Usual work

OH3 records the occupation and industry a patient held for the longest duration during their working life. It supports long-term occupational exposure assessment by providing the usual occupation, industry, duration in years, and start year.

The occupation field uses CWE with HL7 table 0958:

from zato.hl7v2.v2_9 import OH3, CWE

oh3 = OH3()
oh3.occupation = CWE(
    identifier='WLD',
    text='Welder',
    name_of_coding_system='HL70958'
)

Use the usual_occupation_duration_years numeric field:

from zato.hl7v2.v2_9 import OH3

oh3 = OH3()
oh3.usual_occupation_duration_years = (
    '25'
)

The work_unique_identifier field uses the EI datatype:

from zato.hl7v2.v2_9 import OH3, EI

oh3 = OH3()
oh3.work_unique_identifier = EI(
    entity_identifier='OH3-2024-001',
    namespace_id='MAIN'
)

OH2 records individual past or present jobs with detailed schedule and employer data, while OH3 captures only the single occupation and industry held longest over the patient's career. Use OH3 for epidemiological summaries and OH2 for detailed job history.

Zato connects to any system that speaks HL7v2 over MLLP or FHIR over REST. This includes Epic, Cerner, Meditech, Allscripts, and other EHR platforms.

Yes, Zato has built-in MLLP support for sending and receiving HL7v2 messages. MLLP channels handle the framing protocol automatically.

Zato supports HL7 v2.9, which is backward compatible with earlier versions including v2.3, v2.5, and v2.7. Standard segments and datatypes are available as typed Python classes.

Yes. Zato handles both HL7v2 and FHIR natively, so you can parse v2 messages and build FHIR resources with IG-specific extensions in the same service. Typed Python classes are available for both protocols, including extensions from US Core, UK Core, Da Vinci, and other Implementation Guides.