dotimplement · jenniferjiangkells · Oct 1, 2025 · Oct 1, 2025 · Oct 1, 2025 · Oct 1, 2025
diff --git a/README.md b/README.md
diff --git a/docs/assets/images/hc-use-cases-clinical-integration.png b/docs/assets/images/hc-use-cases-clinical-integration.png
diff --git a/docs/assets/images/hc-use-cases-genai-aggregate.png b/docs/assets/images/hc-use-cases-genai-aggregate.png
diff --git a/docs/assets/images/hc-use-cases-ml-deployment.png b/docs/assets/images/hc-use-cases-ml-deployment.png
diff --git a/docs/assets/images/interopengine.png b/docs/assets/images/interopengine.png
diff --git a/docs/assets/images/openapi_docs.png b/docs/assets/images/openapi_docs.png
diff --git a/docs/cookbook/ml_model_deployment.md b/docs/cookbook/ml_model_deployment.md
@@ -0,0 +1,64 @@
+# Deploy ML Models as Healthcare APIs
+
+*This example is coming soon! 🚧*
+
+<div align="center">
+  <img src="../assets/images/hc-use-cases-ml-deployment.png" alt="ML Model Deployment Architecture" width="60%">
+</div>
+
+## Overview
+
+This tutorial will demonstrate how to deploy any trained ML model as a production-ready healthcare API with FHIR input/output, multi-EHR connectivity, and comprehensive monitoring.
+
+## What You'll Learn
+
+- **Model serving architecture** - Deploy Hugging Face, scikit-learn, PyTorch, and custom models
+- **FHIR-native endpoints** - Serve predictions with structured healthcare data formats
+- **Multi-EHR integration** - Connect your model to live FHIR servers for real-time inference
+- **Healthcare data validation** - Ensure type-safe input/output with Pydantic models
+- **Production monitoring** - Track model performance, data drift, and API health
+- **Scalable deployment** - Configure auto-scaling and load balancing for healthcare workloads
+
+## Architecture
+
+The example will showcase:
+
+1. **Model Packaging** - Wrap any ML model with HealthChain's deployment framework
+2. **FHIR Endpoint Creation** - Automatically generate OpenAPI-compliant healthcare APIs
+3. **Real-time Inference** - Process FHIR resources and return structured predictions
+4. **Multi-source Integration** - Connect to Epic, Cerner, and other FHIR systems
+5. **Performance Monitoring** - Track latency, throughput, and prediction quality
+6. **Security & Compliance** - Implement OAuth2, audit logging, and data governance
+
+## Use Cases
+
+Perfect for:
+- **Clinical Decision Support** - Deploy diagnostic or prognostic models in EHR workflows
+- **Population Health** - Serve risk stratification models for large patient cohorts
+- **Research Platforms** - Make trained models available to clinical researchers
+- **AI-powered Applications** - Build healthcare apps with ML-driven features
+
+## Example Models
+
+We'll show deployment patterns for:
+- **Clinical NLP models** - Named entity recognition, clinical coding, text classification
+- **Diagnostic models** - Medical imaging analysis, lab result interpretation
+- **Risk prediction models** - Readmission risk, mortality prediction, drug interactions
+- **Recommendation systems** - Treatment recommendations, medication optimization
+
+## Prerequisites
+
+- A trained ML model (any framework supported)
+- Understanding of FHIR resources and healthcare data standards
+- Python environment with HealthChain installed
+- Basic knowledge of API deployment concepts
+
+## Coming Soon
+
+We're building comprehensive examples covering multiple model types and deployment scenarios!
+
+In the meantime, explore our [Gateway documentation](../reference/gateway/gateway.md) to understand the deployment infrastructure.
+
+---
+
+**Want to be notified when this example is ready?** Join our [Discord community](https://discord.gg/UQC6uAepUz) for updates!
diff --git a/docs/cookbook/multi_ehr_aggregation.md b/docs/cookbook/multi_ehr_aggregation.md
@@ -0,0 +1,55 @@
+# Multi-EHR Data Aggregation Guide
+
+*This example is coming soon! 🚧*
+
+<div align="center">
+  <img src="../assets/images/hc-use-cases-genai-aggregate.png" alt="Multi-EHR Data Aggregation Architecture" width="100%">
+</div>
+
+## Overview
+
+This comprehensive tutorial will show you how to build a patient data aggregation system that connects to multiple EHR systems, combines patient records, and enriches them with AI-powered insights.
+
+## What You'll Learn
+
+- **Multi-source FHIR connectivity** - Connect to Epic, Cerner, and other FHIR servers simultaneously
+- **Patient record matching** - Identify and link patient records across different systems
+- **Data deduplication** - Handle overlapping and duplicate information intelligently
+- **NLP enrichment** - Extract insights from clinical notes and add structured data
+- **Unified patient timelines** - Create comprehensive patient views across all systems
+- **Real-time synchronization** - Keep data updated across multiple sources
+
+## Architecture
+
+The example will demonstrate:
+
+1. **FHIR Gateway Setup** - Configure connections to multiple healthcare systems
+2. **Patient Matching Algorithm** - Match patients across systems using demographics and identifiers
+3. **Data Aggregation Pipeline** - Combine and normalize patient data from different sources
+4. **NLP Processing** - Extract medical entities and conditions from clinical notes
+5. **Conflict Resolution** - Handle discrepancies between different data sources
+6. **Export & Analytics** - Generate unified datasets for research and analytics
+
+## Use Cases
+
+Perfect for:
+- **Healthcare Analytics** - Create comprehensive datasets for population health studies
+- **Clinical Research** - Aggregate patient cohorts from multiple institutions
+- **AI/ML Training** - Build rich, multi-source datasets for model training
+- **Patient Care Coordination** - Provide clinicians with complete patient views
+
+## Prerequisites
+
+- Multiple FHIR server connections (we'll show how to set up test environments)
+- Basic understanding of FHIR resources (Patient, Observation, Condition)
+- Python environment with HealthChain installed
+
+## Coming Soon
+
+We're actively developing this example and it will be available soon!
+
+In the meantime, check out our [Gateway documentation](../reference/gateway/fhir_gateway.md) to understand the fundamentals of multi-source FHIR connectivity.
+
+---
+
+**Want to be notified when this example is ready?** Join our [Discord community](https://discord.gg/UQC6uAepUz) for updates!
diff --git a/docs/index.md b/docs/index.md
@@ -1,6 +1,6 @@
 # Welcome to HealthChain 💫 🏥
 
-HealthChain is an open-source Python framework that makes it easier to connect your AI/ML pipelines to healthcare systems.
+HealthChain is an open-source Python toolkit that streamlines productionizing healthcare AI. Built for AI/ML practitioners, it simplifies the complexity of real-time EHR integrations by providing seamless FHIR integration, unified data pipelines, and production-ready deployment.
 
 [ :fontawesome-brands-discord: Join our Discord](https://discord.gg/UQC6uAepUz){ .md-button .md-button--primary }
 &nbsp;&nbsp;&nbsp;&nbsp;
@@ -10,55 +10,64 @@ HealthChain is an open-source Python framework that makes it easier to connect y
 
 <div class="grid cards" markdown>
 
-
--   :material-tools:{ .lg .middle } __Build a pipeline__
+-   :material-tools:{ .lg .middle } __FHIR-native ML Pipelines__
 
     ---
 
-    Create custom pipelines or use pre-built ones for your healthcare NLP and ML tasks
+    Create custom pipelines or use pre-built ones for healthcare NLP and ML tasks with automatic FHIR output
 
     [:octicons-arrow-right-24: Pipeline](reference/pipeline/pipeline.md)
 
--   :material-connection:{ .lg .middle } __Connect to multiple data sources__
+-   :material-connection:{ .lg .middle } __Multi-EHR Gateway__
 
     ---
 
-    Connect to multiple healthcare data sources and protocols with **HealthChainAPI**.
+    Connect to multiple healthcare systems with unified API supporting FHIR, CDS Hooks, and SOAP/CDA protocols
 
     [:octicons-arrow-right-24: Gateway](reference/gateway/gateway.md)
 
--   :material-database:{ .lg .middle } __Interoperability__
+-   :material-database:{ .lg .middle } __Healthcare Data Conversion__
 
     ---
 
-    Configuration-driven **InteropEngine** to convert between FHIR, CDA, and HL7v2
+    Convert between FHIR, CDA, and HL7v2 formats using configuration-driven InteropEngine
 
     [:octicons-arrow-right-24: Interoperability](reference/interop/interop.md)
 
--   :material-fire:{ .lg .middle } __Utilities__
+-   :material-fire:{ .lg .middle } __Developer Utilities__
 
     ---
 
-    FHIR data model utilities and helpers to make development easier
+    Type-safe FHIR resources, validation helpers, and sandbox environments for rapid development
 
     [:octicons-arrow-right-24: Utilities](reference/utilities/fhir_helpers.md)
 
+</div>
 
+## Getting Started with Healthcare AI
 
-</div>
+HealthChain provides the missing middleware layer between healthcare systems and modern AI/ML development. Whether you're building clinical decision support tools, processing medical documents, or creating multi-system integrations, these docs will guide you through:
+
+- **🔧 Core concepts** - Understand FHIR resources, pipelines, and gateway patterns
+- **📚 Real examples** - Step-by-step tutorials for common healthcare AI use cases
+- **🏗️ Advanced patterns** - Production deployment, authentication, and multi-EHR workflows
+- **🧪 Testing tools** - Sandbox environments and utilities for development
+
+## What You Can Build with HealthChain
 
-## Why HealthChain?
+|   | Use Case                              | Description                                                                 |
+|---|---------------------------------------|-----------------------------------------------------------------------------|
+| 🚨 | **CDS alerts for discharge summaries** | Generate clinical recommendations directly in Epic workflows                |
+| 📋 | **Automatic medical coding**          | Extract ICD-10 or SNOMED-CT codes from physician notes with confidence scores|
+| 🔗 | **Multi-EHR patient aggregation**     | Combine patient records from Epic, Cerner, and specialty systems            |
+| 🤖 | **ML model deployment**               | Serve your trained healthcare models as FHIR-compliant APIs                 |
+| 🔄 | **Legacy document conversion**        | Transform CDA documents to modern FHIR resources                            |
 
-Healthcare AI development has a **missing middleware layer**. Traditional enterprise integration engines move data around, EHR platforms serve end users, but there's nothing in between for developers building AI applications that need to talk to multiple healthcare systems. Few solutions are open-source, and even fewer are built in modern Python where most ML/AI libraries thrive.
+**New to healthcare AI?** Start with our [Quickstart Guide](quickstart.md) to build your first medical NLP pipeline in under 10 minutes.
 
-HealthChain fills that gap with:
+**Ready to integrate with EHRs?** Jump to our [Cookbook](cookbook/index.md) for complete examples including CDS Hooks and FHIR integration.
 
-- **🔥 FHIR-native ML pipelines** - Pre-built NLP/ML pipelines optimized for structured / unstructured healthcare data, or build your own with familiar Python libraries such as 🤗 Hugging Face, 🤖 LangChain, and 📚 spaCy
-- **🔒 Type-safe healthcare data** - Full type hints and Pydantic validation for FHIR resources with automatic data validation and error handling
-- **🔌 Multi-protocol connectivity** - Handle FHIR, CDS Hooks, and SOAP/CDA in the same codebase with OAuth2 authentication and connection pooling
-- **⚡ Event-driven architecture** - Real-time event handling with audit trails and workflow automation built-in
-- **🔄 Built-in interoperability** - Convert between FHIR, CDA, and HL7v2 using a template-based engine
-- **🚀 Production-ready deployment** - FastAPI integration for scalable, real-time applications
+---
 
 HealthChain is made by a small team with experience in software engineering, machine learning, and healthcare NLP. We understand that good data science is about more than just building models, and that good engineering is about more than just building systems. This rings especially true in healthcare, where people, processes, and technology all play a role in making an impact.
 

diff --git a/pyproject.toml b/pyproject.toml
@@ -1,12 +1,12 @@
 [tool.poetry]
 name = "healthchain"
 version = "0.0.0"
-description = "Python toolkit that makes it easier to connect your AI/ML pipelines to healthcare systems"
+description = "Open source framework for productionizing healthcare AI"
 authors = ["Jennifer Jiang-Kells <jenniferjiangkells@gmail.com>", "Adam Kells <adamjkells93@gmail.com>"]
 license = "Apache-2.0"
 readme = "README.md"
 documentation = "https://dotimplement.github.io/HealthChain/"
-keywords = ["nlp", "ai", "llm", "healthcare", "ehr", "mlops"]
+keywords = ["nlp", "ai", "llm", "healthcare", "ehr", "mlops", "fhir"]
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",