fhir-kindling 1.0.0a0

Python library for working with FHIR servers and resources.

:fire: FHIR Kindling

CRUD library for fhir servers, with resource validation and parsing powered by the pydantic models created by fhir.resources. More details in the Documentation.

Features

• Create, Read, Update, Delete resources using a FHIR server's REST API
• Transfer resources between servers while maintaining referential integrity using server-given IDs
• Bundle creation, validation and data management on a FHIR server via the REST API
• Supports Hapi, Blaze and IBM FHIR servers
• CSV serialization of query results
• Synthetic data generation and

Installation

Install the package using pip:

pip install fhir-kindling --user

Extras (optional)

Fhir kindling can be used with the following extras:

• ds for data science related features, such as flattening of resources into a tabular format
• app installs a web app for building queries in a GUI

pip install fhir-kindling[ds,app] --user

Usage

Connecting to a FHIR server

from fhir_kindling import FhirServer

# Connect with basic auth 
basic_auth_server = FhirServer("https://fhir.server/fhir", username="admin", password="admin")
# Connect with static token
token_server = FhirServer("https://fhir.server/fhir", token="your_token")

# Connect using oauth2/oidc
oidc_server = FhirServer("https://fhir.server/fhir", client_id="client_id", client_secret="secret",
                         oidc_provider_url="url")

# Print the server's capability statement
print(basic_auth_server.capabilities)

Query resources from the server

Basic resource query

from fhir_kindling import FhirServer
from fhir.resources.patient import Patient

# Connect using oauth2/oidc
oidc_server = FhirServer("https://fhir.server/fhir", client_id="client_id", client_secret="secret",
                         oidc_provider_url="url")

# query all patients on the server
query = oidc_server.query(Patient, output_format="json").all()
print(query.response)

# Query resources based on name of resource
query = oidc_server.query("Patient", output_format="json").all()
print(query.response)

Query with filters

Filtering the targeted resource is done using the where method on the query object. The filter is created by defining the target field, the comparison operator and the value to compare.

from fhir_kindling import FhirServer

server = FhirServer(api_address="https://fhir.server/fhir")

query = server.query("Patient").where(field="birthDate", operator="gt", value="1980").all()

Including related resources in the query

Resources that reference or are referenced by resources targeted by the query can be included in the response using the include method on the query object.

# server initialization omitted
# get the patients along with the queried conditions
query_patient_condition = server.query("Condition").include(resource="Condition", reference_param="subject").all()

# get the conditions for a patient
query_patient_condition = server.query("Patient")
query_patient_condition = query_patient_condition.include(resource="Condition", reference_param="subject", reverse=True)
response = query_patient_condition.all()

Query resources by reference

If you know the id and resource type of the resource you want to query, you can use the get method for a single reference for a list of references use get_many. The passed references should follow the format of <resource_type>/<id>.

# server initialization omitted
patient = server.get("Patient/123")

patients = server.get_many(["Patient/123", "Patient/456"])

Add resources to the server

Resources can be added to the server using the add method on the server object. Lists of resources can be added using 'add_all'.

from fhir_kindling import FhirServer
from fhir.resources.patient import Patient

# Connect to the server
server = FhirServer(api_address="https://fhir.server/fhir")

# add a single resource
patient = Patient(name=[{"family": "Smith", "given": ["John"]}])
response = server.add(patient)

# add multiple resources
patients = [Patient(name=[{"family": f"Smith_{i}", "given": ["John"]}]) for i in range(10)]
response = server.add_all(patients)

Deleting/Updating resources

Resources can be deleted from the server using the delete method on the server object, it takes as input either references to the resources or the resources itself.
Similarly the update method can be used to update the resources on the server, by passing a list of updated resources.

from fhir_kindling import FhirServer
from fhir.resources.patient import Patient

# Connect to the server
server = FhirServer(api_address="https://fhir.server/fhir")

# add some patients
patients = [Patient(name=[{"family": f"Smith_{i}", "given": ["John"]}]) for i in range(10)]
response = server.add_all(patients)

# change the name of the patients
for patient in response.resources:
    patient.name[0].given[0] = "Jane"

# update the patients on the server
updated_patients = server.update(resources=response.resources)

# delete based on reference
server.delete(references=response.references[:5])
# delete based on resources
server.delete(resources=response.resources[5:])

Transfer resources between servers

Transferring resources between servers is done using the transfer method on the server object. Using this method server assigned ids are used for transfer and referential integrity is maintained.
This method will also attempt to get all the resources that are referenced by the resources being transferred from the origin server and transfer them to the destination server as well.

from fhir_kindling import FhirServer

# initialize the two servers
server_1 = FhirServer(api_address="https://fhir.server/fhir")
server_2 = FhirServer(api_address="https://fhir.server/fhir")

# query some resources from server 1
conditions = server_1.query("Condition").limit(10)
# transfer the resources to server 2
response = server_1.transfer(server_2, conditions)

Performance

This library performs request at least 1.5 times faster than other popular fhir libraries. See Benchmarks for a more detailed description of the benchmarks.

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.