HL7 FHIR Integration for Clinical AI: A Practitioner's Guide

Build production-grade clinical data pipelines from raw HL7 v2 hospital feeds through FHIR R4 APIs, Mirth Connect, Epic EHR, and AI inference.

From raw HL7 v2 feeds to FHIR R4 APIs, Mirth Connect channel engineering, Epic SMART on FHIR, and production clinical AI inference — a complete practitioner curriculum for healthcare data integration.

Course Overview

Clinical AI projects frequently fail at the integration layer — not the model layer. This course closes that gap by teaching the exact data engineering stack and standards that connect live hospital systems to inference engines and model architectures. Grounded in real-world clinic-to-cloud deployments, you will learn to parse raw interface engine streams, authenticate across epic sandbox servers, and design secure hooks for decision support.

  • Platform: Udemy
  • Status: In Development (Launching May 2026)
  • Duration: 14 Hours of Content (8 Modules, 45+ Lessons, 12 Labs)
  • Tech Stack: HL7 v2 · FHIR R4 · Mirth Connect · Epic EHR · SMART on FHIR · Python · Docker · Kubernetes · MLflow
  • Waitlist/Registration: Enroll on Udemy

What You’ll Learn

Track A — Healthcare Data Fundamentals (Modules 1–3)

  • M1: Foundation: HL7 v2 Messaging Deep Dive (~90 min)
    • ADT trigger events, MSH/PID/OBX segments, MLLP transport protocol, and HAPI TestPanel lab.
  • M2: FHIR R4 for Clinical AI Practitioners (~110 min)
    • Patient/Observation/Condition resources, FHIR REST APIs, $lastn operation, Bulk Data $export, and querying Epic sandbox API servers.
  • M3: Mirth Connect: Channel Engineering (~130 min)
    • JavaScript transformers, destination mapping, filters, content-based routing, dead-letter queues, and Mirth REST API CI/CD.

Track B — Production Integration (Modules 4–6)

  • M4: Epic EHR Integration: SMART on FHIR & CDS Hooks (~120 min)
    • SMART OAuth 2.0 launch, JWT client assertion, CDS Hooks discovery, and Epic App Orchard navigation.
  • M5: FHIR Feature Extraction for AI Models (~100 min)
    • Building features from clinical resources, scoring comorbidity indexes (Charlson/Elixhauser), FHIR feature stores, and handling missing laboratory data.
  • M6: Clinical AI Inference & CDS Hooks (~120 min)
    • Real-time prediction with model wrappers, writing-back FHIR RiskAssessment results, and managing clinician alert fatigue.

Track C — AI Integration & Deployment (Modules 7–8)

  • M7: Document Extraction & Natural Language Processing (~90 min)
    • Querying DocumentReference endpoints, scispaCy medical entity extraction, de-identifying clinical notes, and LLM text summarization.
  • M8: Production, Compliance & MLOps (~100 min)
    • HIPAA Safe Harbor de-identification rules, MLflow Model Registry, Evidently AI drift monitoring, FDA SaMD classifications, and the MySurgeryRisk case study.

Three Key Production Projects You Will Build

  1. HL7 → FHIR Integration Channel: A fully functional Mirth Connect channel that ingests live HL7 v2 message ADT/ORU streams, parses the clinical observations, formats them into compliant FHIR R4 Observation/Patient resources, and POSTs them onto a local HAPI FHIR server.
  2. Real-Time Sepsis Risk Pipeline: An end-to-end loop that extracts FHIR features from real-time hospital feeds, propagates them through an XGBoost model wrapper, triggers live CDS Hooks alert cards in a clinician workflow, and logs risk scores inside FHIR RiskAssessment records.
  3. Clinical NLP Pipeline: An automated extractor that pulls patient H&P notes through FHIR DocumentReference, extracts medication/diagnosis categories with scispaCy NLP models, and writes structured Observations back to the EHR.

Target Audience

This is a hands-on engineering course designed for:

  • ML Engineers & AI Architects wanting to work with clinician systems or move into health tech.
  • Software Engineers tasked with Epic EHR integration or health data transformation.
  • Clinical Informaticists looking to add data/ML engineering pipeline capability to their existing clinical background.
  • Biomedical Researcher who want production-grade health IT skills alongside academic model validation.

Instructor

Mohcine Madkour, PhD
Senior AI/ML Engineer & Architect · Biomedical Informatics

I have spent the last decade building AI systems that run on real hospital data — from the MySurgeryRisk surgical risk prediction platform at UF Shands, to predictive maintenance systems at Intuitive Surgical, to connected diagnostics at Cummins. I have written the Mirth transformers, debugged the HL7 feeds, navigated Epic interface team relationships, and shipped models that touched patient care. This course teaches what I wish existed when I started.