About the Client

The client is a venture-backed health technology innovator focused on cardio-respiratory disease prediction through advanced breath-cardio sensor technology and AI-driven diagnostics. Its platform combines real-time physiological monitoring with predictive analytics to enable early intervention and improved patient outcomes.
To operationalize its AI models within hospital environments, the client required seamless interoperability with Epic, one of the most widely deployed enterprise EHR platforms globally.

Problem Overview

The client’s AI engine required:

• • • Structured clinical data ingestion from EPIC
    • The ability to push processed observations back into clinical workflows

However:

• • • EPIC exposes data via Fast Healthcare Interoperability Resources (FHIR R4)
    • The client’s internal APIs followed a proprietary, AI-optimized schema
    • Direct schema compatibility did not exist

Core Challenge

Design and deploy a secure, scalable transformation layer capable of:

• • • Translating FHIR resources into the client’s internal data model
    • Preserving semantic integrity across encounters and observations
    • Supporting bi-directional data exchange
    • Maintaining HIPAA-compliant transport, authentication, and auditability

Without this bridge, the AI platform could not be embedded into hospital ecosystems.

Integration Scope

1. Inbound Data Flows (EPIC → AI Platform)

Support for:

• • • ADT A01 – Admit (Encounter.Search)
    • ADT A02 – Transfer (Encounter.Read)
    • ADT A03 – Discharge
    • ADT A08 – Update Visit
    • ADT A13 – Cancel Discharge
    • ADT A34 – Patient Merge (custom handling)
    • Observation.Read – Vital signs (HR, BP, SpO₂)

2. Outbound Data Flows (AI Platform → EPIC)

• • • ORU R1 – Submission of vitals and AI-derived observations (Observation.Create)

This required full bidirectional interoperability with EPIC’s FHIR R4 APIs.

Technology Architecture

The solution was architected as a stateless, modular transformation engine to enable horizontal scalability across multiple hospital deployments.

What Santeware Built

Santeware engineered a dedicated FHIR Transformation and Orchestration Layer that functioned as an interoperability bridge between EPIC and the AI platform.

1. FHIR Parsing & Semantic Transformation Engine

• • • Parsed FHIR R4 resources (Encounter, Patient, Observation)
    • Normalized complex JSON payloads
    • Mapped standardized fields to proprietary schema elements
    • Implemented extension handling for non-direct mappings

Custom logic was introduced for ADT A34 (patient merge scenarios), compensating for limitations in EPIC’s FHIR implementation.

2. Bidirectional API Orchestration

Inbound:

• • • Event-driven and scheduled encounter retrieval
    • MRN-based patient query mechanisms

Outbound:

• • • AI-generated vitals submitted via Observation.Create
    • Flag indicators for abnormal risk detection

This enabled closed-loop clinical intelligence within EPIC workflows.

3. Security & Compliance Framework

• • • OAuth 2.0 token-based authentication
    • Encrypted API communications (HTTPS)
    • Role-based access controls
    • HIPAA-aligned logging and audit trails

EPIC remained the source of truth while securely feeding the AI analytics engine.

4. Rate Limiting & Resilience Design

• • • Intelligent retry and exponential backoff mechanisms
    • Error traceability and structured logging
    • Pre-production sandbox validation

The system maintained operational stability within EPIC’s API throttling constraints.

The Impact

The integration established a production-ready interoperability framework enabling the AI platform to operate natively within EPIC ecosystems.

• • • Full FHIR R4 compatibility for scoped resources
    • Real-time ingestion of ADT and observational data
    • Bi-directional clinical data exchange operational
    • AI-derived vitals successfully written back into EPIC
    • Secure, HIPAA-compliant deployment model
    • Scalable architecture replicable across additional hospital systems

The platform transitioned from a standalone analytics tool to an embedded clinical intelligence layer within hospital workflows.

Conclusion

Santeware delivered a secure, scalable FHIR transformation layer that bridged standardized EHR data with proprietary AI-driven diagnostics.

By enabling bi-directional interoperability with EPIC, we created the infrastructure required for real-time predictive healthcare delivery within enterprise clinical environments.

This implementation now serves as a replicable model for AI-healthtech organizations seeking seamless integration with leading EHR platforms.