AI Integration Documentation

This section covers all AI-powered features and integrations in the LyfeAI Provider system.

Overview

LyfeAI Provider integrates advanced AI capabilities throughout the healthcare workflow, from document processing to clinical decision support. The system uses OpenAI's GPT-4 as the primary AI engine with fallback mechanisms for reliability.

AI Features

Document Processing

• Document Extraction - Extract structured data from medical documents
• OCR Integration - Process scanned documents and images
• Voice Transcription - Convert voice input to text

Clinical Intelligence

• Clinical Decision Support - AI-powered treatment recommendations
• Risk Assessment - Predictive analytics for patient risks
• Drug Interactions - Medication safety checks

Medical Imaging

• Image Analysis - AI-powered medical image interpretation
• Radiology Reports - Automated report generation
• Progression Tracking - Monitor changes over time

Administrative AI

• Smart Scheduling - Optimize appointment scheduling
• Prior Authorization - Automate insurance approvals
• Quality Measures - Track and improve care quality

Patient Engagement

• AI Chat Assistant - Interactive medical assistant
• Patient Onboarding - Intelligent form completion
• Health Analysis - Comprehensive health assessments

Architecture

AI Service (lib/ai-service.ts)

The core AI service provides:

class AIService {
  // Document processing
  extractPatientData(document: string): ExtractedPatientData
  
  // Clinical intelligence
  generateClinicalInsights(patient: Patient): ClinicalInsights
  analyzeMedications(medications: Medication[]): DrugInteractions
  
  // Medical imaging
  analyzeImage(imageData: string): ImageAnalysis
  
  // Natural language
  generateResponse(prompt: string, context: any): string
}

Integration Pattern

// Server action example
export async function processDocumentWithAI(documentText: string) {
  try {
    // Call AI service
    const extractedData = await aiService.extractPatientData(documentText);
    
    // Validate results
    const validated = validateExtractedData(extractedData);
    
    // Store insights
    await storeAIInsights(validated);
    
    return { success: true, data: validated };
  } catch (error) {
    // Fallback handling
    return handleAIError(error);
  }
}

Configuration

Environment Variables

# Required
OPENAI_API_KEY=your_openai_api_key

# Optional
OPENAI_MODEL=gpt-4-turbo-preview
OPENAI_MAX_TOKENS=4000
OPENAI_TEMPERATURE=0.3
AI_FALLBACK_ENABLED=true

Model Configuration

const modelConfig = {
  documentExtraction: {
    model: "gpt-4-turbo-preview",
    temperature: 0.1,  // Low for accuracy
    maxTokens: 4000
  },
  clinicalInsights: {
    model: "gpt-4",
    temperature: 0.3,  // Balanced
    maxTokens: 2000
  },
  conversation: {
    model: "gpt-3.5-turbo",
    temperature: 0.7,  // Higher for natural conversation
    maxTokens: 1000
  }
};

Prompt Engineering

Best Practices

1. Structured Prompts

const prompt = `
You are a medical data extraction specialist.

Task: Extract patient information from the following document.

Requirements:
- Extract demographics (name, DOB, gender)
- Identify medical conditions with ICD-10 codes
- List current medications with dosages
- Note any allergies

Output format: JSON matching the ExtractedPatientData schema.

Document:
${documentText}
`;

2. Context Injection

const contextualPrompt = `
Patient Context:
- Age: ${patient.age}
- Conditions: ${patient.conditions.join(', ')}
- Medications: ${patient.medications.join(', ')}

Question: ${userQuestion}

Provide a medically accurate response considering the patient's context.
`;

3. Output Validation

const schema = z.object({
  demographics: z.object({
    firstName: z.string(),
    lastName: z.string(),
    dateOfBirth: z.string()
  }),
  confidence: z.number().min(0).max(1)
});

const validated = schema.parse(aiResponse);

Error Handling

Fallback Strategies

1. Service Unavailable

if (!aiService.isAvailable()) {
  return simulatedAIResponse(input);
}

2. Rate Limiting

try {
  return await aiService.process(input);
} catch (error) {
  if (error.code === 'rate_limit_exceeded') {
    await delay(error.retryAfter);
    return await aiService.process(input);
  }
  throw error;
}

3. Invalid Responses

const response = await aiService.generate(prompt);
if (!isValidResponse(response)) {
  logger.warn('Invalid AI response', { response });
  return fallbackResponse();
}

Security & Compliance

PHI Protection

1. Data Anonymization

const anonymized = anonymizePatientData(patientData);
const insights = await aiService.analyze(anonymized);

2. Audit Logging

await auditLog.record({
  action: 'ai_analysis',
  userId: currentUser.id,
  patientId: patient.id,
  aiModel: 'gpt-4',
  timestamp: new Date()
});

3. Consent Management

if (!patient.aiConsentGiven) {
  throw new Error('Patient has not consented to AI analysis');
}

HIPAA Compliance

• No PHI in prompts unless necessary
• Encrypted transmission to AI services
• Audit trails for all AI interactions
• Data retention policies
• Regular security assessments

Performance Optimization

Caching

const cacheKey = `ai_insights_${patient.id}_${hash(data)}`;
const cached = await cache.get(cacheKey);
if (cached) return cached;

const result = await aiService.generateInsights(data);
await cache.set(cacheKey, result, { ttl: 3600 });
return result;

Batch Processing

const batchResults = await aiService.batchProcess(
  documents.map(doc => ({
    id: doc.id,
    prompt: createPrompt(doc)
  }))
);

Streaming Responses

const stream = await aiService.streamResponse(prompt);
for await (const chunk of stream) {
  yield chunk;
}

Monitoring & Analytics

Metrics to Track

• Response times
• Error rates
• Token usage
• Accuracy scores
• User satisfaction

Dashboard Integration

interface AIMetrics {
  totalRequests: number;
  averageLatency: number;
  errorRate: number;
  tokenUsage: {
    daily: number;
    monthly: number;
  };
  accuracyScore: number;
}

Testing

Unit Tests

describe('AI Service', () => {
  it('extracts patient data correctly', async () => {
    const mockResponse = createMockAIResponse();
    jest.spyOn(aiService, 'extract').mockResolvedValue(mockResponse);
    
    const result = await processDocument(testDocument);
    expect(result.demographics.firstName).toBe('John');
  });
});

Integration Tests

it('handles AI service failures gracefully', async () => {
  mockAIServiceDown();
  
  const result = await processDocumentWithAI(document);
  expect(result.success).toBe(true);
  expect(result.usedFallback).toBe(true);
});

Future Enhancements

Planned Features

1. Multi-modal AI

   • Combined text and image analysis
   • Voice-enabled interactions
   • Video consultation analysis

2. Advanced Analytics

   • Population health insights
   • Predictive modeling
   • Treatment outcome prediction

3. Specialized Models

   • Radiology-specific AI
   • Pathology analysis
   • Genomic interpretation

4. Local AI Options

   • On-premise deployment
   • Edge computing
   • Privacy-preserving models

Resources

• OpenAI API Documentation
• HIPAA Compliance for AI
• Medical AI Best Practices