Secure, compliant model-to-model communication infrastructure for healthcare AI.
The Model Context Protocol (MCP) is HealthCloud's standardized protocol for secure, compliant AI model-to-model communication. It enables AI agents to collaborate while maintaining strict compliance with healthcare regulations.
MCP ensures that when multiple AI models work together, patient data remains encrypted, audit trails are maintained, and all interactions comply with HIPAA, GDPR, and other healthcare data protection standards.
All model communications are encrypted in transit using TLS 1.3 and at rest using AES-256.
Every model interaction is logged with timestamps, model IDs, data exchanged, and outcomes.
Optimized for real-time clinical workflows with sub-100ms response times.
Maintains patient context and session state across multiple model invocations.
/api/v1/mcp/sessions{
"source_model_id": "radiology-classifier-v2",
"target_model_id": "clinical-decision-engine",
"patient_id": "patient-123",
"context": {
"encounter_id": "encounter-456",
"clinical_priority": "high"
},
"compliance_requirements": {
"hipaa": true,
"encrypt_phi": true,
"audit_level": "detailed"
}
}{
"session_id": "mcp-sess-789abc",
"status": "active",
"expires_at": "2025-11-02T11:30:00Z",
"encryption_key_id": "key-123",
"audit_trail_id": "audit-456"
}/api/v1/mcp/sessions/:sessionId/messages{
"message_type": "inference_request",
"payload": {
"fhir_resources": [
{
"resourceType": "DiagnosticReport",
"id": "report-123",
"status": "final",
"code": {
"coding": [{
"system": "http://loinc.org",
"code": "24627-2",
"display": "Chest X-Ray"
}]
},
"conclusion": "Possible pneumonia",
"imagingStudy": {
"reference": "ImagingStudy/study-456"
}
}
]
},
"metadata": {
"priority": "high",
"timeout_ms": 5000
}
}import { MCPClient } from '@healthcloud/mcp';
const mcpClient = new MCPClient({
apiKey: process.env.HEALTHCLOUD_API_KEY,
modelId: 'radiology-classifier-v2'
});
// Create a secure session
const session = await mcpClient.createSession({
targetModelId: 'clinical-decision-engine',
patientId: 'patient-123',
context: {
encounterId: 'encounter-456',
clinicalPriority: 'high'
}
});
// Send encrypted message
const response = await session.send({
type: 'inference_request',
payload: {
fhirResources: [diagnosticReport, imagingStudy]
}
});
// Process response
console.log('Recommendation:', response.recommendation);
console.log('Confidence:', response.confidence);
// Close session (triggers audit log finalization)
await session.close();Radiology AI analyzes X-ray → sends findings to clinical decision support model → generates treatment recommendations → final review by physician AI assistant
Prescription AI validates new medication → queries drug interaction model → checks patient allergy database → confirms with pharmacogenomics model
EHR extraction model → eligibility criteria matching → genomic compatibility check → trial recommendation engine
Patient risk stratification model → readmission prediction → resource allocation AI → care coordination recommendations
Ready to build secure, compliant AI agent workflows?