Update cookbook docs

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

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()

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()

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