Test Suite Results:
  Case 1 - NSTEMI (Cardiology):
    Accuracy: 100%
    Key Findings: Correct troponin interpretation, appropriate cath lab activation
 
  Case 2 - Community-Acquired Pneumonia:
    Accuracy: 100%
    Key Findings: Correct CURB-65 scoring, appropriate antibiotic selection
 
  Case 3 - Diabetic Ketoacidosis:
    Accuracy: 100%
    Key Findings: Correct AG calculation, insulin protocol compliance
 
  Case 4 - Acute Stroke:
    Accuracy: 100%
    Key Findings: tPA window assessment, NIHSS scoring accuracy
 
  Case 5 - Appendicitis (Pediatric):
    Accuracy: 100%
    Key Findings: Alvarado score calculation, surgical consultation timing
 
Overall Clinical Accuracy: 5/5 (100%)

# Real-time transcription with clinical context
class ClinicalTranscriber:
    """Medical-optimized speech-to-text with terminology enhancement"""
 
    def transcribe_encounter(self, audio_stream):
        # Specialized medical vocabulary handling
        transcription = self.whisper_model.transcribe(
            audio_stream,
            vocabulary_boost=[
                'hypertension', 'diabetes', 'metformin',
                'bilateral', 'pruritic', 'erythematous'
            ]
        )
 
        # Clinical entity extraction
        entities = self.extract_medical_entities(transcription)
 
        return {
            'raw_text': transcription,
            'entities': entities,
            'structured_data': self.to_fhir(entities)
        }

Coding Capabilities:
  ICD-10-CM:
    - Diagnosis code assignment from clinical context
    - Specificity optimization (highest specificity selection)
    - HCC risk adjustment awareness
 
  CPT:
    - E/M level calculation (time-based & complexity)
    - Procedure code suggestion from documentation
    - Modifier application guidance
 
  Compliance:
    - Medical necessity validation
    - LCD/NCD coverage checks
    - Audit trail generation

// FHIR R4 Resource Generation
interface PatientEncounter {
  resourceType: 'Encounter';
  status: 'in-progress' | 'finished';
  class: {
    system: 'http://terminology.hl7.org/CodeSystem/v3-ActCode';
    code: 'AMB' | 'EMER' | 'IMP';
  };
  subject: Reference<Patient>;
  participant: Reference<Practitioner>[];
  diagnosis: EncounterDiagnosis[];
}
 
// Automatic FHIR bundle generation from encounter
const bundle = await vitruviana.generateFHIRBundle(encounterId);

// Multi-tenant data isolation
const middleware = async (req, res, next) => {
  const organizationId = await verifyJWTAndExtractOrg(req.headers.authorization);
 
  // All queries automatically scoped to organization
  req.prisma = new PrismaClient({
    datasources: {
      db: {
        url: process.env.DATABASE_URL
      }
    }
  }).$extends({
    query: {
      $allModels: {
        async $allOperations({ args, query }) {
          args.where = { ...args.where, organizationId };
          return query(args);
        }
      }
    }
  });
 
  next();
};

Production Deployment:
  Frontend: Vercel (Edge Network)
  Backend: Render (Dedicated Instance)
  Database: Render PostgreSQL (Encrypted)
  File Storage: AWS S3 (Server-Side Encryption)
  Monitoring: OpenTelemetry + Sentry

Solo Practice: $99/month
  - 1 provider seat
  - 100 encounters/month
  - Basic coding assistance
  - Email support
 
Group Practice: $199/month
  - Up to 5 provider seats
  - Unlimited encounters
  - Advanced coding + CDI
  - Priority support
  - Custom templates
 
Enterprise: Custom
  - Unlimited seats
  - Dedicated instance
  - EHR integration
  - On-premise option
  - 24/7 support
  - Custom AI training

Completed (✅):
  - Multi-agent orchestration framework
  - GPT-5 clinical reasoning integration
  - Real-time transcription pipeline
  - HIPAA-compliant infrastructure
  - User authentication & multi-tenancy
  - Basic coding assistance
  - FHIR resource generation
  - Stripe billing integration
  - Production deployment (Vercel + Render)
 
In Progress (🔄):
  - Advanced CDI features
  - EHR integration connectors
  - Mobile app (React Native)
  - Voice command interface
 
Planned (📋):
  - On-premise deployment option
  - Custom model fine-tuning
  - Specialty-specific modules

class ClinicalReasoningPipeline:
    """Multi-step clinical reasoning with evidence integration"""
 
    async def process_encounter(self, transcript, patient_context):
        # Step 1: Extract clinical entities
        entities = await self.intake_agent.extract(transcript)
 
        # Step 2: Generate differential diagnosis
        ddx = await self.orchestrator.generate_ddx(
            entities,
            patient_context
        )
 
        # Step 3: Evidence retrieval for top diagnoses
        evidence = await self.evidence_agent.retrieve(ddx[:5])
 
        # Step 4: Final assessment with reasoning chain
        assessment = await self.orchestrator.synthesize(
            entities, ddx, evidence,
            reasoning_mode='chain_of_thought'
        )
 
        return assessment

Safety Constraints:
  Never:
    - Provide diagnosis without clinical context
    - Recommend controlled substances autonomously
    - Override provider clinical judgment
    - Store PHI in AI model memory
 
  Always:
    - Flag critical/emergent findings
    - Cite evidence sources
    - Maintain audit trail
    - Defer to human judgment

# Provider preference learning
def adapt_template(provider_id, encounter_type, feedback):
    """Continuously improve documentation style per provider"""
 
    preferences = get_provider_preferences(provider_id)
 
    # Learn section ordering preferences
    preferences.section_order = update_preference(
        preferences.section_order,
        feedback.preferred_order
    )
 
    # Learn terminology preferences
    preferences.terminology = update_preference(
        preferences.terminology,
        feedback.preferred_terms
    )
 
    save_preferences(provider_id, preferences)