Merge branch 'main' into ehrclient_timeout

README.md

@@ -47,11 +47,11 @@ nlp_pipeline = Pipeline[Document]()
 
 # Add TextPreProcessor component
 preprocessor = TextPreProcessor(tokenizer="spacy")
-nlp_pipeline.add(preprocessor)
+nlp_pipeline.add_node(preprocessor)
 
 # Add Model component (assuming we have a pre-trained model)
 model = Model(model_path="path/to/pretrained/model")
-nlp_pipeline.add(model)
+nlp_pipeline.add_node(model)
 
 # Add TextPostProcessor component
 postprocessor = TextPostProcessor(
@@ -60,7 +60,7 @@ postprocessor = TextPostProcessor(
         "high blood pressure": "hypertension"
     }
 )
-nlp_pipeline.add(postprocessor)
+nlp_pipeline.add_node(postprocessor)
 
 # Build the pipeline
 nlp = nlp_pipeline.build()

docs/api/component.md

@@ -1,6 +1,6 @@
 # Component
 
-::: healthchain.pipeline.components.basecomponent
+::: healthchain.pipeline.components.base
 ::: healthchain.pipeline.components.preprocessors
-::: healthchain.pipeline.components.models
+::: healthchain.pipeline.components.model
 ::: healthchain.pipeline.components.postprocessors

docs/api/connectors.md

@@ -0,0 +1,5 @@
+# Connectors
+
+::: healthchain.io.base
+::: healthchain.io.cdaconnector
+::: healthchain.io.cdsfhirconnector

docs/api/pipeline.md

@@ -1,3 +1,3 @@
 # Pipeline
 
-::: healthchain.pipeline.basepipeline
+::: healthchain.pipeline.base

docs/cookbook/cds_sandbox.md

@@ -1,13 +1,13 @@
 # Build a CDS sandbox
 
-A CDS sandbox which uses `gpt-4o` to summarise patient information from synthetically generated FHIR resources received from the `patient-view` CDS hook.
+A CDS sandbox which uses `gpt-4o` to summarise patient information from synthetically generated FHIR resources received from the `patient-view` CDS hook. [NEEDS UPDATING]
 
 ```python
 import healthchain as hc
 
 from healthchain.use_cases import ClinicalDecisionSupport
 from healthchain.data_generators import CdsDataGenerator
-from healthchain.models import Card, CdsFhirData, CDSRequest
+from healthchain.models import CdsFhirData, CDSRequest, CDSResponse
 
 from langchain_openai import ChatOpenAI
 from langchain_core.prompts import PromptTemplate
@@ -37,12 +37,16 @@ class CdsSandbox(ClinicalDecisionSupport):
     return data
 
   @hc.api
-  def my_service(self, request: CDSRequest) -> List[Card]:
+  def my_service(self, request: CDSRequest) -> CDSResponse:
     result = self.chain.invoke(str(request.prefetch))
-    return Card(
-      summary="Patient summary",
-      indicator="info",
-      source={"label": "openai"},
-      detail=result,
+    return CDSResponse(
+      cards=[
+        Card(
+          summary="Patient summary",
+          indicator="info",
+          source={"label": "openai"},
+          detail=result,
+        )
+      ]
     )
 ```

docs/cookbook/notereader_sandbox.md

@@ -18,6 +18,7 @@ from healthchain.models import (
 class NotereaderSandbox(ClinicalDocumentation):
   def __init__(self):
       self.cda_path = "./resources/uclh_cda.xml"
+      self.pipeline = MedicalCodingPipeline.load("./resources/models/medcat_model.zip")
 
   @hc.ehr(workflow="sign-note-inpatient")
   def load_data_in_client(self) -> CcdData:
@@ -27,38 +28,7 @@ class NotereaderSandbox(ClinicalDocumentation):
       return CcdData(cda_xml=xml_string)
 
   @hc.api
-  def my_service(self, ccd_data: CcdData) -> CcdData:
-
-    # Apply extraction method from ccd_data.note
-
-    new_problem = ProblemConcept(
-      code="38341003",
-      code_system="2.16.840.1.113883.6.96",
-      code_system_name="SNOMED CT",
-      display_name="Hypertension",
-    )
-    new_allergy = AllergyConcept(
-      code="70618",
-      code_system="2.16.840.1.113883.6.96",
-      code_system_name="SNOMED CT",
-      display_name="Allergy to peanuts",
-    )
-    new_medication = MedicationConcept(
-      code="197361",
-      code_system="2.16.840.1.113883.6.88",
-      code_system_name="RxNorm",
-      display_name="Lisinopril 10 MG Oral Tablet",
-      dosage=Quantity(value=10, unit="mg"),
-      route=Concept(
-        code="26643006",
-        code_system="2.16.840.1.113883.6.96",
-        code_system_name="SNOMED CT",
-        display_name="Oral",
-      ),
-    )
-    ccd_data.problems = [new_problem]
-    ccd_data.allergies = [new_allergy]
-    ccd_data.medications = [new_medication]
-
-    return ccd_data
+  def my_service(self, request: CdaRequest) -> CdaResponse:
+    response = self.pipeline(request)
+    return response
 ```

docs/quickstart.md

@@ -6,36 +6,34 @@ After [installing HealthChain](installation.md), get up to speed quickly with th
 
 ### Pipeline 🛠️
 
-The `Pipeline` module in HealthChain provides a flexible way to build and manage processing pipelines for NLP and ML tasks that can easily interface with
-parsers and connectors to integrate with electronic health record (EHR) systems.
+HealthChain Pipelines provide a flexible way to build and manage processing pipelines for NLP and ML tasks that can easily integrate with electronic health record (EHR) systems.
 
 You can build pipelines with three different approaches:
 
 #### 1. Build Your Own Pipeline with Inline Functions
 
-This is the most flexible approach, ideal for quick experiments and prototyping. Initialize a pipeline type hinted with the container type you want to process, then add components to your pipeline with the `@add` decorator.
+This is the most flexible approach, ideal for quick experiments and prototyping. Initialize a pipeline type hinted with the container type you want to process, then add components to your pipeline with the `@add_node` decorator.
 
 Compile the pipeline with `.build()` to use it.
 
 ```python
 from healthchain.pipeline import Pipeline
-from healthchain.io.containers import Document
+from healthchain.io import Document
 
 nlp_pipeline = Pipeline[Document]()
 
-@nlp_pipeline.add
+@nlp_pipeline.add_node
 def tokenize(doc: Document) -> Document:
     doc.tokens = doc.text.split()
     return doc
 
-@nlp_pipeline.add
+@nlp_pipeline.add_node
 def pos_tag(doc: Document) -> Document:
-    # Dummy POS tagging
     doc.pos_tags = ["NOUN" if token[0].isupper() else "VERB" for token in doc.tokens]
     return doc
 
-# Build and use the pipeline
 nlp = nlp_pipeline.build()
+
 doc = Document("Patient has a fracture of the left femur.")
 doc = nlp(doc)
 
@@ -46,51 +44,74 @@ print(doc.pos_tags)
 # ['NOUN', 'VERB', 'VERB', 'VERB', 'VERB', 'VERB']
 ```
 
-#### 2. Build Your Own Pipeline with Components and Models
+#### 2. Build Your Own Pipeline with Components, Models, and Connectors
 
-Components are stateful - they're classes instead of functions. They can be useful for grouping related processing steps together, or wrapping specific models.
+Components are stateful - they're classes instead of functions. They can be useful for grouping related processing steps together, setting configurations, or wrapping specific model loading steps.
 
 HealthChain comes with a few pre-built components, but you can also easily add your own. You can find more details on the [Components](./reference/pipeline/component.md) and [Models](./reference/pipeline/models/models.md) documentation pages.
 
-Add components to your pipeline with the `.add()` method and compile with `.build()`.
+Add components to your pipeline with the `.add_node()` method and compile with `.build()`.
 
 ```python
 from healthchain.pipeline import Pipeline
-from healthchain.io.containers import Document
 from healthchain.pipeline.components import TextPreProcessor, Model, TextPostProcessor
+from healthchain.io import Document
 
 pipeline = Pipeline[Document]()
 
-pipeline.add(TextPreProcessor())
-pipeline.add(Model(model_path="path/to/model"))
-pipeline.add(TextPostProcessor())
+pipeline.add_node(TextPreProcessor())
+pipeline.add_node(Model(model_path="path/to/model"))
+pipeline.add_node(TextPostProcessor())
 
 pipe = pipeline.build()
+
 doc = Document("Patient presents with hypertension.")
-doc = pipe(doc)
+output = pipe(doc)
+```
+
+Let's go one step further! You can use [Connectors](./reference/pipeline/connectors/connectors.md) to work directly with [CDA](https://www.hl7.org.uk/standards/hl7-standards/cda-clinical-document-architecture/) and [FHIR](https://hl7.org/fhir/) data received from healthcare system APIs. Add Connectors to your pipeline with the `.add_input()` and `.add_output()` methods.
+
+```python
+from healthchain.pipeline import Pipeline
+from healthchain.pipeline.components import Model
+from healthchain.io import CdaConnector
+from healthchain.models import CdaRequest
+
+pipeline = Pipeline()
+cda_connector = CdaConnector()
+
+pipeline.add_input(cda_connector)
+pipeline.add_node(Model(model_path="path/to/model"))
+pipeline.add_output(cda_connector)
+
+pipe = pipeline.build()
+
+cda_data = CdaRequest(document="<CDA XML content>")
+output = pipe(cda_data)
 ```
 
 #### 3. Use Prebuilt Pipelines
 
-Prebuilt pipelines are pre-configured collections of `Components` and `Models`. They are configured for specific use cases, offering the highest level of abstraction. This is the easiest way to get started if you already know the use case you want to build for.
+Prebuilt pipelines are pre-configured collections of Components, Models, and Connectors. They are built for specific use cases, offering the highest level of abstraction. This is the easiest way to get started if you already know the use case you want to build for.
 
 For a full list of available prebuilt pipelines and details on how to configure and customize them, see the [Pipelines](./reference/pipeline/pipeline.md) documentation page.
 
 ```python
 from healthchain.pipeline import MedicalCodingPipeline
+from healthchain.models import CdaRequest
 
 pipeline = MedicalCodingPipeline.load("./path/to/model")
 
-doc = Document("Patient diagnosed with myocardial infarction.")
-doc = pipeline(doc)
+cda_data = CdaRequest(document="<CDA XML content>")
+output = pipeline(cda_data)
 ```
 
 ### Sandbox 🧪
-Once you've built your pipeline, you might want to experiment with how you want your pipeline to interact with different health systems. A sandbox helps you stage and test the end-to-end workflow of your pipeline application where real-time EHR integrations are involved.
+Once you've built your pipeline, you might want to experiment with how it interacts with different healthcare systems. A sandbox helps you stage and test the end-to-end workflow of your pipeline application where real-time EHR integrations are involved.
 
-Running a sandbox will start a `FastAPI` server with standardized API endpoints and create a sandboxed environment for you to interact with your application.
+Running a sandbox will start a [FastAPI](https://fastapi.tiangolo.com/) server with pre-defined standardized endpoints and create a sandboxed environment for you to interact with your application.
 
-To create a sandbox, initialize a class that inherits from a type of `UseCase` and decorate it with the `@hc.sandbox` decorator.
+To create a sandbox, initialize a class that inherits from a type of [UseCase](./reference/sandbox/use_cases/use_cases.md) and decorate it with the `@hc.sandbox` decorator.
 
 Every sandbox also requires a **client** function marked by `@hc.ehr` and a **service** function marked by `@hc.api`. A **workflow** must be specified when creating an EHR client.
 
@@ -101,6 +122,7 @@ import healthchain as hc
 
 from healthchain.use_cases import ClinicalDocumentation
 from healthchain.pipeline import MedicalCodingPipeline
+from healthchain.models import CdaRequest, CdaResponse, CcdData
 
 @hc.sandbox
 class MyCoolSandbox(ClinicalDocumentation):
@@ -117,9 +139,9 @@ class MyCoolSandbox(ClinicalDocumentation):
         return CcdData(cda_xml=xml_string)
 
     @hc.api
-    def my_service(self, ccd_data: CcdData) -> CcdData:
+    def my_service(self, request: CdaRequest) -> CdaResponse:
         # Run your pipeline
-        results = self.pipeline(ccd_data)
+        results = self.pipeline(request)
         return results
 
 if __name__ == "__main__":
@@ -137,27 +159,21 @@ healthchain run my_sandbox.py
 
 This will start a server by default at `http://127.0.0.1:8000`, and you can interact with the exposed endpoints at `/docs`. Data generated from your sandbox runs is saved at `./output/` by default.
 
-Then run:
-
-```bash
-cd streamlist_demo
-streamlit run app.py
-```
-
 ## Utilities ⚙️
 ### Data Generator
 
 You can use the data generator to generate synthetic data for your sandbox runs.
 
 The `.generate()` is dependent on use case and workflow. For example, `CdsDataGenerator` will generate synthetic [FHIR](https://hl7.org/fhir/) data suitable for the workflow specified by the use case.
 
-We're currently working on generating synthetic [CDA](https://www.hl7.org.uk/standards/hl7-standards/cda-clinical-document-architecture/) data. If you're interested in contributing, please [reach out](https://discord.gg/UQC6uAepUz)!
+We're working on generating synthetic [CDA](https://www.hl7.org.uk/standards/hl7-standards/cda-clinical-document-architecture/) data. If you're interested in contributing, please [reach out](https://discord.gg/UQC6uAepUz)!
 
 [(Full Documentation on Data Generators)](./reference/utilities/data_generator.md)
 
 === "Within client"
     ```python
     import healthchain as hc
+
     from healthchain.use_cases import ClinicalDecisionSupport
     from healthchain.models import CdsFhirData
     from healthchain.data_generators import CdsDataGenerator

docs/reference/pipeline/component.md

@@ -16,7 +16,7 @@ Components are the building blocks of the healthchain pipeline. They are designe
 You can create your own custom components by extending the `BaseComponent` class and implementing the `__call__` method.
 
 ```python
-from healthchain.pipeline.basecomponent import BaseComponent
+from healthchain.pipeline.base import BaseComponent
 
 class MyCustomComponent(BaseComponent):
     def __init__(self, **kwargs):

docs/reference/pipeline/connectors/cdaconnector.md

@@ -0,0 +1,55 @@
+# CDA Connector
+
+The `CdaConnector` handles Clinical Document Architecture (CDA) documents, serving as both an input and output connector in the pipeline. It parses CDA documents, extracting free-text notes and relevant structured clinical data into a `Document` object, and can return an annotated CDA document as output.
+
+This connector is particularly useful for clinical documentation improvement (CDI) workflows where CDA documents need to be processed and updated with additional structured data.
+
+[(Full Documentation on Clinical Documentation)](../../sandbox/use_cases/clindoc.md)
+
+## Usage
+
+```python
+from healthchain.io import CdaConnector, Document
+from healthchain.models import CdaRequest
+from healthchain.pipeline import Pipeline
+
+# Create a pipeline with CdaConnector
+pipeline = Pipeline()
+
+cda_connector = CdaConnector()
+pipeline.add_input(cda_connector)
+pipeline.add_output(cda_connector)
+
+# Example CDA request
+cda_request = CdaRequest(document="<CDA XML content>")
+
+# Example 1: Simple pipeline execution
+pipe = pipeline.build()
+cda_response = pipe(cda_request)
+print(cda_response)
+# Output: CdaResponse(document='<Annotated CDA XML content>')
+
+# Example 2: Accessing CDA data inside a pipeline node
+@pipeline.add_node
+def example_pipeline_node(document: Document) -> Document:
+    print(document.ccd_data)
+    return document
+
+pipe = pipeline.build()
+cda_response = pipe(cda_request)
+# Output: CcdData object...
+```
+
+## Accessing data inside your pipeline
+
+Data parsed from the CDA document is stored in the `Document.ccd_data` attribute as a `CcdData` object, as shown in the example above.
+
+[(CcdData Reference)](../../../api/data_models.md#healthchain.models.data.ccddata.CcdData)
+
+## Configuration
+
+The `overwrite` parameter in the `CdaConnector` constructor determines whether existing data in the document should be overwritten. This can be useful for readability with very long CDA documents when the receiving system does not require the full document.
+
+```python
+cda_connector = CdaConnector(overwrite=True)
+```