnoses ranked by probability and clinical relevance.
- Differential ranking
- Probability scoring
- Evidence weighting
- guideline matching
- Red flag detection
Pharmacy Agent
Screens medications for interactions, contraindications, and dosing adjustments.
- Drug interaction check
- Dose optimization
- Allergy screening
- Renal adjustment
- Formulary matching
Research Agent
Retrieves relevant medical literature and clinical evidence for complex cases.
- Literature search
- Trial matching
- Evidence synthesis
- Citation management
- Update tracking
Documentation Agent
Generates clinical summaries, referral letters, and billing codes.
- SOAP note generation
- Referral drafting
- ICD-10 coding
- Discharge summaries
- Audit trails
Use Cases
Primary Care Support
Assist general practitioners with differential diagnosis and treatment planning.
Emergency Triage
Accelerate emergency department triage with rapid risk assessment.
Chronic Disease Management
Monitor and manage patients with chronic conditions using AI-powered alerts.
Specialist Referrals
Generate evidence-backed referral recommendations with clinical summaries.
Clinical Research
Identify eligible patients for clinical trials based on inclusion criteria.
Quality Assurance
Flag potential diagnostic errors and recommend second opinions.