gender-codes-and-representation-in-different-clinical-models

Gender codes and representation in different clinical models

Capturing accurate patient information is crucial for providing quality care. One important data element is gender, which helps healthcare professionals understand patients' unique healthcare needs. The representation and coding of gender vary across different clinical models and standards, posing challenges for interoperability and data exchange.

HL7v2

In HL7v2, patient gender is captured in the PID.8 field, with the following values supported (v2 table 0001, Administrative Sex):

Code	Description
F	Female
M	Male
O	Other
U	Unknown
A	Ambiguous
N	Not applicable
X	Non-Binary

The above code system is also available as the AdministrativeSex code system in the UTG project (OID: 2.16.840.1.113883.18.2).

HL7v3 and C-CDA

In C-CDA, gender information is found in the administrativeGenderCode element and uses the AdministrativeGender code system (OID: 2.16.840.1.113883.5.1):

Code	Description
F	Female
M	Male
UN	Undifferentiated

The above code system is also available as the AdministrativeGender code system in the UTG project (OID: 2.16.840.1.113883.5.1).

Additionally, the code can be annotated with the nullFlavor attribute, which can be set to, for example, NA (not applicable) or UNK (unknown). (See the NullFlavor code system for more details.)

FHIR

In FHIR, patient's gender is represented using the AdministrativeGender code system (OID: 2.16.840.1.113883.4.642.4.2):

Code	Description
male	Male
female	Female
other	Other
unknown	Unknown

Mapping gender codes

The FHIR specification provides the mappings from FHIR codes to HL7v2 and v3 codes in the form of ConceptMap resources:

Bottom line

The representation and coding of gender is different in different clinical models. The ability to map gender codes between these models is necessary to enable interoperability and seamless data exchange, facilitating better patient care and research. Harmonizing the representation of gender across systems can enhance data accuracy, improve patient experiences, and contribute to more inclusive healthcare practices.