HealthChain is an open-source SDK for production-ready healthcare AI. Skip months of custom integration work with built-in FHIR support, real-time EHR connectivity, and deployment tooling for healthcare AI/ML systems — all in Python.
pip install healthchain# Scaffold a FHIR Gateway project
healthchain new my-app -t fhir-gateway
cd my-app
# Run locally
healthchain serve
Edit app.py to add your model, and healthchain.yaml to configure compliance, security, and deployment settings.
See the CLI reference for all commands.
HealthChain is the quickest way for AI engineers to connect their models to real healthcare data.
🔌 Multi-EHR Data Aggregation Aggregate patient data from multiple FHIR sources into unified records with built-in NLP processing and automatic deduplication Getting Started → |
🚀 Deploy ML Models as Healthcare APIs Turn any trained model into a production-ready FHIR endpoint with OAuth2 authentication and type-safe healthcare data handling Getting Started → |
⚡️ Real-Time Clinical Workflow Integration Deploy AI models as CDS services that integrate directly into EHR workflows — alerts, recommendations, and automated coding at the point of care Getting Started → |
🔥 FHIR Development Utilities Type-safe FHIR resource creation, validation helpers, and sandbox environments — skip the boilerplate and work with healthcare data natively Getting Started → |
Electronic health record (EHR) data is specific, complex, and fragmented. Most healthcare AI projects require months of manual integration and custom validation on top of model development. This leads to fragile pipelines that break easily and consume valuable developer time.
HealthChain understands healthcare protocols and data formats natively, so you don't have to build that knowledge from scratch. Skip months of custom integration work and productionize your healthcare AI faster.
- Optimized for real-time - Connect to live FHIR APIs and integration points instead of stale data exports
- Automatic validation - Type-safe FHIR models prevent broken healthcare data
- Native LLM + ML support - Wire up any model, from LLMs to scikit-learn, and output results as FHIR
- Developer experience - Modular and extensible architecture works across any EHR system
- Production-ready foundations - Dockerized deployment, configurable security and compliance settings, and an architecture designed for real-world healthcare environments
Featured & Presented:
- Featured by Medplum for open source integration with Epic
- Featured in TLDR AI Newsletter (900K+ developers)
- Presented at NHS Python Open Source Conference (watch talk)
- Built from NHS AI deployment experience – read the origin story
Exploring HealthChain for your product or organization? Get in touch to discuss integrations, pilots, or collaborations, or join our Discord to connect with the community.
Building a Pipeline [Docs]
from healthchain.pipeline import Pipeline
from healthchain.pipeline.components.integrations import SpacyNLP
from healthchain.io import Document
# Create medical NLP pipeline
nlp_pipeline = Pipeline[Document]()
nlp_pipeline.add_node(SpacyNLP.from_model_id("en_core_web_sm"))
nlp = nlp_pipeline.build()
doc = Document("Patient presents with hypertension and diabetes.")
result = nlp(doc)
spacy_doc = result.nlp.get_spacy_doc()
print(f"Entities: {[(ent.text, ent.label_) for ent in spacy_doc.ents]}")
print(f"FHIR conditions: {result.fhir.problem_list}") # Auto-converted to FHIR BundleCreating a Gateway [Docs]
from healthchain.gateway import HealthChainAPI, FHIRGateway
from healthchain.fhir.r4b import Patient
# Create healthcare application
app = HealthChainAPI(title="Multi-EHR Patient Data")
# Connect to multiple FHIR sources
fhir = FHIRGateway()
fhir.add_source("epic", "fhir://fhir.epic.com/r4?client_id=epic_client_id")
fhir.add_source("cerner", "fhir://fhir.cerner.com/r4?client_id=cerner_client_id")
@fhir.aggregate(Patient)
def enrich_patient_data(id: str, source: str) -> Patient:
"""Get patient data from any connected EHR and add AI enhancements"""
bundle = fhir.search(
Patient,
{"_id": id},
source,
add_provenance=True,
provenance_tag="ai-enhanced",
)
return bundle
app.register_gateway(fhir)
# Available at: GET /fhir/transform/Patient/123?source=epic
# Available at: GET /fhir/transform/Patient/123?source=cerner
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, port=8000)Testing with Sandbox [Docs]
from healthchain.gateway import HealthChainAPI, CDSHooksService
cds = CDSHooksService()
app = HealthChainAPI(title="Discharge Summarizer")
app.register_service(cds, path="/cds")
# Server lifecycle handled automatically
with app.sandbox("discharge-summary") as client:
client.load_from_path("./data/patients", pattern="*_patient.json")
responses = client.send_requests()
client.save_results("./output/")- 🔒 Production security and compliance — audit logging, API authentication, and governance config for NHS/HIPAA deployments
- 🔌 Deeper EHR connectivity — more FHIR sources, live data patterns, and real-world integration examples from pilot deployments
- 📊 Observability and Eval — model eval, deployment telemetry, and audit trails for clinical AI systems
- 🤖 AI agent ecosystem — MCP server, Claude skill for healthcare data workflows, and agentic integrations for the next generation of clinical AI tools
HealthChain is built by and for AI engineers working with healthcare data. The best contributions come from people who have hit a real problem and have something specific to say about it.
Get started:
- Working with healthcare or research data? Contribute a cookbook — bring your use case, I'll personally support you through it
- Read CONTRIBUTING.md for guidelines
- Technical questions and ideas → GitHub Discussions
- Pilots and partnerships → email
This project builds on fhir.resources and CDS Hooks standards developed by HL7 and Boston Children's Hospital.
© 2024–2026 dotimplement ai. HealthChain is an open source project maintained by dotimplement ai.