Introduction

FHIRPath Normative Release (v2.0.0) implementation in Python, along side it provides support for FHIR Search API and Query (we called it fql(FHIR Query Language)) API to fetch FHIR resources from any data-source(database). This library is built in ORM like approach. Our goal is to make 100% (as much as possible) FHIRPath Normative Release (v2.0.0) specification compliance product.

• Supports FHIR® STU3 and R4.
• Supports multiple provider´s engine. Now Plone & guillotina framework powered providers fhirpath-guillotina and collective.fhirpath respectively are supported and more coming soon.
• Supports multiple dialects, for example elasticsearch, GraphQL, PostgreSQL. Although now elasticsearch has been supported.
• Provide full support of FHIR Search with easy to use API.

Usages

This library is kind of abstract type, where all specifications from FHIRPath Normative Release (v2.0.0) are implemented rather than completed solution (ready to go). The main reason behind this design pattern, to support multiple database systems as well as well as any framework, there is no dependency.

fhirpath never taking care of creating indexes, mappings (elasticsearch) and storing data, if you want to use this library, you have to go through any of existing providers (see list bellow) or make your own provider (should not too hard work).

Simple example

Assumption:

1. Elasticsearch server 7.x.x Installed.
2. Mappings and indexes are handled manually.
3. Data (document) also are stored manually.

Create Connection and Engine:

>>> from fhirpath.connectors import create_connection
>>> from fhirpath.engine.es import ElasticsearchEngine
>>> from fhirpath.engine import dialect_factory
>>> from fhirpath.enums import FHIR_VERSION

>>> host, port = "127.0.0.1", 9200
>>> conn_str = "es://@{0}:{1}/".format(host, port)
>>> connection = create_connection(conn_str, "elasticsearch.Elasticsearch")
>>> connection.raw_connection.ping()
True
>>> engine = ElasticsearchEngine(FHIR_VERSION.R4, lambda x: connection, dialect_factory)

Basic Search:

>>> from fhirpath.search import Search
>>> from fhirpath.search import SearchContext

>>> search_context = SearchContext(engine, "Organization")
>>> params = (
....    ("active", "true"),
....    ("_lastUpdated", "2010-05-28T05:35:56+00:00"),
....    ("_profile", "http://hl7.org/fhir/Organization"),
....    ("identifier", "urn:oid:2.16.528.1|91654"),
....    ("type", "http://hl7.org/fhir/organization-type|prov"),
....    ("address-postalcode", "9100 AA"),
....    ("address", "Den Burg"),
.... )
>>> fhir_search = Search(search_context, params=params)
>>> bundle = fhir_search()
>>> len(bundle.entry) == 0
True

Basic Query:

>>> from fhirpath.enums import SortOrderType
>>> from fhirpath.query import Q_
>>> from fhirpath.fql import T_
>>> from fhirpath.fql import V_
>>> from fhirpath.fql import exists_
>>> query_builder = Q_(resource="Organization", engine=engine)
>>>  query_builder = (
....    query_builder.where(T_("Organization.active") == V_("true"))
....    .where(T_("Organization.meta.lastUpdated", "2010-05-28T05:35:56+00:00"))
....    .sort(sort_("Organization.meta.lastUpdated", SortOrderType.DESC))
.... )
>>> query_result = query_builder(async_result=False)
>>> for resource in query_result:
....    assert resource.__class__.__name__ == "OrganizationModel"
>>> # test fetch all
>>> result = query_result.fetchall()
>>> result.__class__.__name__ == "EngineResult"
True

>>> query_builder = Q_(resource="ChargeItem", engine=engine)
>>> query_builder = query_builder.where(exists_("ChargeItem.enteredDate"))
>>> result = query_builder(async_result=False).single()
>>> result is not None
True
>>> isinstance(result, builder._from[0][1])
True

>>> query_builder = Q_(resource="ChargeItem", engine=engine)
>>> query_builder = query_builder.where(exists_("ChargeItem.enteredDate"))
>>> result = query_builder(async_result=False).first()
>>> result is not None
True
>>> isinstance(result, builder._from[0][1])
True

Available Provider (known)

Currently very few numbers of providers available, however more will coming soon.

fhirpath-guillotina

A guillotina framework powered provider, battery included, ready to go! Please follow associated documentation.

1. Engine: Elasticsearch
2. PyPi: https://pypi.org/project/fhirpath-guillotina/
3. Source: https://github.com/nazrulworld/fhirpath_guillotina

collective.fhirpath

A Plone powered provider, like fhirpath-guillotina every thing is included. ready to go, although has a dependency on plone.app.fhirfield.

1. Engine: Elasticsearch
2. PyPi: https://pypi.org/project/collective.fhirpath/
3. Source: https://github.com/nazrulworld/collective.fhirpath

unlisted

Why are you waiting for? You are welcome to list your provider here! Developing provider should not be so hard, as fhirpath is giving you convenient APIs.

Elasticsearch Custom Analyzer

To get some special search features for reference type field, you will need to setup custom analyzer for your elasticsearch index.

Example Custom Analyzer:

settings = {
    "analysis": {
        "normalizer": {
            "fhir_token_normalizer": {"filter": ["lowercase", "asciifolding"]}
        },
        "analyzer": {
            "fhir_reference_analyzer": {
                "tokenizer": "keyword",
                "filter": ["fhir_reference_filter"],
            },
        },
        "filter": {
            "fhir_reference_filter": {
                "type": "pattern_capture",
                "preserve_original": True,
                "patterns": [r"(?:\w+\/)?(https?\:\/\/.*|[a-zA-Z0-9_-]+)"],
            },
        },
        "char_filter": {},
        "tokenizer": {},
    }

Example Mapping (Reference Field):

"properties": {
  "reference": {
    "type": "text",
    "index": true,
    "store": false,
    "analyzer": "fhir_reference_analyzer"
}

ToDo

1. fhirbase engine aka provider implementation.
2. All methods/functions are defined in FHIRPath specification, would be completed.
3. Implement https://github.com/ijl/orjson
4. https://developers.redhat.com/blog/2017/11/16/speed-python-using-rust/

Credits

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

© Copyright HL7® logo, FHIR® logo and the flaming fire are registered trademarks owned by Health Level Seven International

"FHIR® is the registered trademark of HL7 and is used with the permission of HL7. Use of the FHIR trademark does not constitute endorsement of this product by HL7" https://github.com/beda-software/fhirpath-py