From 021bbfaeddaa781988c38a27fcb4e47f35f52931 Mon Sep 17 00:00:00 2001
From: jenniferjiangkells <jenniferjiangkells@gmail.com>
Date: Tue, 12 Aug 2025 19:30:06 +0100
Subject: [PATCH 1/4] Update cookbook docs

---
 cookbook/notereader_clinical_coding_fhir.py   | 173 ++++++++++++
 docs/assets/images/medplum_client.png         | Bin 0 -> 116010 bytes
 docs/cookbook/cds_sandbox.md                  | 203 --------------
 docs/cookbook/clinical_coding.md              | 259 ++++++++++++++++++
 docs/cookbook/discharge_summarizer.md         | 226 +++++++++++++++
 docs/cookbook/index.md                        |   4 +-
 docs/cookbook/notereader_sandbox.md           |  75 -----
 .../prebuilt_pipelines/summarization.md       |   2 +-
 mkdocs.yml                                    |   4 +-
 9 files changed, 663 insertions(+), 283 deletions(-)
 create mode 100644 cookbook/notereader_clinical_coding_fhir.py
 create mode 100644 docs/assets/images/medplum_client.png
 delete mode 100644 docs/cookbook/cds_sandbox.md
 create mode 100644 docs/cookbook/clinical_coding.md
 create mode 100644 docs/cookbook/discharge_summarizer.md
 delete mode 100644 docs/cookbook/notereader_sandbox.md

diff --git a/cookbook/notereader_clinical_coding_fhir.py b/cookbook/notereader_clinical_coding_fhir.py
new file mode 100644
index 00000000..34570a6c
--- /dev/null
+++ b/cookbook/notereader_clinical_coding_fhir.py
@@ -0,0 +1,173 @@
+#!/usr/bin/env python3
+"""
+HealthChain Clinical Documentation Improvement Service
+=====================================================
+
+A complete CDI service that processes clinical notes and extracts billing codes.
+Demonstrates FHIR-native pipelines, legacy system integration, and multi-source data handling.
+
+Requirements:
+- pip install healthchain
+- pip install scispacy
+- pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.4/en_core_sci_sm-0.5.4.tar.gz
+- pip install python-dotenv
+
+Run:
+- python notereader_clinical_coding_fhir.py  # Demo and start server
+"""
+
+import os
+import uvicorn
+from datetime import datetime, timezone
+
+import healthchain as hc
+from fhir.resources.documentreference import DocumentReference
+from fhir.resources.meta import Meta
+from spacy.tokens import Span
+from dotenv import load_dotenv
+
+from healthchain.fhir import create_document_reference
+from healthchain.gateway.api import HealthChainAPI
+from healthchain.gateway.fhir import FHIRGateway
+from healthchain.gateway.soap import NoteReaderService
+from healthchain.io import CdaAdapter, Document
+from healthchain.models import CdaRequest
+from healthchain.pipeline.medicalcodingpipeline import MedicalCodingPipeline
+from healthchain.sandbox.use_cases import ClinicalDocumentation
+
+
+load_dotenv()
+
+
+BILLING_URL = (
+    f"fhir://api.medplum.com/fhir/R4/"
+    f"?client_id={os.environ.get('MEDPLUM_CLIENT_ID')}"
+    f"&client_secret={os.environ.get('MEDPLUM_CLIENT_SECRET')}"
+    f"&token_url={os.environ.get('MEDPLUM_TOKEN_URL', 'https://api.medplum.com/oauth2/token')}"
+    f"&scope={os.environ.get('MEDPLUM_SCOPE', 'openid')}"
+)
+
+
+def create_pipeline():
+    """Build FHIR-native ML pipeline with automatic problem extraction."""
+    pipeline = MedicalCodingPipeline.from_model_id("en_core_sci_sm", source="spacy")
+
+    # Add custom entity linking
+    @pipeline.add_node(position="after", reference="SpacyNLP")
+    def link_entities(doc: Document) -> Document:
+        """Add CUI codes to medical entities for problem extraction"""
+        if not Span.has_extension("cui"):
+            Span.set_extension("cui", default=None)
+
+        spacy_doc = doc.nlp.get_spacy_doc()
+
+        # Simple dummy linker for demo purposes
+        dummy_linker = {
+            "pneumonia": "233604007",
+            "type 2 diabetes mellitus": "44054006",
+            "congestive heart failure": "42343007",
+            "chronic kidney disease": "431855005",
+            "hypertension": "38341003",
+            "community acquired pneumonia": "385093006",
+            "ventilator associated pneumonia": "233717007",
+            "anaphylaxis": "39579001",
+            "delirium": "2776000",
+            "depression": "35489007",
+            "asthma": "195967001",
+            "copd": "13645005",
+        }
+
+        for ent in spacy_doc.ents:
+            if ent.text.lower() in dummy_linker:
+                ent._.cui = dummy_linker[ent.text.lower()]
+
+        return doc
+
+    return pipeline
+
+
+def create_app():
+    """Create production healthcare API."""
+    pipeline = create_pipeline()
+    cda_adapter = CdaAdapter()
+
+    # Modern FHIR sources
+    fhir_gateway = FHIRGateway()
+    fhir_gateway.add_source("billing", BILLING_URL)
+
+    # Legacy CDA processing
+    note_service = NoteReaderService()
+
+    @note_service.method("ProcessDocument")
+    def ai_coding_workflow(request: CdaRequest):
+        doc = cda_adapter.parse(request)
+        doc = pipeline(doc)
+
+        for condition in doc.fhir.problem_list:
+            # Add basic provenance tracking
+            condition.meta = Meta(
+                source="urn:healthchain:pipeline:cdi",
+                lastUpdated=datetime.now(timezone.utc).isoformat(),
+            )
+            fhir_gateway.create(condition, source="billing")
+
+        cda_response = cda_adapter.format(doc)
+
+        return cda_response
+
+    # Register services
+    app = HealthChainAPI(title="Epic CDI Service with FHIR integration")
+    app.register_gateway(fhir_gateway, path="/fhir")
+    app.register_service(note_service, path="/notereader")
+
+    return app
+
+
+def create_sandbox():
+    @hc.sandbox(base_url="http://localhost:8000/")
+    class NotereaderSandbox(ClinicalDocumentation):
+        """Sandbox for testing clinical documentation workflows"""
+
+        def __init__(self):
+            super().__init__()
+            self.data_path = "./resources/uclh_cda.xml"
+
+        @hc.ehr(workflow="sign-note-inpatient")
+        def load_clinical_document(self) -> DocumentReference:
+            """Load a sample CDA document for processing"""
+            with open(self.data_path, "r") as file:
+                xml_content = file.read()
+
+            return create_document_reference(
+                data=xml_content,
+                content_type="text/xml",
+                description="Sample CDA document from sandbox",
+            )
+
+    return NotereaderSandbox()
+
+
+# Create the app
+app = create_app()
+
+
+if __name__ == "__main__":
+    import threading
+    from time import sleep
+
+    # Start server
+    def run_server():
+        uvicorn.run(app, port=8000, log_level="warning")
+
+    server_thread = threading.Thread(target=run_server, daemon=True)
+    server_thread.start()
+    sleep(2)  # Wait for startup
+
+    # Test sandbox
+    sandbox = create_sandbox()
+    sandbox.start_sandbox()
+
+    try:
+        server_thread.join()
+    except KeyboardInterrupt:
+        pass
diff --git a/docs/assets/images/medplum_client.png b/docs/assets/images/medplum_client.png
new file mode 100644
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HcmV?d00001

diff --git a/docs/cookbook/cds_sandbox.md b/docs/cookbook/cds_sandbox.md
deleted file mode 100644
index 2cfe69ef..00000000
--- a/docs/cookbook/cds_sandbox.md
+++ /dev/null
@@ -1,203 +0,0 @@
-# Build a CDS sandbox for encounter discharge summarization
-
-This tutorial demonstrates how to build a clinical decision support (CDS) application that summarizes information in encounter discharge notes to streamline operational workflows using the `encounter-discharge` CDS hook workflow. We will use the `SummarizationPipeline` for the application and test it using the HealthChain sandbox server.
-
-Check out the full working example [here](https://github.com/dotimplement/HealthChain/tree/main/cookbook/cds_discharge_summarizer_hf_chat.py)!
-
-## Setup
-
-Make sure you have a [Hugging Face API token](https://huggingface.co/docs/hub/security-tokens) and set it as the `HUGGINGFACEHUB_API_TOKEN` environment variable.
-
-```python
-import getpass
-import os
-
-if not os.getenv("HUGGINGFACEHUB_API_TOKEN"):
-    os.environ["HUGGINGFACEHUB_API_TOKEN"] = getpass.getpass(
-      "Enter your token: "
-    )
-```
-
-If you are using a chat model, make sure you have the necessary `langchain` packages installed.
-
-```bash
-pip install langchain langchain-huggingface
-```
-
-## Initialize the pipeline
-
-First, we'll initialize our model and pipeline. You can choose between:
-
-- A transformer model fine-tuned for summarization (like `google/pegasus-xsum`)
-- An LLM chat model (like `zephyr-7b-beta`) with custom prompting
-
-If you are using a chat model, we recommend you initialize the pipeline with the [LangChain](https://python.langchain.com/docs/integrations/chat/huggingface/) wrapper to fully utilize the chat interface and prompting functionality.
-
-=== "Non-chat model"
-    ```python
-    from healthchain.pipeline import SummarizationPipeline
-
-    pipeline = SummarizationPipeline.from_model_id(
-      "google/pegasus-xsum", source="huggingface", task="summarization"
-      )
-    ```
-
-
-=== "Chat model"
-    ```python
-    from healthchain.pipeline import SummarizationPipeline
-
-    from langchain_huggingface.llms import HuggingFaceEndpoint
-    from langchain_huggingface import ChatHuggingFace
-    from langchain_core.prompts import PromptTemplate
-    from langchain_core.output_parsers import StrOutputParser
-
-    hf = HuggingFaceEndpoint(
-        repo_id="HuggingFaceH4/zephyr-7b-beta",
-        task="text-generation",
-        max_new_tokens=512,
-        do_sample=False,
-        repetition_penalty=1.03,
-    )
-
-    model = ChatHuggingFace(llm=hf)
-
-    template = """
-    Provide a concise, objective summary of the input text in
-    short bullet points separated by new lines, focusing on key
-    actions such as appointments and medication dispense instructions,
-    without using second or third person pronouns.\n'''{text}'''
-    """
-    prompt = PromptTemplate.from_template(template)
-
-    chain = prompt | model | StrOutputParser()
-
-    pipeline = SummarizationPipeline.load(chain, source="langchain")
-    ```
-
-Loading your model into `SummarizationPipeline` will automatically handle the data parsing and text extraction. Now it's ready to use with the sandbox!
-
-## Build the sandbox
-
-We'll deploy our `SummarizationPipeline` as a Clinical Decision Support sandbox. We can do this by creating a class that inherits from `ClinicalDecisionSupport` and decorating it with the `@hc.sandbox` decorator.
-
-We'll also need to implement the service method, which will process the request through our pipeline and return the result. We can define the service method with the `@hc.api` decorator.
-
-```python
-import healthchain as hc
-
-from healthchain.sandbox.use_cases import ClinicalDecisionSupport
-from healthchain.models import CDSRequest, CDSResponse
-
-@hc.sandbox
-class DischargeNoteSummarizer(ClinicalDecisionSupport):
-  def __init__(self):
-    self.pipeline = pipeline
-
-    @hc.api
-    def my_service(self, request: CDSRequest) -> CDSResponse:
-        result = self.pipeline.process_request(request)
-        return result
-```
-
-## Add a data generator
-
-Now that we have our service function defined, we'll need to generate some test data for the sandbox to send to our service. We can use the `CdsDataGenerator` to generate synthetic FHIR data for testing.
-
-By default, the generator generates a random single patient with structured FHIR resources. To pass in free-text discharge notes for our `SummarizationPipeline`, we can set the `free_text_path` and `column_name` parameters.
-
-```python
-from healthchain.data_generators import CdsDataGenerator
-
-data_generator = CdsDataGenerator()
-data = data_generator.generate(
-  free_text_path="data/discharge_notes.csv", column_name="text"
-)
-print(data.model_dump())
-# {
-#    "prefetch": {
-#        "entry": [
-#        {
-#            "resource": {
-#                "resourceType": "Bundle",
-#                ...
-#            }
-#        }
-#    ]
-# }
-```
-
-The data generator returns a `Prefetch` object, which ensures that the data is parsed correctly inside the sandbox.
-
-## Define client workflow
-
-To finish our sandbox, we'll define a client function that loads the data generator into the sandbox. We'll use the `@hc.ehr` decorator and pass in the CDS hook workflow that we want to use - in this case, `encounter-discharge`. This will automatically send the generated test data to our service method when a request is made, using the workflow format that we specified.
-
-```python
-import healthchain as hc
-
-from healthchain.sandbox.use_cases import ClinicalDecisionSupport
-from healthchain.models import CDSRequest, CDSResponse, Prefetch
-
-@hc.sandbox
-class DischargeNoteSummarizer(ClinicalDecisionSupport):
-  def __init__(self):
-    self.pipeline = pipeline
-    self.data_generator = data_generator
-
-  @hc.api
-  def my_service(self, request: CDSRequest) -> CDSResponse:
-    result = self.pipeline.process_request(request)
-    return result
-
-  @hc.ehr(workflow="encounter-discharge")
-  def load_data_in_client(self) -> Prefetch:
-    data = self.data_generator.generate_prefetch()
-    return data
-```
-
-## Run the sandbox
-
-Start the sandbox by running the `start_sandbox()` method on your class instance.
-
-```python
-summarizer = DischargeNoteSummarizer()
-summarizer.start_sandbox()
-```
-
-Then run the sandbox using the HealthChain CLI:
-
-```bash
-healthchain run discharge_summarizer.py
-```
-
-The sandbox will:
-
-- Start a FastAPI server with your service method mounted to CDS endpoints at `http://localhost:8000/`
-- Generate synthetic data and send it to your service method
-- Save the processed request and response to the `output/requests` and `output/responses` folders of your current working directory
-
-An example response containing CDS cards might look like this:
-
-```json
-{
-    "cards": [
-        {
-            "summary": "Action Item 1",
-            "indicator": "info",
-            "source": {
-                "label": "Card Generated by HealthChain"
-            },
-            "detail": "- Transport arranged for 11:00 HRs, requires bariatric ambulance and 2 crew members (confirmed)."
-        },
-        {
-            "summary": "Action Item 2",
-            "indicator": "info",
-            "source": {
-                "label": "Card Generated by HealthChain"
-            },
-            "detail": "- Medication reconciliation completed, discharge medications prepared (Apixaban 5mg, Baclofen 20mg MR, new anticoagulation card) for collection by daughter"
-        }
-    ]
-}
-```
diff --git a/docs/cookbook/clinical_coding.md b/docs/cookbook/clinical_coding.md
new file mode 100644
index 00000000..5cb2a7c8
--- /dev/null
+++ b/docs/cookbook/clinical_coding.md
@@ -0,0 +1,259 @@
+# Build a NoteReader Service with FHIR Integration
+
+This tutorial shows you how to build a NoteReader clinical coding service that connects legacy CDA systems with modern FHIR servers. We'll process clinical notes, extract billing codes, and handle both old and new healthcare data formats. We'll use [Epic NoteReader](https://www.leidos.com/products/epic-note-reader) as the legacy system and [Medplum](https://www.medplum.com/) as the FHIR server.
+
+Check out the full working example [here](https://github.com/dotimplement/HealthChain/tree/main/cookbook/notereader_clinical_coding_fhir.py)!
+
+## Setup
+
+We'll use [scispacy](https://allenai.github.io/scispacy/) for medical entity extraction in this example. Make sure to install the required dependencies:
+
+```bash
+pip install healthchain
+pip install scispacy python-dotenv
+pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.4/en_core_sci_sm-0.5.4.tar.gz
+```
+
+If you'd like to test the FHIR integration with Medplum, make sure you have the following environment variables set. To setup Medplum, register an account on [Medplum](https://www.medplum.com/docs/tutorials/register) and obtain your [Client Credentials](https://www.medplum.com/docs/auth/methods/client-credentials).
+
+![Medplum Client Application](../assets/images/medplum_client.png)
+
+```bash
+# .env file
+MEDPLUM_CLIENT_ID=your_client_id
+MEDPLUM_CLIENT_SECRET=your_client_secret
+```
+
+## Initialize the pipeline
+
+First, we'll create a [medical coding pipeline](../reference/pipeline/pipeline.md) with a custom entity linking node for extracting conditions from clinical text.
+
+The example below just uses a dummy mapping of medical concepts to [SNOMED CT](https://www.snomed.org/), but you can do more fancy stuff with it if you want.
+
+```python
+from healthchain.pipeline.medicalcodingpipeline import MedicalCodingPipeline
+from healthchain.io import Document
+from spacy.tokens import Span
+
+def create_pipeline():
+    """Build FHIR-native ML pipeline with automatic problem extraction."""
+    pipeline = MedicalCodingPipeline.from_model_id("en_core_sci_sm", source="spacy")
+
+    # Add custom entity linking
+    @pipeline.add_node(position="after", reference="SpacyNLP")
+    def link_entities(doc: Document) -> Document:
+        """Add CUI codes to medical entities for problem extraction"""
+        if not Span.has_extension("cui"):
+            Span.set_extension("cui", default=None)
+
+        spacy_doc = doc.nlp.get_spacy_doc()
+
+        # Dummy medical concept mapping to SNOMED CT codes
+        medical_concepts = {
+            "pneumonia": "233604007",
+            "type 2 diabetes mellitus": "44054006",
+            "congestive heart failure": "42343007",
+            "chronic kidney disease": "431855005",
+            "hypertension": "38341003",
+            # Add more mappings as needed
+        }
+
+        for ent in spacy_doc.ents:
+            if ent.text.lower() in medical_concepts:
+                ent._.cui = medical_concepts[ent.text.lower()]
+
+        return doc
+
+    return pipeline
+```
+
+The `MedicalCodingPipeline` automatically:
+
+- Extracts medical entities using the `scispacy` model
+- Converts entities to FHIR [Condition](https://www.hl7.org/fhir/condition.html) resources
+- Populates the document's `problem_list` for downstream processing
+
+## Add the CDA Adapter
+
+The [CdaAdapter](../reference/pipeline/adapters/cdaadapter.md) converts CDA documents to HealthChain's [Document](../reference/pipeline/data_container.md) format using an instance of the [InteropEngine](../reference/interop/engine.md). This lets you work with legacy clinical documents without having to leave FHIR.
+
+```python
+from healthchain.io import CdaAdapter
+from healthchain.engine import create_interop
+
+# Create an interop engine with default configuration
+interop_engine = create_interop()
+cda_adapter = CdaAdapter(engine=interop_engine)
+
+# Parse the CDA document to a Document object
+doc = cda_adapter.parse(request)
+
+# Access the FHIR resources in the Document object
+doc.fhir.problem_list
+
+# Format the Document object back to a CDA document
+response = cda_adapter.format(doc)
+```
+
+What it does:
+
+- Parses CDA XML documents
+- Extracts clinical text and coded data from the CDA document
+- Stores the CDA XML as a [DocumentReference](https://www.hl7.org/fhir/documentreference.html) object in the `fhir` attribute of the `Document` object
+- Stores the extracted problems from the CDA document in the `problem_list` attribute of the `Document` object
+
+## Set up FHIR Gateway
+
+[FHIR gateways](../reference/gateway/fhir_gateway.md) connect to external FHIR servers. You can add multiple FHIR sources to the gateway via connection strings with the `add_source` method. The gateway will handle authentication and connections for you.
+
+```python
+import os
+from healthchain.gateway.fhir import FHIRGateway
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# Configure FHIR connection with OAuth2 authentication
+BILLING_URL = (
+    f"fhir://api.medplum.com/fhir/R4/"
+    f"?client_id={os.environ.get('MEDPLUM_CLIENT_ID')}"
+    f"&client_secret={os.environ.get('MEDPLUM_CLIENT_SECRET')}"
+    f"&token_url=https://api.medplum.com/oauth2/token"
+    f"&scope=openid"
+)
+
+# Initialize FHIR gateway and register external systems
+fhir_gateway = FHIRGateway()
+fhir_gateway.add_source("billing", BILLING_URL)
+
+# You can add multiple FHIR sources:
+# fhir_gateway.add_source("ehr", "fhir://epic.example.com/fhir/R4/")
+# fhir_gateway.add_source("registry", "fhir://registry.example.com/fhir/R4/")
+```
+
+## Set up the NoteReader Service
+
+Now let's set up the [NoteReader Service](../reference/gateway/soap_cda.md). This is an Epic specific module that allows third party services to interact with Epic's CDI service via CDA. It's somewhat niche but it be like that sometimes.
+
+The good thing about NoteReader is that it's already integrated in existing EHR workflows. The bad thing is it's legacy stuff and relatively rigid.
+
+We can make it more exciting by routing the extracted conditions to a FHIR server using the FHIR gateway inside the NoteReader service method, so you can do other cool stuff to it.
+
+```python
+from healthchain.gateway.soap import NoteReaderService
+
+# Create the NoteReader service
+note_service = NoteReaderService()
+
+@note_service.method("ProcessDocument")
+def ai_coding_workflow(request: CdaRequest):
+    # Parse CDA document from legacy system
+    doc = cda_adapter.parse(request)
+
+    # Process through ML pipeline to extract medical entities
+    doc = pipeline(doc)
+
+    # Access the extracted FHIR resources
+    for condition in doc.fhir.problem_list:
+        # Add metadata for audit and provenance tracking
+        condition.meta = Meta(
+            source="urn:healthchain:pipeline:cdi",
+            lastUpdated=datetime.now(timezone.utc).isoformat(),
+        )
+        # Send to external FHIR server via gateway
+        fhir_gateway.create(condition, source="billing")
+
+    # Return processed CDA response to the legacy system
+    cda_response = cda_adapter.format(doc)
+
+    return cda_response
+```
+
+## Build the service
+
+Time to put it all together! Using [HealthChainAPI](../reference/gateway/api.md), we can create a service with both FHIR and NoteReader endpoints.
+
+```python
+from healthchain.gateway.api import HealthChainAPI
+
+# Register services with the API gateway
+app = HealthChainAPI(title="Healthcare Integration Gateway")
+
+app.register_gateway(fhir_gateway, path="/fhir")
+app.register_service(note_service, path="/notereader")
+```
+
+## Test with sample documents
+
+You can test the service with sample clinical documents using the [sandbox utility](../reference/utilities/sandbox.md) so you don't have to go out there and source a real EHR for our neat little demo. Woohoo.
+
+```python
+import healthchain as hc
+from healthchain.sandbox.use_cases import ClinicalDocumentation
+from healthchain.fhir import create_document_reference
+from fhir.resources.documentreference import DocumentReference
+
+def create_sandbox():
+    @hc.sandbox(base_url="http://localhost:8000/")
+    class NotereaderSandbox(ClinicalDocumentation):
+        """Sandbox for testing clinical documentation workflows"""
+        def __init__(self):
+            super().__init__()
+            self.data_path = "./resources/uclh_cda.xml"
+
+        @hc.ehr(workflow="sign-note-inpatient")
+        def load_clinical_document(self) -> DocumentReference:
+            """Load a sample CDA document for processing"""
+            with open(self.data_path, "r") as file:
+                xml_content = file.read()
+
+            return create_document_reference(
+                data=xml_content,
+                content_type="text/xml",
+                description="Sample CDA document from sandbox",
+            )
+
+    return NotereaderSandbox()
+```
+
+## Run the complete example
+
+Run `HealthChainAPI` with `uvicorn` and start the sandbox to test the service.
+
+```python
+import uvicorn
+
+uvicorn.run(app)
+
+sandbox = create_sandbox()
+sandbox.start_sandbox()
+```
+
+## What happens when you run this
+
+Here's the workflow:
+
+=== "1. Server Startup"
+    - **URL:** `http://localhost:8000/`
+    - **Endpoints:**
+        - FHIR API: `/fhir/*`
+        - SOAP API: `/notereader`
+        - Documentation: `/docs`
+
+=== "2. Sandbox"
+    - Loads a sample CDA document from the `resources/` directory.
+    - Sends a SOAP request to the notereader service at `http://localhost:8000/notereader/ProcessDocument`.
+    - Receives the processed CDA document in response.
+    - Saves both the original and processed CDA documents to the `output/` directory.
+
+=== "3. Pipeline"
+    - Converts the CDA document to FHIR resources.
+    - Processes the text data from the CDA document using the pipeline.
+    - Creates FHIR `Condition` resources from extracted conditions.
+    - Converts the new FHIR resources back into an updated CDA document.
+
+=== "4. Output"
+    - Stores the `Condition` resources in the Medplum FHIR server.
+    - Returns the processed CDA document to the sandbox/EHR workflow.
+
+That's it! You can now test the service with sample documents and see the FHIR resources being created in Medplum. 🎉
diff --git a/docs/cookbook/discharge_summarizer.md b/docs/cookbook/discharge_summarizer.md
new file mode 100644
index 00000000..e6125f52
--- /dev/null
+++ b/docs/cookbook/discharge_summarizer.md
@@ -0,0 +1,226 @@
+# Build a CDS Hooks Service for Discharge Summarization
+
+This tutorial shows you how to build a CDS service that integrates with EHR systems. We'll automatically summarize discharge notes and return actionable recommendations using the [CDS Hooks standard](https://cds-hooks.org/).
+
+Check out the full working example [here](https://github.com/dotimplement/HealthChain/tree/main/cookbook/cds_discharge_summarizer_hf_chat.py)!
+
+## Setup
+
+Make sure you have a [Hugging Face API token](https://huggingface.co/docs/hub/security-tokens) and set it as the `HUGGINGFACEHUB_API_TOKEN` environment variable.
+
+```python
+import getpass
+import os
+
+if not os.getenv("HUGGINGFACEHUB_API_TOKEN"):
+    os.environ["HUGGINGFACEHUB_API_TOKEN"] = getpass.getpass(
+      "Enter your token: "
+    )
+```
+
+If you are using a chat model, make sure you have the necessary `langchain` packages installed.
+
+```bash
+pip install langchain langchain-huggingface
+```
+
+## Initialize the pipeline
+
+First, we'll create a [summarization pipeline](../reference/pipeline/pipeline.md) with domain-specific prompting for discharge workflows. You can choose between:
+
+- **Transformer models** fine-tuned for clinical summarization (like `google/pegasus-xsum`)
+- **Large Language Models** with custom clinical prompting (like `zephyr-7b-beta`)
+
+For LLM approaches, we'll use [LangChain](https://python.langchain.com/docs/integrations/chat/huggingface/) for better prompting.
+
+=== "Non-chat model"
+    ```python
+    from healthchain.pipeline import SummarizationPipeline
+
+    pipeline = SummarizationPipeline.from_model_id(
+      "google/pegasus-xsum", source="huggingface", task="summarization"
+      )
+    ```
+
+
+=== "Chat model"
+    ```python
+    from healthchain.pipeline import SummarizationPipeline
+
+    from langchain_huggingface.llms import HuggingFaceEndpoint
+    from langchain_huggingface import ChatHuggingFace
+    from langchain_core.prompts import PromptTemplate
+    from langchain_core.output_parsers import StrOutputParser
+
+    hf = HuggingFaceEndpoint(
+        repo_id="HuggingFaceH4/zephyr-7b-beta",
+        task="text-generation",
+        max_new_tokens=512,
+        do_sample=False,
+        repetition_penalty=1.03,
+    )
+
+    model = ChatHuggingFace(llm=hf)
+
+    template = """
+    You are a discharge planning assistant for hospital operations.
+    Provide a concise, objective summary focusing on actionable items
+    for care coordination, including appointments, medications, and
+    follow-up instructions. Format as bullet points.\n'''{text}'''
+    """
+    prompt = PromptTemplate.from_template(template)
+
+    chain = prompt | model | StrOutputParser()
+
+    pipeline = SummarizationPipeline.load(chain, source="langchain")
+    ```
+
+The `SummarizationPipeline` automatically:
+
+- Parses FHIR resources from CDS Hooks requests
+- Extracts clinical text from discharge documents
+- Formats outputs as CDS cards according to the CDS Hooks specification
+
+## Add the CDS FHIR Adapter
+
+The [CdsFhirAdapter](../reference/pipeline/adapters/cdsfhiradapter.md) converts between CDS Hooks requests and HealthChain's [Document](../reference/pipeline/data_container.md) format. This makes it easy to work with FHIR data in CDS workflows.
+
+```python
+from healthchain.io import CdsFhirAdapter
+
+cds_adapter = CdsFhirAdapter()
+
+# Parse the CDS request to a Document object
+cds_adapter.parse(request)
+
+# Format the Document object back to a CDS response
+cds_adapter.format(doc)
+```
+
+What it does:
+
+- Parses FHIR resources from CDS requests
+- Extracts text from [DocumentReference](https://www.hl7.org/fhir/documentreference.html) resources
+- Formats responses as CDS cards
+
+## Set up the CDS service
+
+Now let's create the CDS service. [HealthChainAPI](../reference/gateway/api.md) gives you discovery endpoints, validation, and docs automatically.
+
+```python
+from healthchain.gateway import HealthChainAPI, CDSHooksService
+from healthchain.models import CDSRequest, CDSResponse
+from healthchain.io import CdsFhirAdapter
+
+def create_pipeline():
+    """Build the discharge summarization pipeline"""
+    # Configure your pipeline (using previous examples)
+    return pipeline
+
+def create_app():
+    """Create the CDS Hooks application"""
+    pipeline = create_pipeline()
+    adapter = CdsFhirAdapter()
+
+    # Initialize the CDS service
+    cds_service = CDSHooksService()
+
+    # Define the CDS service function
+    @cds_service.hook("encounter-discharge", id="discharge-summary")
+    def handle_discharge_summary(request: CDSRequest) -> CDSResponse:
+        """Process discharge summaries with AI"""
+        # Parse CDS request to internal Document format
+        doc = adapter.parse(request)
+
+        # Process through AI pipeline
+        processed_doc = pipeline(doc)
+
+        # Format response with CDS cards
+        response = adapter.format(processed_doc)
+        return response
+
+    # Register the service with the API gateway
+    app = HealthChainAPI(title="Discharge Summary CDS Service")
+    app.register_service(cds_service)
+
+    return app
+```
+
+
+## Test with sample clinical data
+
+Let's test the service with some sample discharge notes using the [sandbox utility](../reference/utilities/sandbox.md) and the [CdsDataGenerator](../reference/utilities/data_generator.md):
+
+```python
+from healthchain.data_generators import CdsDataGenerator
+
+data_generator = CdsDataGenerator()
+data = data_generator.generate(
+  free_text_path="data/discharge_notes.csv", column_name="text"
+)
+print(data.model_dump())
+# {
+#    "prefetch": {
+#        "entry": [
+#        {
+#            "resource": {
+#                "resourceType": "Bundle",
+#                ...
+#            }
+#        }
+#    ]
+# }
+```
+
+The data generator returns a `Prefetch` object, which ensures that the data is parsed correctly by the CDS service.
+
+## Run the complete example
+
+Run the service with `uvicorn`:
+
+```python
+import uvicorn
+
+app = create_app()
+
+uvicorn.run(app)
+```
+
+## What happens when you run this
+
+## Workflow Overview
+
+=== "1. Service Startup"
+    - **URL:** [http://localhost:8000/](http://localhost:8000/)
+    - **Service discovery:** `/cds-services`
+    - **CDS endpoint:** `/cds-services/discharge-summary`
+    - **API docs:** `/docs`
+
+=== "2. Request Processing"
+    - Receives CDS Hooks requests from EHR systems
+    - Summarizes discharge notes using AI
+    - Returns CDS cards with clinical recommendations
+
+=== "3. Example CDS Response"
+    ```json
+    {
+      "cards": [
+        {
+          "summary": "Discharge Transportation",
+          "indicator": "info",
+          "source": {
+            "label": "HealthChain Discharge Assistant"
+          },
+          "detail": "• Transport arranged for 11:00 HRs\n• Requires bariatric ambulance and 2 crew members\n• Confirmation number: TR-2024-001"
+        },
+        {
+          "summary": "Medication Management",
+          "indicator": "warning",
+          "source": {
+            "label": "HealthChain Discharge Assistant"
+          },
+          "detail": "• Discharge medications: Apixaban 5mg, Baclofen 20mg MR\n• New anticoagulation card prepared\n• Collection by daughter scheduled"
+        }
+      ]
+    }
+    ```
diff --git a/docs/cookbook/index.md b/docs/cookbook/index.md
index 94811ffb..ed71d616 100644
--- a/docs/cookbook/index.md
+++ b/docs/cookbook/index.md
@@ -2,5 +2,5 @@
 
 The best way to learn is by example! Here are some to get you started:
 
-- [Build a CDS sandbox](./cds_sandbox.md): Build a clinical decision support (CDS) system that uses *encounter-discharge* to summarize discharge notes.
-- [Build a Clinical Documentation sandbox](./notereader_sandbox.md): Build a NoteReader system which extracts problem, medication, and allergy concepts from free-text clinical notes.
+- [Summarize Discharge Notes with CDS Hooks](./discharge_summarizer.md): Implement a CDS Hooks service that listens for `encounter-discharge` events, automatically generates concise summaries of discharge notes, and delivers clinical recommendations directly into EHR workflows.
+- [Automate Clinical Coding and FHIR Integration](./clinical_coding.md): Build a system that extracts medical conditions from clinical documentation, maps them to SNOMED CT codes, and synchronizes structured Condition resources with external FHIR servers (Medplum) for billing and analytics.
diff --git a/docs/cookbook/notereader_sandbox.md b/docs/cookbook/notereader_sandbox.md
deleted file mode 100644
index 1e6cdcc8..00000000
--- a/docs/cookbook/notereader_sandbox.md
+++ /dev/null
@@ -1,75 +0,0 @@
-# NoteReader Sandbox
-
-A sandbox example of NoteReader clinical documentation improvement which extracts problems, medications, and allergies entries from the progress note section of a pre-configured CDA document using [scispacy](https://github.com/allenai/scispacy) with a custom entity linker component.
-
-Full example coming soon!
-
-```python
-import healthchain as hc
-
-from healthchain.io import Document
-from healthchain.models.requests import CdaRequest
-from healthchain.models.responses import CdaResponse
-from healthchain.pipeline import MedicalCodingPipeline
-from healthchain.sandbox.use_cases import ClinicalDocumentation
-from healthchain.fhir import create_document_reference
-
-from spacy.tokens import Span
-
-from fhir.resources.documentreference import DocumentReference
-
-pipeline = MedicalCodingPipeline.from_model_id("en_core_sci_sm", source="spacy")
-
-@pipeline.add_node(position="after", reference="SpacyNLP")
-def link_entities(doc: Document) -> Document:
-    # Register the extension if it doesn't exist already
-    if not Span.has_extension("cui"):
-        Span.set_extension("cui", default=None)
-    spacy_doc = doc.nlp.get_spacy_doc()
-
-    dummy_linker = {"fever": "C0006477",
-                    "cough": "C0006477",
-                    "cold": "C0006477",
-                    "flu": "C0006477",
-                    "headache": "C0006477",
-                    "sore throat": "C0006477",
-                    }
-
-    for ent in spacy_doc.ents:
-        if ent.text in dummy_linker:
-            ent._.cui = dummy_linker[ent.text]
-
-    doc.update_problem_list_from_nlp()
-
-    return doc
-
-
-@hc.sandbox
-class NotereaderSandbox(ClinicalDocumentation):
-    def __init__(self):
-        self.pipeline = pipeline
-
-    @hc.ehr(workflow="sign-note-inpatient")
-    def load_data_in_client(self) -> DocumentReference:
-        with open("./resources/uclh_cda.xml", "r") as file:
-            xml_string = file.read()
-
-        cda_document_reference = create_document_reference(
-            data=xml_string,
-            content_type="text/xml",
-            description="Original CDA Document loaded from my sandbox",
-        )
-
-        return cda_document_reference
-
-    @hc.api
-    def my_service(self, request: CdaRequest) -> CdaResponse:
-        result = self.pipeline.process_request(request)
-
-        return result
-
-
-if __name__ == "__main__":
-    clindoc = NotereaderSandbox()
-    clindoc.start_sandbox()
-```
diff --git a/docs/reference/pipeline/prebuilt_pipelines/summarization.md b/docs/reference/pipeline/prebuilt_pipelines/summarization.md
index 0182cc91..ebba8ec3 100644
--- a/docs/reference/pipeline/prebuilt_pipelines/summarization.md
+++ b/docs/reference/pipeline/prebuilt_pipelines/summarization.md
@@ -1,3 +1,3 @@
 # SummarizationPipeline
 
-Full documentation coming soon! Check out this [cookbook example](../../../cookbook/cds_sandbox.md) for now.
+Full documentation coming soon! Check out this [cookbook example](../../../cookbook/discharge_summarizer.md) for now.
diff --git a/mkdocs.yml b/mkdocs.yml
index 90c13394..2002192e 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -14,8 +14,8 @@ nav:
       - Licence: distribution.md
   - Cookbook:
       - cookbook/index.md
-      - CDS Sandbox: cookbook/cds_sandbox.md
-      - NoteReader Sandbox: cookbook/notereader_sandbox.md
+      - Discharge Summarizer: cookbook/discharge_summarizer.md
+      - Clinical Coding: cookbook/clinical_coding.md
   - Docs:
       - Welcome: reference/index.md
       - Gateway:

From 32c54c47b9b38cf1a6991f52680964706f0e3999 Mon Sep 17 00:00:00 2001
From: jenniferjiangkells <jenniferjiangkells@gmail.com>
Date: Wed, 13 Aug 2025 18:27:25 +0100
Subject: [PATCH 2/4] Update cookbooks

---
 README.md                                    |  2 +-
 cookbook/cds_discharge_summarizer_hf_chat.py | 70 ++++++++++++++------
 cookbook/cds_discharge_summarizer_hf_trf.py  | 49 +++++++++++---
 cookbook/notereader_clinical_coding_fhir.py  |  5 +-
 docs/cookbook/clinical_coding.md             | 31 ++++++---
 docs/cookbook/discharge_summarizer.md        |  2 +-
 6 files changed, 113 insertions(+), 46 deletions(-)

diff --git a/README.md b/README.md
index 0cc76743..f4b8bfd8 100644
--- a/README.md
+++ b/README.md
@@ -34,7 +34,7 @@ First time here? Check out our [Docs](https://dotimplement.github.io/HealthChain
 ## Why use HealthChain?
 -  **EHR integrations are manual and time-consuming** - **HealthChainAPI** abstracts away complexities so you can focus on AI development, not learning FHIR APIs, CDS Hooks, and authentication schemes.
 -  **Healthcare data is fragmented and complex** - **InteropEngine** handles the conversion between FHIR, CDA, and HL7v2 so you don't have to become an expert in healthcare data standards.
--  [**Most healthcare data is unstructured**](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6372467/) - HealthChain **Pipelines** are optimized for real-time AI and NLP applications that deal with realistic healthcare data.
+-  [**Most healthcare data is unstructured**](https://pmc.ncbi.nlm.nih.gov/articles/PMC10566734/) - HealthChain **Pipelines** are optimized for real-time AI and NLP applications that deal with realistic healthcare data.
 - **Built by health tech developers, for health tech developers** - HealthChain is tech stack agnostic, modular, and easily extensible with built-in compliance and audit features.
 
 ## HealthChainAPI
diff --git a/cookbook/cds_discharge_summarizer_hf_chat.py b/cookbook/cds_discharge_summarizer_hf_chat.py
index 259a1341..72df60d3 100644
--- a/cookbook/cds_discharge_summarizer_hf_chat.py
+++ b/cookbook/cds_discharge_summarizer_hf_chat.py
@@ -1,13 +1,12 @@
 import healthchain as hc
-
+from healthchain.gateway import HealthChainAPI, CDSHooksService
 from healthchain.pipeline import SummarizationPipeline
-from healthchain.models import CDSRequest, CDSResponse, Prefetch
-from healthchain.data_generators import CdsDataGenerator
 from healthchain.sandbox.use_cases import ClinicalDecisionSupport
+from healthchain.models import Prefetch, CDSRequest, CDSResponse
+from healthchain.data_generators import CdsDataGenerator
 
 from langchain_huggingface.llms import HuggingFaceEndpoint
 from langchain_huggingface import ChatHuggingFace
-
 from langchain_core.prompts import PromptTemplate
 from langchain_core.output_parsers import StrOutputParser
 
@@ -21,47 +20,76 @@
 
 def create_summarization_chain():
     hf = HuggingFaceEndpoint(
-        repo_id="HuggingFaceH4/zephyr-7b-beta",
+        repo_id="deepseek-ai/DeepSeek-R1-0528",
         task="text-generation",
         max_new_tokens=512,
         do_sample=False,
         repetition_penalty=1.03,
     )
+
     model = ChatHuggingFace(llm=hf)
+
     template = """
-    You are a bed planner for a hospital. Provide a concise, objective summary of the input text in short bullet points separated by new lines,
-    focusing on key actions such as appointments and medication dispense instructions, without using second or third person pronouns.\n'''{text}'''
+    You are a discharge planning assistant for hospital operations.
+    Provide a concise, objective summary focusing on actionable items
+    for care coordination, including appointments, medications, and
+    follow-up instructions. Format as bullet points with no preamble.\n'''{text}'''
     """
     prompt = PromptTemplate.from_template(template)
+
     return prompt | model | StrOutputParser()
 
 
-@hc.sandbox
+# Create the healthcare application
+app = HealthChainAPI(
+    title="Discharge Note Summarizer",
+    description="AI-powered discharge note summarization service",
+)
+
+chain = create_summarization_chain()
+pipeline = SummarizationPipeline.load(
+    chain, source="langchain", template_path="templates/cds_card_template.json"
+)
+
+# Create CDS Hooks service
+cds = CDSHooksService()
+
+
+@cds.hook("encounter-discharge", id="discharge-summarizer")
+def discharge_summarizer(request: CDSRequest) -> CDSResponse:
+    result = pipeline.process_request(request)
+    return result
+
+
+# Register the CDS service
+app.register_service(cds, path="/cds")
+
+
+@hc.sandbox(api="http://localhost:8000")
 class DischargeNoteSummarizer(ClinicalDecisionSupport):
     def __init__(self):
-        # Initialize pipeline and data generator
-        chain = create_summarization_chain()
-        self.pipeline = SummarizationPipeline.load(
-            chain, source="langchain", template_path="templates/cds_card_template.json"
-        )
+        super().__init__(path="/cds/cds-services/discharge-summarizer")
         self.data_generator = CdsDataGenerator()
 
     @hc.ehr(workflow="encounter-discharge")
     def load_data_in_client(self) -> Prefetch:
-        # Generate synthetic FHIR data for testing
         data = self.data_generator.generate_prefetch(
             free_text_path="data/discharge_notes.csv", column_name="text"
         )
         return data
 
-    @hc.api
-    def my_service(self, request: CDSRequest) -> CDSResponse:
-        # Process the request through our pipeline
-        result = self.pipeline.process_request(request)
-        return result
-
 
 if __name__ == "__main__":
-    # Start the sandbox server
+    import uvicorn
+    import threading
+
+    # Start the API server in a separate thread
+    def start_api():
+        uvicorn.run(app, port=8000)
+
+    api_thread = threading.Thread(target=start_api, daemon=True)
+    api_thread.start()
+
+    # Start the sandbox
     summarizer = DischargeNoteSummarizer()
     summarizer.start_sandbox()
diff --git a/cookbook/cds_discharge_summarizer_hf_trf.py b/cookbook/cds_discharge_summarizer_hf_trf.py
index 36ee119f..e65e9916 100644
--- a/cookbook/cds_discharge_summarizer_hf_trf.py
+++ b/cookbook/cds_discharge_summarizer_hf_trf.py
@@ -1,5 +1,5 @@
 import healthchain as hc
-
+from healthchain.gateway import HealthChainAPI, CDSHooksService
 from healthchain.pipeline import SummarizationPipeline
 from healthchain.sandbox.use_cases import ClinicalDecisionSupport
 from healthchain.models import Prefetch, CDSRequest, CDSResponse
@@ -13,12 +13,35 @@
     os.environ["HUGGINGFACEHUB_API_TOKEN"] = getpass.getpass("Enter your token: ")
 
 
-@hc.sandbox
+# Create the healthcare application
+app = HealthChainAPI(
+    title="Discharge Note Summarizer",
+    description="AI-powered discharge note summarization service",
+)
+
+# Initialize pipeline
+pipeline = SummarizationPipeline.from_model_id(
+    "google/pegasus-xsum", source="huggingface", task="summarization"
+)
+
+# Create CDS Hooks service
+cds = CDSHooksService()
+
+
+@cds.hook("encounter-discharge", id="discharge-summarizer")
+def discharge_summarizer(request: CDSRequest) -> CDSResponse:
+    result = pipeline.process_request(request)
+    return result
+
+
+# Register the CDS service
+app.register_service(cds, path="/cds")
+
+
+@hc.sandbox(api="http://localhost:8000")
 class DischargeNoteSummarizer(ClinicalDecisionSupport):
     def __init__(self):
-        self.pipeline = SummarizationPipeline.from_model_id(
-            "google/pegasus-xsum", source="huggingface", task="summarization"
-        )
+        super().__init__(path="/cds/cds-services/discharge-summarizer")
         self.data_generator = CdsDataGenerator()
 
     @hc.ehr(workflow="encounter-discharge")
@@ -28,12 +51,18 @@ def load_data_in_client(self) -> Prefetch:
         )
         return data
 
-    @hc.api
-    def my_service(self, request: CDSRequest) -> CDSResponse:
-        result = self.pipeline.process_request(request)
-        return result
-
 
 if __name__ == "__main__":
+    import uvicorn
+    import threading
+
+    # Start the API server in a separate thread
+    def start_api():
+        uvicorn.run(app, port=8000)
+
+    api_thread = threading.Thread(target=start_api, daemon=True)
+    api_thread.start()
+
+    # Start the sandbox
     summarizer = DischargeNoteSummarizer()
     summarizer.start_sandbox()
diff --git a/cookbook/notereader_clinical_coding_fhir.py b/cookbook/notereader_clinical_coding_fhir.py
index 34570a6c..45af7f0a 100644
--- a/cookbook/notereader_clinical_coding_fhir.py
+++ b/cookbook/notereader_clinical_coding_fhir.py
@@ -1,8 +1,5 @@
 #!/usr/bin/env python3
 """
-HealthChain Clinical Documentation Improvement Service
-=====================================================
-
 A complete CDI service that processes clinical notes and extracts billing codes.
 Demonstrates FHIR-native pipelines, legacy system integration, and multi-source data handling.
 
@@ -124,7 +121,7 @@ def ai_coding_workflow(request: CdaRequest):
 
 
 def create_sandbox():
-    @hc.sandbox(base_url="http://localhost:8000/")
+    @hc.sandbox(api="http://localhost:8000/")
     class NotereaderSandbox(ClinicalDocumentation):
         """Sandbox for testing clinical documentation workflows"""
 
diff --git a/docs/cookbook/clinical_coding.md b/docs/cookbook/clinical_coding.md
index 5cb2a7c8..36537a91 100644
--- a/docs/cookbook/clinical_coding.md
+++ b/docs/cookbook/clinical_coding.md
@@ -1,6 +1,6 @@
 # Build a NoteReader Service with FHIR Integration
 
-This tutorial shows you how to build a NoteReader clinical coding service that connects legacy CDA systems with modern FHIR servers. We'll process clinical notes, extract billing codes, and handle both old and new healthcare data formats. We'll use [Epic NoteReader](https://www.leidos.com/products/epic-note-reader) as the legacy system and [Medplum](https://www.medplum.com/) as the FHIR server.
+This tutorial shows you how to build a clinical coding service that connects legacy [CDA](https://hl7.org/cda/) systems with modern [FHIR servers](https://build.fhir.org/http.html). We'll process clinical notes, extract billing codes, and handle both old and new healthcare data formats. We'll use [Epic NoteReader](https://discovery.hgdata.com/product/epic-notereader-cdi) as the legacy system and [Medplum](https://www.medplum.com/) as the FHIR server.
 
 Check out the full working example [here](https://github.com/dotimplement/HealthChain/tree/main/cookbook/notereader_clinical_coding_fhir.py)!
 
@@ -9,8 +9,7 @@ Check out the full working example [here](https://github.com/dotimplement/Health
 We'll use [scispacy](https://allenai.github.io/scispacy/) for medical entity extraction in this example. Make sure to install the required dependencies:
 
 ```bash
-pip install healthchain
-pip install scispacy python-dotenv
+pip install healthchain scispacy python-dotenv
 pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.4/en_core_sci_sm-0.5.4.tar.gz
 ```
 
@@ -28,7 +27,7 @@ MEDPLUM_CLIENT_SECRET=your_client_secret
 
 First, we'll create a [medical coding pipeline](../reference/pipeline/pipeline.md) with a custom entity linking node for extracting conditions from clinical text.
 
-The example below just uses a dummy mapping of medical concepts to [SNOMED CT](https://www.snomed.org/), but you can do more fancy stuff with it if you want.
+The example below just uses a dictionary lookup of medical concepts to a [SNOMED CT](https://www.snomed.org/) code for demo purposes, but you can obviously do more fancy stuff with it if you want.
 
 ```python
 from healthchain.pipeline.medicalcodingpipeline import MedicalCodingPipeline
@@ -71,7 +70,20 @@ The `MedicalCodingPipeline` automatically:
 
 - Extracts medical entities using the `scispacy` model
 - Converts entities to FHIR [Condition](https://www.hl7.org/fhir/condition.html) resources
-- Populates the document's `problem_list` for downstream processing
+- Populates the Document's `fhir.problem_list` for downstream processing
+
+It is equivalent to building a pipeline with the following components:
+
+```python
+from healthchain.pipeline import Pipeline
+from healthchain.pipeline.components import SpacyNLP, FHIRProblemListExtractor
+from healthchain.io.containers import Document
+
+pipeline = Pipeline[Document]()
+
+pipeline.add_node(SpacyNLP.from_model_id("en_core_sci_sm"))
+pipeline.add_node(FHIRProblemListExtractor())
+```
 
 ## Add the CDA Adapter
 
@@ -99,8 +111,9 @@ What it does:
 
 - Parses CDA XML documents
 - Extracts clinical text and coded data from the CDA document
-- Stores the CDA XML as a [DocumentReference](https://www.hl7.org/fhir/documentreference.html) object in the `fhir` attribute of the `Document` object
-- Stores the extracted problems from the CDA document in the `problem_list` attribute of the `Document` object
+- Stores the text data in `doc.text`
+- Stores the CDA XML as a [DocumentReference](https://www.hl7.org/fhir/documentreference.html) resource in `doc.fhir.bundle`
+- Stores the extracted [Condition](https://www.hl7.org/fhir/condition.html) resources from the CDA document in `doc.fhir.problem_list`
 
 ## Set up FHIR Gateway
 
@@ -137,7 +150,7 @@ Now let's set up the [NoteReader Service](../reference/gateway/soap_cda.md). Thi
 
 The good thing about NoteReader is that it's already integrated in existing EHR workflows. The bad thing is it's legacy stuff and relatively rigid.
 
-We can make it more exciting by routing the extracted conditions to a FHIR server using the FHIR gateway inside the NoteReader service method, so you can do other cool stuff to it.
+We can make it more exciting by routing the extracted conditions to a FHIR server inside the NoteReader service method, so you can do other cool modern stuff to it.
 
 ```python
 from healthchain.gateway.soap import NoteReaderService
@@ -194,7 +207,7 @@ from healthchain.fhir import create_document_reference
 from fhir.resources.documentreference import DocumentReference
 
 def create_sandbox():
-    @hc.sandbox(base_url="http://localhost:8000/")
+    @hc.sandbox(api="http://localhost:8000/")
     class NotereaderSandbox(ClinicalDocumentation):
         """Sandbox for testing clinical documentation workflows"""
         def __init__(self):
diff --git a/docs/cookbook/discharge_summarizer.md b/docs/cookbook/discharge_summarizer.md
index e6125f52..22ab33b6 100644
--- a/docs/cookbook/discharge_summarizer.md
+++ b/docs/cookbook/discharge_summarizer.md
@@ -53,7 +53,7 @@ For LLM approaches, we'll use [LangChain](https://python.langchain.com/docs/inte
     from langchain_core.output_parsers import StrOutputParser
 
     hf = HuggingFaceEndpoint(
-        repo_id="HuggingFaceH4/zephyr-7b-beta",
+        repo_id="deepseek-ai/DeepSeek-R1-0528",
         task="text-generation",
         max_new_tokens=512,
         do_sample=False,

From 797abffc64852429e8c85ba2907fca6d73e66fb1 Mon Sep 17 00:00:00 2001
From: jenniferjiangkells <jenniferjiangkells@gmail.com>
Date: Wed, 13 Aug 2025 18:27:49 +0100
Subject: [PATCH 3/4] Minor docstring and error message fix

---
 healthchain/pipeline/medicalcodingpipeline.py | 28 +++++--------------
 healthchain/pipeline/summarizationpipeline.py |  7 +++--
 healthchain/sandbox/clients/ehr.py            |  9 ++++--
 healthchain/sandbox/environment.py            |  1 +
 4 files changed, 20 insertions(+), 25 deletions(-)

diff --git a/healthchain/pipeline/medicalcodingpipeline.py b/healthchain/pipeline/medicalcodingpipeline.py
index 0ea3758f..fc1aac5f 100644
--- a/healthchain/pipeline/medicalcodingpipeline.py
+++ b/healthchain/pipeline/medicalcodingpipeline.py
@@ -1,41 +1,27 @@
 from healthchain.pipeline.base import BasePipeline, ModelConfig
 from healthchain.pipeline.mixins import ModelRoutingMixin
-from healthchain.pipeline.components.fhirproblemextractor import (
-    FHIRProblemListExtractor,
-)
+from healthchain.pipeline.components import FHIRProblemListExtractor
 
 
 class MedicalCodingPipeline(BasePipeline, ModelRoutingMixin):
     """
     Pipeline for extracting and coding medical concepts from clinical documents using NLP models.
+    Only extracts conditions to problems list for now.
 
     Stages:
         1. NER+L: Extracts and links medical concepts from document text.
         2. Problem Extraction (last): Converts medical entities to FHIR problem list.
 
+    Attributes:
+        extract_problems: Whether to automatically extract FHIR problem list (default: True)
+        patient_ref: Patient reference for created conditions (default: "Patient/123")
+        code_attribute: Name of the spaCy extension attribute containing medical codes (default: "cui")
+
     Usage Examples:
-        # Basic usage - extracts CUI codes to SNOMED conditions
         >>> pipeline = MedicalCodingPipeline.from_model_id("en_core_sci_sm", source="spacy")
 
-        # Custom patient reference
-        >>> pipeline = MedicalCodingPipeline.from_model_id(
-        ...     "en_core_sci_sm", source="spacy", patient_ref="Patient/demo-123"
-        ... )
-
-        # Different code attribute (e.g., for models that output SNOMED IDs)
-        >>> pipeline = MedicalCodingPipeline.from_model_id(
-        ...     "en_core_sci_sm", source="spacy", code_attribute="snomed_id"
-        ... )
-
-        # Skip automatic problem extraction
-        >>> pipeline = MedicalCodingPipeline.from_model_id(
-        ...     "en_core_sci_sm", source="spacy", extract_problems=False
-        ... )
-
-        # With Hugging Face
         >>> pipeline = MedicalCodingPipeline.from_model_id("bert-base-uncased", task="ner")
 
-        # With LangChain
         >>> chain = ChatPromptTemplate.from_template("Extract medical codes: {text}") | ChatOpenAI()
         >>> pipeline = MedicalCodingPipeline.load(chain)
     """
diff --git a/healthchain/pipeline/summarizationpipeline.py b/healthchain/pipeline/summarizationpipeline.py
index 4aa3a7ea..f04b857f 100644
--- a/healthchain/pipeline/summarizationpipeline.py
+++ b/healthchain/pipeline/summarizationpipeline.py
@@ -12,11 +12,14 @@ class SummarizationPipeline(BasePipeline, ModelRoutingMixin):
         1. Summarization: Generates summaries from document text.
         2. Card Creation: Formats summaries into CDS Hooks cards.
 
+    Attributes:
+        output_template: Template for the output summary (default: "{text}")
+        output_template_path: Path to the output template file (default: None)
+        delimiter: Delimiter for the output summary (default: "\n")
+
     Usage Examples:
-        # With Hugging Face
         >>> pipeline = SummarizationPipeline.from_model_id("facebook/bart-large-cnn", source="huggingface")
 
-        # With LangChain
         >>> chain = ChatPromptTemplate.from_template("Summarize: {text}") | ChatOpenAI()
         >>> pipeline = SummarizationPipeline.load(chain)
     """
diff --git a/healthchain/sandbox/clients/ehr.py b/healthchain/sandbox/clients/ehr.py
index 30c2cfe9..4cadaf71 100644
--- a/healthchain/sandbox/clients/ehr.py
+++ b/healthchain/sandbox/clients/ehr.py
@@ -68,7 +68,7 @@ async def send_request(self, url: str) -> List[Dict]:
             Notes:
                 This method logs errors rather than raising them, to avoid interrupting the batch processing of requests.
         """
-        async with httpx.AsyncClient() as client:
+        async with httpx.AsyncClient(follow_redirects=True) as client:
             responses: List[Dict] = []
             timeout = httpx.Timeout(self.timeout, read=None)
             for request in self.request_data:
@@ -86,6 +86,7 @@ async def send_request(self, url: str) -> List[Dict]:
                         responses.append(response_model.model_dump_xml())
                     else:
                         # TODO: use model_dump_json() once Pydantic V2 timezone serialization issue is resolved
+                        log.debug(f"Making POST request to: {url}")
                         response = await client.post(
                             url=url,
                             json=request.model_dump(exclude_none=True),
@@ -100,8 +101,12 @@ async def send_request(self, url: str) -> List[Dict]:
                             # Fallback to raw response if parsing fails
                             responses.append(response_data)
                 except httpx.HTTPStatusError as exc:
+                    try:
+                        error_content = exc.response.json()
+                    except Exception:
+                        error_content = exc.response.text
                     log.error(
-                        f"Error response {exc.response.status_code} while requesting {exc.request.url!r}: {exc.response.json()}"
+                        f"Error response {exc.response.status_code} while requesting {exc.request.url!r}: {error_content}"
                     )
                     responses.append({})
                 except httpx.TimeoutException as exc:
diff --git a/healthchain/sandbox/environment.py b/healthchain/sandbox/environment.py
index 2a852eb3..09af3d5e 100644
--- a/healthchain/sandbox/environment.py
+++ b/healthchain/sandbox/environment.py
@@ -83,6 +83,7 @@ def start_sandbox(
         log.info(
             f"Sending {len(self._client.request_data)} requests generated by {self._client.__class__.__name__} to {endpoint}"
         )
+        log.debug(f"API: {self.api}, Path: {self.path}, Endpoint: {endpoint}")
 
         try:
             self.responses = asyncio.run(self._client.send_request(url=endpoint))

From 905937d66c9ac81a9085669bedd9e0871c3b3392 Mon Sep 17 00:00:00 2001
From: jenniferjiangkells <jenniferjiangkells@gmail.com>
Date: Wed, 13 Aug 2025 18:28:02 +0100
Subject: [PATCH 4/4] poetry.lock

---
 poetry.lock | 805 ++++++++++++++++++++++++++--------------------------
 1 file changed, 403 insertions(+), 402 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 3047e90a..031c5edd 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -13,13 +13,13 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.9.0"
-description = "High level compatibility layer for multiple asynchronous event loop implementations"
+version = "4.10.0"
+description = "High-level concurrency and networking framework on top of asyncio or Trio"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c"},
-    {file = "anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028"},
+    {file = "anyio-4.10.0-py3-none-any.whl", hash = "sha256:60e474ac86736bbfd6f210f7a61218939c318f43f9972497381f1c5e930ed3d1"},
+    {file = "anyio-4.10.0.tar.gz", hash = "sha256:3f3fae35c96039744587aa5b8371e7e8e603c0702999535961dd336026973ba6"},
 ]
 
 [package.dependencies]
@@ -29,8 +29,6 @@ sniffio = ">=1.1"
 typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
 
 [package.extras]
-doc = ["Sphinx (>=8.2,<9.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
-test = ["anyio[trio]", "blockbuster (>=1.5.23)", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
 trio = ["trio (>=0.26.1)"]
 
 [[package]]
@@ -145,13 +143,13 @@ files = [
 
 [[package]]
 name = "certifi"
-version = "2025.7.14"
+version = "2025.8.3"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "certifi-2025.7.14-py3-none-any.whl", hash = "sha256:6b31f564a415d79ee77df69d757bb49a5bb53bd9f756cbbe24394ffd6fc1f4b2"},
-    {file = "certifi-2025.7.14.tar.gz", hash = "sha256:8ea99dbdfaaf2ba2f9bac77b9249ef62ec5218e7c2b2e903378ed5fccf765995"},
+    {file = "certifi-2025.8.3-py3-none-any.whl", hash = "sha256:f6c12493cfb1b06ba2ff328595af9350c65d6644968e5d3a2ffd78699af217a5"},
+    {file = "certifi-2025.8.3.tar.gz", hash = "sha256:e564105f78ded564e3ae7c923924435e1daa7463faeab5bb932bc53ffae63407"},
 ]
 
 [[package]]
@@ -246,103 +244,90 @@ files = [
 
 [[package]]
 name = "charset-normalizer"
-version = "3.4.2"
+version = "3.4.3"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7"
 files = [
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-    {file = "charset_normalizer-3.4.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b2d318c11350e10662026ad0eb71bb51c7812fc8590825304ae0bdd4ac283acd"},
-    {file = "charset_normalizer-3.4.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9cbfacf36cb0ec2897ce0ebc5d08ca44213af24265bd56eca54bee7923c48fd6"},
-    {file = "charset_normalizer-3.4.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:18dd2e350387c87dabe711b86f83c9c78af772c748904d372ade190b5c7c9d4d"},
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 [[package]]
@@ -739,13 +724,13 @@ dev = ["flake8", "markdown", "twine", "wheel"]
 
 [[package]]
 name = "griffe"
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 python-versions = ">=3.9"
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 [package.dependencies]
@@ -810,13 +795,13 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
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@@ -894,13 +879,13 @@ files = [
 
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 [package.dependencies]
@@ -2420,90 +2405,103 @@ pyyaml = "*"
 
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 platformdirs = ">=3.9.1,<5"
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