Platform Overview

Clinical AI infrastructure
built for production.

A deep look into the architecture, modules, integrations, and security practices that power Synthia Health across 40+ health systems.

System Architecture

End-to-end clinical data pipeline

From electronic health record ingestion through AI inference to real-time clinical output, every stage is designed for HIPAA-grade security and sub-200ms latency.

Source
EHR Systems
Epic, Cerner, Meditech via HL7 FHIR R4
→
Ingest
Data Normalization
De-identification, OMOP CDM mapping, validation
→
Process
AI Pipeline
Ensemble models, NLP, temporal analysis, pharmacokinetics
→
Validate
Clinical Review
Confidence scoring, evidence linking, bias detection
→
Output
Clinical Action
EHR alerts, dashboards, CPOE integration, reports
Core Modules

Four specialized AI modules

Each module is independently validated, continuously monitored, and designed to integrate seamlessly into existing clinical workflows.

Module 01

Predictive Diagnostics

Our temporal modeling engine ingests streaming vitals, lab trends, and clinical notes to identify conditions before they fully manifest. The system analyzes rate-of-change patterns across 127 biomarkers simultaneously, comparing real-time patient trajectories against a knowledge base of 14 million de-identified encounters.

  • Transformer-based temporal model with 340M parameters trained on multi-institutional ICU data
  • Covers 340+ conditions including sepsis, acute kidney injury, cardiac arrest, pulmonary embolism, and respiratory failure
  • 72-hour prediction window with rolling 15-minute confidence updates
  • Explainable AI output: every prediction includes the top 5 contributing features with SHAP values
  • Automated retraining pipeline with monthly model refresh cycles
97.3%
Diagnostic Accuracy
72hr
Prediction Window
340+
Conditions Covered
94.1%
Sensitivity (Sepsis)
Module 02

Continuous Patient Monitoring

Real-time streaming analysis of bedside telemetry, wearable data, nurse assessments, and medication administration records. Our intelligent alert prioritization system learns institutional-specific alarm patterns to dramatically reduce fatigue while maintaining clinical safety.

  • Ingests up to 2,000 data points per second per patient from bedside monitors and IoT devices
  • Multi-channel fusion: ECG, SpO2, blood pressure, respiratory rate, temperature, and accelerometry
  • Contextual alert scoring considers patient acuity, recent interventions, and nursing workload
  • MEWS/NEWS2 auto-calculation with predictive deterioration overlay
  • Configurable escalation pathways with role-based notification routing
68%
Alert Fatigue Reduction
2,000/s
Data Points Per Patient
99.7%
Critical Event Detection
<50ms
Alert Latency
Module 03

Drug Interaction Analysis

Goes beyond standard formulary-based checks by incorporating patient-specific pharmacogenomic data, renal/hepatic function, age-adjusted dosing, and a continuously updated pharmacokinetic knowledge graph spanning 12,000+ drug entities and 280,000+ interaction pairs.

  • Knowledge graph with 12,000+ drug entities including investigational compounds from ClinicalTrials.gov
  • CYP450 enzyme pathway modeling with patient-specific genetic variant integration
  • Real-time renal dosing adjustment based on eGFR trends and dialysis schedules
  • Polypharmacy risk scoring for patients on 5+ concurrent medications
  • Integration with institutional formulary and pharmacy workflow systems
99.2%
Interaction Detection Rate
12K+
Drug Entities
280K+
Interaction Pairs
3.2x
vs. Standard Formulary
Module 04

Clinical Decision Support

Evidence-based treatment recommendations grounded in current literature, institutional protocols, and patient-specific factors. Every recommendation is linked to its supporting evidence with full citation chains, enabling clinicians to verify and trust AI-assisted decisions.

  • Continuously indexed against PubMed, Cochrane Library, and 35 specialty-specific guideline databases
  • Institutional protocol engine allows custom clinical pathways and order sets
  • Patient-specific contraindication checking across allergies, comorbidities, and genetic markers
  • Natural language rationale generation for every recommendation, using GPT-based clinical summarization
  • Feedback loop: clinician acceptance/override data used for continuous model improvement
89%
Clinician Acceptance Rate
2.4M+
Evidence Sources Indexed
35
Guideline Databases
41%
Faster Decision Time
Integrations

Designed to fit your
existing infrastructure

Native interoperability with major EHR platforms, standards-based APIs, and flexible deployment options.

HL7 FHIR R4

Standards-Based Interoperability

Full HL7 FHIR R4 compliance with support for US Core, Da Vinci, and SMART on FHIR authorization. Our FHIR server supports all resource types relevant to clinical decision support.

  • Patient, Encounter, Observation, Condition
  • MedicationRequest, MedicationAdministration
  • DiagnosticReport, Procedure, AllergyIntolerance
  • ClinicalImpression, RiskAssessment
  • SMART on FHIR launch context
  • Bulk FHIR export for analytics
EHR Systems

Supported Platforms

Pre-built connectors for the three leading EHR platforms, with a universal adapter framework for custom integrations with any FHIR-capable system.

  • Epic (Interconnect, App Orchard certified)
  • Oracle Health / Cerner (Millennium, code certified)
  • Meditech (Expanse, Web Services API)
  • Allscripts (Open API, Touchworks)
  • athenahealth (athenaNet, Marketplace)
  • Custom HL7v2 ADT/ORM/ORU adapters
API

REST API Endpoints

Comprehensive REST API with OAuth 2.0 authentication, rate limiting, webhook subscriptions, and full OpenAPI 3.0 documentation.

  • POST /v2/analyze — Full clinical analysis
  • POST /v2/predict — Predictive diagnostics
  • POST /v2/interactions — Drug check
  • GET /v2/patient/{id}/timeline — History
  • POST /v2/alerts/subscribe — Webhooks
  • GET /v2/models/status — Health check
clinical_analysis.py
# Full clinical analysis with evidence linking
import synthia

client = synthia.Client(
  api_key="sk-synth-...",
  environment="production",
  fhir_base="https://ehr.hospital.org/fhir/R4"
)

# Run predictive analysis on encounter
result = client.analyze(
  patient_id="Patient/abc-123",
  encounter_id="Encounter/enc-456",
  modules=["predictive", "monitoring", "pharma"],
  include_evidence=True,
  confidence_threshold=0.85
)

# Access structured predictions
for dx in result.diagnoses:
  print(f"{dx.condition}: {dx.confidence:.1%}")
  print(f" Evidence: {dx.evidence_refs}")
drug_interaction_check.py
# Real-time drug interaction analysis
from synthia.pharma import InteractionEngine

engine = InteractionEngine(
  include_pharmacogenomics=True,
  renal_adjustment=True
)

interactions = engine.check(
  patient_id="Patient/abc-123",
  new_medication={
    "code": "RxNorm:1049221",
    "name": "Amiodarone 200mg",
    "route": "oral"
  },
  active_meds=get_active_medications("Patient/abc-123")
)

# Severity-ranked interaction results
for alert in interactions.alerts:
  print(f"[{alert.severity}] {alert.description}")
  print(f" Mechanism: {alert.mechanism}")
  print(f" Recommendation: {alert.recommendation}")
webhook_alerts.js
// Subscribe to real-time clinical alerts via webhooks
const synthia = require('@synthia/sdk');

const client = new synthia.Client({
  apiKey: process.env.SYNTHIA_API_KEY,
  webhookSecret: process.env.WEBHOOK_SECRET
});

// Register webhook for critical alerts
await client.alerts.subscribe({
  url: 'https://hospital.org/api/synthia-alerts',
  events: ['critical_alert', 'deterioration', 'interaction'],
  filters: {
    severity: ['high', 'critical'],
    units: ['ICU', 'ED', 'Step-Down']
  }
});

// Handle incoming webhook payloads
app.post('/api/synthia-alerts', (req, res) => {
  const alert = client.verifyWebhook(req);
  console.log(`Alert: ${alert.type} for ${alert.patientId}`);
  notifyCareTeam(alert);
});
Security & Compliance

Enterprise-grade security
at every layer

Patient data protection is not a feature -- it is the foundation of everything we build.

Certified

HIPAA Compliance

Full compliance with the HIPAA Privacy Rule, Security Rule, and Breach Notification Rule. Annual third-party audits by Coalfire with zero critical findings across four consecutive years.

  • Business Associate Agreements (BAA) executed with all partners
  • End-to-end encryption: AES-256 at rest, TLS 1.3 in transit
  • Automated PHI detection and de-identification pipeline
  • Role-based access control with multi-factor authentication
  • Comprehensive audit logging with 7-year retention
Audited

SOC 2 Type II

Continuous compliance across all five Trust Services Criteria: Security, Availability, Processing Integrity, Confidentiality, and Privacy. Audited annually by Ernst & Young.

  • Continuous monitoring of 200+ security controls
  • 99.99% uptime SLA with geographic redundancy
  • Incident response plan with 15-minute escalation SLA
  • Penetration testing conducted quarterly by NCC Group
  • Vendor risk management program for all sub-processors
Validated

HITRUST CSF

HITRUST r2 certified, providing the most comprehensive and prescriptive security framework specifically designed for the healthcare industry.

  • Full r2 certification covering 19 control domains
  • Maps to NIST CSF, ISO 27001, PCI DSS, and COBIT
  • Annual validated assessment with independent assessor
  • Continuous assurance program with real-time control monitoring
  • Third-party risk management integrated into certification scope
Protected

Data Handling Practices

Patient data never leaves your infrastructure in our on-premise deployment model. For cloud deployments, data residency and sovereignty requirements are fully configurable.

  • On-premise deployment: zero data egress, all processing local
  • Private cloud: dedicated VPC with customer-managed encryption keys
  • Automated data retention policies with cryptographic deletion verification
  • De-identification pipeline exceeds Safe Harbor and Expert Determination standards
  • Model training only on consented, IRB-approved, de-identified datasets
Get Started

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the platform?

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