FHIR® Resources (R5, R4B, STU3)

FHIR (Fast Healthcare Interoperability Resources) is a specification for exchanging healthcare information electronically. It is designed to facilitate the exchange of data between different healthcare systems and applications, and is commonly used to build APIs (Application Programming Interfaces) for healthcare data. It is based on modern web technologies, and is designed to be easy to use and implement. It uses a modular approach, with a set of "resources" that represent different types of healthcare data (such as patients, observations, and diagnoses). These resources can be combined and extended as needed to support a wide range of healthcare use cases.

This "fhir.resources" package is powered by pydantic V2, so it become faster in performance. Obviously it is written in modern python and has data validation built-in. It provides tools and classes for all of the FHIR Resources defined in the FHIR specification, and allows you to create and manipulate FHIR resources in Python. You can then use these resources to build FHIR-based APIs or to work with FHIR data in other ways.

• Easy to construct, easy to extended validation, easy to export.
• By inheriting behaviour from pydantic, compatible with ORM.
• Full support of FHIR® Extensibility for Primitive Data Types are available.
• Previous release of FHIR® Resources are available.
• Free software: BSD license

Experimental XML and YAML serialization and deserialization supports. See [Advanced Usages] section!

FHIR® Version Info

FHIR® (Release R5, version 5.0.0) is available as default. Also previous versions are available as Python sub-package (each release name string becomes sub-package name, i.e R4B ). From fhir.resources version 7.0.0; there is no FHIR R4 instead of R4B is available as sub-package.

Available Previous Versions:

• R4B (4.3.0)
• STU3 (3.0.2)

Installation

Just a simple pip install fhir.resources is enough. But if you want development version, just clone from https://github.com/nazrulworld/fhir.resources and pip install -e .[dev].

Usages

Example: 1: This example creates a new Organization resource with some of its attributes (id, active, name, address):

>>> from fhir.resources.organization import Organization
>>> from fhir.resources.address import Address
>>> data = {
...     "id": "f001",
...     "active": True,
...     "name": "Acme Corporation",
...     "address": [{"country": "Switzerland"}]
... }
>>> org = Organization(**data)
>>> org.get_resource_type() == "Organization"
True
>>> isinstance(org.address[0], Address)
True
>>> org.address[0].country == "Switzerland"
True
>>> org.model_dump()['active'] is True
True

Example: 2: This example creates a new Organization resource from json string:

>>> from fhir.resources.organization import Organization
>>> from fhir.resources.address import Address
>>> json_str = '''{"resourceType": "Organization",
...     "id": "f001",
...     "active": True,
...     "name": "Acme Corporation",
...     "address": [{"country": "Switzerland"}]
... }'''
>>> org = Organization.model_dump_json(json_str)
>>> isinstance(org.address[0], Address)
True
>>> org.address[0].country == "Switzerland"
True
>>> org.dict()['active'] is True
True

Example: 3: This example creates a new Patient resource from json object(py dict):

>>> from fhir.resources.patient import Patient
>>> from fhir.resources.humanname import HumanName
>>> from datetime import date
>>> json_obj = {"resourceType": "Patient",
...     "id": "p001",
...     "active": True,
...     "name": [
...         {"text": "Adam Smith"}
...      ],
...     "birthDate": "1985-06-12"
... }
>>> pat = Patient.model_validate(json_obj)
>>> isinstance(pat.name[0], HumanName)
True
>>> pat.birthDate == date(year=1985, month=6, day=12)
True
>>> pat.active is True
True

Example: 4: This example creates a new Patient resource from json file:

>>> from fhir.resources.patient import Patient
>>> import os
>>> import pathlib
>>> filename = pathlib.Path("foo/bar.json")
>>> pat = Patient.model_validate_json(filename.read_bytes())
>>> pat.get_resource_type() == "Patient"
True

Example: 5: This example creates a new Organization resource in python way:

>>> from fhir.resources.organization import Organization
>>> from fhir.resources.address import Address
>>> json_obj = {"resourceType": "Organization",
...     "id": "f001",
...     "active": True,
...     "name": "Acme Corporation",
...     "address": [{"country": "Switzerland"}]
... }

>>> org = Organization.model_construct()
>>> org.id = "f001"
>>> org.active = True
>>> org.name = "Acme Corporation"
>>> org.address = list()
>>> address = Address.model_construct()
>>> address.country = "Switzerland"
>>> org.address.append(address)
>>> org.model_dump() == json_obj
True

Note

Please note that due to the way the validation works, you will run into issues if you are using model_construct() to create resources that have more than one mandatory field. See this comment in issue#56 for details.

Example: 5: Auto validation while providing wrong datatype:

>>> try:
...     org = Organization({"id": "fmk", "address": ["i am wrong type"]})
...     raise AssertionError("Code should not come here")
... except ValueError:
...     pass

Advanced Usages

FHIR Comments (JSON)

It is possible to add comments inside json like xml, but need to follow some convention, what is suggested by Grahame Grieve; is implemented here.

Also it is possible to generate json string output without comments.

Examples:

>>> observation_str = b"""{
...  "resourceType": "Observation",
...  "id": "f001",
...    "fhir_comments": [
...      "   a specimen identifier - e.g. assigned when the specimen was taken by the orderer/placer  use the accession number for the filling lab   ",
...      "  Placer ID  "
...    ],
...  "text": {
...      "fhir_comments": [
...      "   a specimen identifier - e.g. assigned when the specimen was taken by the orderer/placer  use the accession number for the filling lab   ",
...      "  Placer ID  "
...    ],
...    "status": "generated",
...    "div": "<div xmlns=\"http://www.w3.org/1999/xhtml\">.........</div>"
...  },
...  "identifier": [
...    {
...      "use": "official",
...      "system": "http://www.bmc.nl/zorgportal/identifiers/observations",
...      "value": "6323"
...    }
...  ],
...  "status": "final",
...  "_status": {
...      "fhir_comments": [
...            "  EH: Note to balloters  - lots of choices for whole blood I chose this.  "
...          ]
...  },
...  "code": {
...    "coding": [
...      {
...        "system": "http://loinc.org",
...        "code": "15074-8",
...        "display": "Glucose [Moles/volume] in Blood"
...      }
...    ]
...  },
...  "subject": {
...    "reference": "Patient/f001",
...    "display": "P. van de Heuvel"
...  },
...  "effectivePeriod": {
...    "start": "2013-04-02T09:30:10+01:00"
...  },
...  "issued": "2013-04-03T15:30:10+01:00",
...  "performer": [
...    {
...      "reference": "Practitioner/f005",
...      "display": "A. Langeveld"
...    }
...  ],
...  "valueQuantity": {
...    "value": 6.3,
...    "unit": "mmol/l",
...    "system": "http://unitsofmeasure.org",
...    "code": "mmol/L"
...  },
...  "interpretation": [
...    {
...      "coding": [
...        {
...          "system": "http://terminology.hl7.org/CodeSystem/v3-ObservationInterpretation",
...          "code": "H",
...          "display": "High"
...        }
...      ]
...    }
...  ],
...  "referenceRange": [
...    {
...      "low": {
...        "value": 3.1,
...        "unit": "mmol/l",
...        "system": "http://unitsofmeasure.org",
...        "code": "mmol/L"
...      },
...      "high": {
...        "value": 6.2,
...        "unit": "mmol/l",
...        "system": "http://unitsofmeasure.org",
...        "code": "mmol/L"
...      }
...    }
...  ]
... }"""
>>> from fhir.resources.observation import Observation
>>> obj = Observation.model_validate_json(observation_str)
>>> "fhir_comments" in obj.model_dump_json()
>>> # Test comments filtering
>>> "fhir_comments" not in obj.model_dump_json(exclude_comments=True)

Special Case: Missing data

In some cases, implementers might find that they do not have appropriate data for an element with minimum cardinality = 1. In this case, the element must be present, but unless the resource or a profile on it has made the actual value of the primitive data type mandatory, it is possible to provide an extension that explains why the primitive value is not present. Example (required intent element is missing but still valid because of extension):

>>> json_str = b"""{
...    "resourceType": "MedicationRequest",
...    "id": "1620518",
...    "meta": {
...        "versionId": "1",
...        "lastUpdated": "2020-10-27T11:04:42.215+00:00",
...        "source": "#z072VeAlQWM94jpc",
...        "tag": [
...            {
...                "system": "http://www.alpha.alp/use-case",
...                "code": "EX20"
...            }
...        ]
...    },
...    "status": "completed",
...    "_intent": {
...        "extension": [
...            {
...                "url": "http://hl7.org/fhir/StructureDefinition/data-absent-reason",
...                "valueCode": "unknown"
...            }
...        ]
...    },
...    "medicationReference": {
...        "reference": "Medication/1620516",
...        "display": "Erythromycin 250 MG Oral Tablet"
...    },
...    "subject": {
...        "reference": "Patient/1620472"
...    },
...    "encounter": {
...        "reference": "Encounter/1620506",
...        "display": "Follow up encounter"
...    },
...    "authoredOn": "2018-06-16",
...    "requester": {
...        "reference": "Practitioner/1620502",
...        "display": "Dr. Harold Hippocrates"
...    },
...    "reasonReference": [
...        {
...            "reference": "Condition/1620514",
...            "display": "Otitis Media"
...        }
...    ],
...    "dosageInstruction": [
...        {
...            "text": "250 mg 4 times per day for 10 days",
...            "timing": {
...                "repeat": {
...                    "boundsDuration": {
...                        "value": 10,
...                        "unit": "day",
...                        "system": "http://unitsofmeasure.org",
...                        "code": "d"
...                    },
...                    "frequency": 4,
...                    "period": 1,
...                    "periodUnit": "d"
...                }
...            },
...            "doseAndRate": [
...                {
...                    "doseQuantity": {
...                        "value": 250,
...                        "unit": "mg",
...                        "system": "http://unitsofmeasure.org",
...                        "code": "mg"
...                    }
...                }
...            ]
...        }
...    ],
...    "priorPrescription": {
...        "reference": "MedicationRequest/1620517",
...        "display": "Amoxicillin prescription"
...    }
... }"""
>>> from fhir.resources.medicationrequest import MedicationRequest
>>> obj = MedicationRequest.model_validate_json(json_str)
>>> "intent" not in obj.model_dump()

Custom Validators

This feature is not available

fhir.resources is providing the extensive API to create and attach custom validator into any model. See more about root validator Some convention you have to follow though, while creating a root validator.

1. Number of arguments are fixed, as well as names are also. i.e (cls, values).
2. Should return values, unless any exception need to be raised.
3. Validator should be attached only one time for individual Model. Update [from now, it's not possible to attach multiple time same name validator on same class]

Example 1: Validator for Patient:

from typing import Dict
from fhir.resources.patient import Patient

import datetime

def validate_birthdate(cls, values: Dict):
    if not values:
        return values
    if "birthDate" not in values:
        raise ValueError("Patient's ``birthDate`` is required.")

    minimum_date = datetime.date(2002, 1, 1)
    if values["birthDate"] > minimum_date:
        raise ValueError("Minimum 18 years patient is allowed to use this system.")
    return values
# we want this validator to execute after data evaluating by individual field validators.
Patient.add_root_validator(validate_gender, pre=False)

Example 2: Validator for Patient from Validator Class:

from typing import Dict
from fhir.resources.patient import Patient

import datetime

class MyValidator:
    @classmethod
    def validate_birthdate(cls, values: Dict):
        if not values:
            return values
        if "birthDate" not in values:
            raise ValueError("Patient's ``birthDate`` is required.")

        minimum_date = datetime.date(2002, 1, 1)
        if values["birthDate"] > minimum_date:
            raise ValueError("Minimum 18 years patient is allowed to use this system.")
        return values
# we want this validator to execute after data evaluating by individual field validators.
Patient.add_root_validator(MyValidator.validate_gender, pre=False)

important notes It is possible add root validator into any base class like DomainResource. In this case you have to make sure root validator is attached before any import of derived class, other than validator will not trigger for successor class (if imported before) by nature.

ENUM Validator

fhir.resources is providing API for enum constraint for each field (where applicable), but it-self doesn't enforce enum based validation! see discussion here. If you want to enforce enum constraint, you have to create a validator for that.

Example: Gender Enum:

from typing import Dict
from fhir.resources.patient import Patient

def validate_gender(cls, values: Dict):
    if not values:
        return values
    enums = cls.model_fields["gender"].json_schema_extra["enum_values"]
    if "gender" in values and values["gender"] not in enums:
        raise ValueError("write your message")
    return values

Patient.add_root_validator(validate_gender, pre=True)

Reference Validator

fhir.resources is also providing enum like list of permitted resource types through json_schema_extra enum_reference_types. You can get that list by following above (Enum) approaches resource_types = cls.model_fields["managingOrganization"].json_schema_extra["enum_reference_types"]

pydantic Field Type Support

All available fhir resources (types) can be use as pydantic's Field's value types. See issue#46 Support for FastAPI pydantic response models. The module fhirtypes.py contains all fhir resources related types and should trigger validator automatically.

Resource.id aka fhirtypes.Id constraint extensibility

There are a lots of discussion here here i.) https://bit.ly/360HksL ii.) https://bit.ly/3o1fZgl about the length of Resource.Id's value. Based on those discussions, we recommend that keep your Resource.Id size within 64 letters (for the seek of intercompatibility with third party system), but we are also providing freedom about the length of Id, in respect with others opinion that 64 chr length is not sufficient, so default length is now 255. But it is offering to customize as your own requirement by doing a monkey patch.

Examples::

# patch.py >>> import importlib >>> from fhir_core import constraints >>> from fhir_core import types as ftypes >>> constraints.TYPES_ID_MAX_LENGTH = 64 >>> importlib.reload(ftypes)

Note: when you will change that behaviour, that would impact into your whole project.

XML Supports

Along side with JSON string export, it is possible to export as XML string! Before using this feature, make sure associated dependent library is installed. Use fhir.resources[xml] or fhir.resources[all] as your project requirements.

XML schema validator! It is possible to provide custom xmlparser, during load from file or string, meaning that you can validate data against FHIR xml schema(and/or your custom schema).

Example-1 Export::

>>> from fhir.resources.patient import Patient
>>> data = {"active": True, "gender": "male", "birthDate": "2000-09-18", "name": [{"text": "Primal Kons"}]}
>>> patient_obj = Patient(**data)
>>> xml_str = patient_obj.model_dump_xml(pretty_print=True)
>>> print(xml_str)
<?xml version='1.0' encoding='utf-8'?>
<Patient xmlns="http://hl7.org/fhir">
  <active value="true"/>
  <name>
    <text value="Primal Kons"/>
  </name>
  <gender value="male"/>
  <birthDate value="2000-09-18"/>
</Patient>

Example-2 Import from string::

>>> from fhir.resources.patient import Patient
>>> data = {"active": True, "gender": "male", "birthDate": "2000-09-18", "name": [{"text": "Primal Kons"}]}
>>> patient_obj = Patient(**data)
>>> xml_str = patient_obj.model_dump_xml(pretty_print=True)
>>> print(xml_str)
>>> data = b"""<?xml version='1.0' encoding='utf-8'?>
... <Patient xmlns="http://hl7.org/fhir">
...   <active value="true"/>
...   <name>
...     <text value="Primal Kons"/>
...   </name>
...   <gender value="male"/>
...   <birthDate value="2000-09-18"/>
... </Patient>"""
>>> patient = Patient.model_validate_xml(data)
>>> print(patient.model_dump_json(indent=2))
{
  "resourceType": "Patient",
  "active": true,
  "name": [
    {
      "text": "Primal Kons",
      "family": "Kons",
      "given": [
        "Primal"
      ]
    }
  ],
  "gender": "male",
  "birthDate": "2000-09-18"
}

>>> with xml parser
>>> import lxml
>>> from fhir_core.xml_utils import xml_loads
>>> schema = lxml.etree.XMLSchema(file=str(FHIR_XSD_DIR / "patient.xsd"))
>>> xmlparser = lxml.etree.XMLParser(schema=schema)
>>> patient2 = xml_loads(Patient, data, xmlparser=xmlparser)
>>> patient2 == patient
True

Example-3 Import from file::

>>> import pathlib
>>> patient3 = Patient.model_validate_xml(pathlib.Path("Patient.xml").read_bytes())
>>> patient3 == patient and patient3 == patient2
True

XML FAQ

• Although generated XML is validated against FHIR/patient.xsd and FHIR/observation.xsd in tests, but we suggest you check output of your production data.
• Comment feature is included, but we recommend you check in your complex usages.

YAML Supports

Although there is no official support for YAML documented in FHIR specification, but as an experimental feature, we add this support. Now it is possible export/import YAML strings. Before using this feature, make sure associated dependent library is installed. Use fhir.resources[yaml] or fhir.resources[all] as your project requirements.

Example-1 Export::

>>> from fhir.resources.patient import Patient
>>> data = {"active": True, "gender": "male", "birthDate": "2000-09-18", "name": [{"text": "Primal Kons", "family": "Kons", "given": ["Primal"]}]}
>>> patient_obj = Patient(**data)
>>> yml_str = patient_obj.model_dump_yaml(indent=2)
>>> print(yml_str)
resourceType: Patient
active: true
name:
- text: Primal Kons
  family: Kons
  given:
  - Primal
gender: male
birthDate: 2000-09-18

Example-2 Import from YAML string::

>>> from fhir.resources.patient import Patient
>>> data = b"""
... resourceType: Patient
... active: true
... name:
... - text: Primal Kons
...   family: Kons
...   given:
...   - Primal
...  gender: male
...  birthDate: 2000-09-18
... """
>>> patient_obj = Patient.model_validate_yaml(data)
>>> json_str = patient_obj.model_dump_json(indent=2)
>>> print(json_str)
{
  "resourceType": "Patient",
  "active": true,
  "name": [
    {
      "text": "Primal Kons",
      "family": "Kons",
      "given": [
        "Primal"
      ]
    }
  ],
  "gender": "male",
  "birthDate": "2000-09-18"
}

Example-3 Import from YAML file::

>>> import pathlib
>>> from fhir.resources.patient import Patient
>>> patient_obj = Patient.model_validate_yaml(pathlib.Path("Patient.yml").read_bytes())
>>> json_str = patient_obj.model_dump_json(indent=2)
>>> print(json_str)
{
  "resourceType": "Patient",
  "active": true,
  "name": [
    {
      "text": "Primal Kons",
      "family": "Kons",
      "given": [
        "Primal"
      ]
    }
  ],
  "gender": "male",
  "birthDate": "2000-09-18"
}

YAML FAQ

• We are using https://pyyaml.org/ PyYAML library, for serialization/deserialization but if we find more faster library, we could use that. you are welcome to provide us your suggestion.
• YAML based comments is not supported yet, instead json comments syntax is used! Of course this comment feature is in our todo list.

Allow Empty String

Although this is not good practice to allow empty string value against FHIR primitive data type String. But we in real life scenario, is it unavoidable sometimes.

Examples::

Place this code inside your __init__.py module or any place, just to make sure that this fragment of codes is runtime executed.

>>> # ``__init__.py``
>>> import importlib
>>> from fhir_core import constraints
>>> from fhir_core import types as ftypes
>>> constraints.TYPES_STRING_ALLOW_EMPTY_STR = True
>>> importlib.reload(ftypes)

FHIR release R4B over R4

FHIR release R4B is coming with not that much changes over the release of R4. So we decided not to create separate sub-package for R4 like STU3, instead there just overlaps on existing R4. This also means that in future, when we will work on R5; there will be sub-package for R4B and no R4. We suggest you to try make a plan to be upgraded to R4B. Here you could find related information dealing-strategy-R4-R4B.

You could find full discussion here #116

Migration (from 7.X.X to 8.X.X)

There is no breaking changes in terms of api/functions. But it is recommended that you should use new functions from Pydantic V2 instead of using deprecated functions.

Replacements and/or new functions.

• From FHIRAbstractModel::dict to FHIRAbstractModel::model_dump
• From FHIRAbstractModel::json to FHIRAbstractModel::model_dump_json
• From FHIRAbstractModel::yaml to FHIRAbstractModel::model_dump_yaml
• From FHIRAbstractModel::xml to FHIRAbstractModel::model_dump_xml
• From FHIRAbstractModel::parse_obj to FHIRAbstractModel::model_validate
• From FHIRAbstractModel::parse_raw to FHIRAbstractModel::model_validate_json
• From FHIRAbstractModel::parse_file to no replacement, we suggest you use pathlib (see examples)
• FHIRAbstractModel::model_validate_yaml parse YAML string to FHIRAbstractModel.
• FHIRAbstractModel::model_validate_xml parse XML string to FHIRAbstractModel.

Breaking: removed attributes and/or functions/methods.

• The attribute resource_type has been removed from base FHIR class. For example no more Resource.resource_type attribute. Now you have to use Resource.get_resource_type() instead. Although "resource_type" key is available when you will serialize as json or python dict.

Migration (from 6.X.X to 7.0.X)

First of all, you have to correct all imports path, if you wish to keep continue using FHIR release R4B or R4, as those resources are moved under sub-package named R4B. Then if you wish to use current R5 release, read carefully the following documents.

1. See the full changes history -> https://build.fhir.org/history.html
2. See complete lists of differences between R5 and R4B -> https://hl7.org/fhir/R5/diff.html
3. If you are planning to migrate direct from the release R4, then it is important to look at the differences between R4B and R4 here -> https://hl7.org/fhir/R4B/diff.html

Migration (from later than 6.X.X)

This migration guide states some underlying changes of API and replacement, those are commonly used from later than 6.X.X version.

fhir.resources.fhirelementfactory.FHIRElementFactory::instantiate

Replacement: fhir.resources.construct_fhir_element

• First parameter value is same as previous, the Resource name.
• Second parameter is more flexible than previous! it is possible to provide not only json dict but also json string or json file path.
• No third parameter, what was in previous version.

fhir.resources.fhirabstractbase.FHIRAbstractBase::__init__

Replacement: fhir.resources.fhirabstractmodel.FHIRAbstractModel::parse_obj<classmethod>

• First parameter value is same as previous, json dict.
• No second parameter, what was in previous version.

fhir.resources.fhirabstractbase.FHIRAbstractBase::as_json

Replacement: fhir.resources.fhirabstractmodel.FHIRAbstractModel::dict

• Output are almost same previous, but there has some difference in case of some date type, for example py date, datetime, Decimal are in object representation.
• It is possible to use fhir.resources.fhirabstractmodel.FHIRAbstractModel::json as replacement, when json string is required (so not need further, json dumps from dict)

Note:

All resources/classes are derived from fhir.resources.fhirabstractmodel.FHIRAbstractModel what was previously from fhir.resources.fhirabstractbase.FHIRAbstractBase.

Release and Version Policy

Starting from version 5.0.0 we are following our own release policy and we although follow Semantic Versioning scheme like FHIR® version. Unlike previous statement (bellow), releasing now is not dependent on FHIR®.

removed statement

This package is following FHIR® release and versioning policy, for example say, FHIR releases next version 4.0.1, we also release same version here.

Credits

All FHIR® Resources (python classes) are generated using fhir-parser which is forked from https://github.com/smart-on-fhir/fhir-parser.git.

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