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HL7 FHIR Integration for Clinical AI — Udemy Course
Now on Udemy  ·  New course 2025

HL7 & FHIR Integration
for Clinical AI A Practitioner's Guide · Mirth Connect · Epic

Build production-grade clinical data pipelines from raw HL7 v2 hospital feeds through FHIR R4 APIs, Mirth Connect channels, Epic EHR connectivity, and live AI inference — with every concept backed by real hospital deployments.

Enroll now — $19.99 View curriculum ↓
8×
Modules
45+
Lessons
12+
Hands-on labs
14h
Total content
$0
Lab tool cost
SourceEpic EHR
→
ProtocolHL7 v2
→
EngineMirth Connect
→
StandardFHIR R4
→
InferenceAI Model
→
FeedbackCDS Hooks

Purpose

Why this course exists

Clinical AI fails at the integration layer — not the model layer. This course fixes that gap by teaching the exact data engineering stack that connects hospital systems to inference engines.

🏥
Real-world, not theoretical
Every concept is grounded in production deployments at major academic medical centers — including the MySurgeryRisk platform at UF Shands Hospital.
⚡
End-to-end pipeline mastery
From raw HL7 v2 byte streams to CDS Hooks alert cards inside Epic — you will own the full stack, not just isolated pieces.
🆓
Zero tool cost to learn
Mirth Connect Community Edition, Epic FHIR public sandbox, HAPI FHIR Server, and HAPI TestPanel are all free. No hospital system access required.
🤖
AI-first integration design
Every architectural decision — trigger events, channel filters, FHIR query patterns — is made with downstream AI inference in mind, not just data movement.

Who is this for

Built for practitioners,
not observers

This is a hands-on engineering course. If you write code and want to build or extend clinical AI systems, you belong here.

✓
ML engineers & AI architects working in healthcare or moving into the space who need to understand how clinical data actually flows.
✓
Software engineers tasked with Epic or EHR integration projects who need to understand HL7, FHIR, and Mirth from first principles.
✓
Clinical informaticists and health IT professionals who want to add AI/ML integration capability to their existing domain expertise.
✓
Researchers building clinical AI models who need to understand how production hospital data is structured, ingested, and governed.
✓
Developers from other domains — fintech, IoT, enterprise software — making a deliberate move into health tech.
✓
Biomedical informatics students and postdocs who want production-level engineering skills alongside their academic training.
Prerequisites — you need these coming in
Python (intermediate) REST APIs & JSON Basic OAuth concepts Command-line comfort Docker basics (M8)

Sample content

Real code, real patterns

Every lab produces working code you can deploy. Here's a taste of what you'll build in Module 3.

mirth_transformer.js  ·  Module 3 — Mirth Connect
// HL7 v2 OBX → FHIR R4 Observation — Mirth destination transformer var mrn = msg['PID']['PID.3']['PID.3.1'].toString(); var loinc = msg['OBX'][0]['OBX.3']['OBX.3.1'].toString(); var val = parseFloat(msg['OBX'][0]['OBX.5']['OBX.5.1'].toString()); var flag = msg['OBX'][0]['OBX.8']['OBX.8.1'].toString(); var fhir = { resourceType: 'Observation', status: 'final', subject: { reference: 'Patient?identifier=urn:epic:mrn|' + mrn }, code: { coding: [{ system: 'http://loinc.org', code: loinc }] }, valueQuantity:{ value: val, unit: 'mg/dL' }, interpretation: flag ? [{ coding: [{ code: flag }] }] : undefined }; channelMap.put('fhirPayload', JSON.stringify(fhir)); // → Destination: HTTP POST to HAPI FHIR server /fhir/r4/Observation

Curriculum

8 modules, end to end

Structured from fundamentals to production — each module builds on the last, culminating in a fully deployed clinical AI pipeline.

M1
Foundation: HL7 v2 Messaging Deep Dive
~90 min
7 lessons1 lab
MSH / PID / OBX segments ADT trigger events MLLP transport HAPI TestPanel lab
M2
FHIR R4 for Clinical AI Practitioners
~110 min
9 lessons1 lab
Patient / Observation / Condition $lastn operation Bulk Data $export Epic sandbox queries
M3
Mirth Connect: Channel Engineering
~130 min
10 lessons1 lab
JS transformers Content-based routing Dead-letter queues CI/CD via Mirth REST API
M4
Epic Integration: SMART on FHIR & Interconnect
~120 min
9 lessons1 lab
SMART OAuth 2.0 flows JWT client assertion CDS Hooks cards App Orchard registration
M5
FHIR Feature Extraction for AI Models
~100 min
8 lessons1 lab
Time-series lab features Charlson / Elixhauser scoring FHIR feature store Missing data imputation
M6
Clinical AI Inference & CDS Hooks
~120 min
8 lessons1 lab
FastAPI inference service FHIR RiskAssessment write-back ADT trigger → model loop Alert fatigue strategy
M7
NLP on Clinical Notes via DocumentReference
~90 min
8 lessons1 lab
scispaCy entity extraction SDOH from free text LLM note summarization FHIR write-back of NLP output
M8
Production, Compliance & MLOps
~100 min
9 lessons1 labcase study
HIPAA Safe Harbor de-ID MLflow model registry Evidently AI monitoring MySurgeryRisk case study FDA SaMD landscape

Capstone projects

Three production systems
you will build

Labs are not toy exercises. Each produces a deployable artifact you can show, extend, and put in a portfolio.

01
🔌
HL7 → FHIR Integration Channel
A fully working Mirth Connect channel that receives Epic HL7 v2 ADT and ORU feeds, transforms them to FHIR R4 Observation and Patient resources, and POSTs them to a local HAPI FHIR server with audit logging.
02
🧠
Real-Time Sepsis Risk Pipeline
Mirth ADT trigger → FHIR feature extraction → XGBoost inference → CDS Hooks alert card surfaced inside a simulated Epic clinician workflow, with FHIR RiskAssessment write-back and MLflow tracking.
03
📄
Clinical NLP Pipeline
A pipeline that retrieves H&P notes via FHIR DocumentReference, extracts medications, diagnoses, and social determinants using scispaCy, and writes structured FHIR Observations back to the server.

Technology stack

Every tool is free

Mirth Connect CE
FHIR R4 (Epic sandbox)
HAPI FHIR Server
HAPI TestPanel
FastAPI
XGBoost / scikit-learn
scispaCy
MLflow
Evidently AI
PostgreSQL / Redis
Docker
CDS Hooks

Instructor

Built by someone who
shipped it in production

MM
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.
PhD Computer Science Postdoc · UTHealth Houston Intuitive Surgical Cummins UF Shands · MySurgeryRisk AI/ML Boot Camp Instructor
Launch offer — limited time

Stop guessing how
clinical data actually flows

In 14 hours you will go from "what is an OBX segment" to deploying a FHIR-native AI inference service that surfaces risk scores inside Epic. All tools are free. All labs produce real, deployable code.

$19.99
List price $129.99
Launch discount · limited seats

Enroll on Udemy
30-day Udemy money-back guarantee · lifetime access · certificate of completion
© 2025 Mohcine Madkour, PhD — mohcinemadkour.com HL7 · FHIR R4 · Mirth Connect · Epic · Clinical AI