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
new file mode 100644
index 00000000..45af7f0a
--- /dev/null
+++ b/cookbook/notereader_clinical_coding_fhir.py
@@ -0,0 +1,170 @@
+#!/usr/bin/env python3
+"""
+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(api="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
index 00000000..db9aa32c
Binary files /dev/null and b/docs/assets/images/medplum_client.png differ
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..36537a91
--- /dev/null
+++ b/docs/cookbook/clinical_coding.md
@@ -0,0 +1,272 @@
+# Build a NoteReader Service with FHIR Integration
+
+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)!
+
+## 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 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 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
+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 `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
+
+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 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
+
+[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 inside the NoteReader service method, so you can do other cool modern 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(api="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..22ab33b6
--- /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="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 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/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))
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:
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_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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 optional = false
 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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 [[package]]
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 [[package]]
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