Possible causal relationship

PCR documents a possible causal relationship between a product and an adverse event. It captures the implicated product, manufacturing and expiration dates, evaluation status, and relatedness assessment, and is used in product experience reports for regulatory post-market surveillance.

The implicated_product field uses the CWE datatype with HL7 table 0670:

from zato.hl7v2.v2_9 import PCR, CWE

pcr = PCR()
pcr.implicated_product = CWE(
    identifier='DEV001',
    text='CardioMonitor',
    name_of_coding_system='LOCAL'
)

The relatedness_assessment field uses HL7 table 0250 to indicate the degree of causal relationship:

from zato.hl7v2.v2_9 import PCR

pcr = PCR()

# HR = Highly related
pcr.relatedness_assessment = 'HR'

# SR = Somewhat related
pcr.relatedness_assessment = 'SR'

# NR = Not related
pcr.relatedness_assessment = 'NR'

Use product_evaluation_status (table 0247) and product_evaluation_results (table 0674):

from zato.hl7v2.v2_9 import PCR, CWE

pcr = PCR()
pcr.product_evaluation_status = CWE(
    identifier='CO',
    text='Complete',
    name_of_coding_system='HL70247'
)
pcr.product_evaluation_results = CWE(
    identifier='NC',
    text='No cause',
    name_of_coding_system='HL70674'
)

Use the DTM-typed date fields to record when a device was implanted and later removed:

from zato.hl7v2.v2_9 import PCR

pcr = PCR()
pcr.product_implantation_date = (
    '20230615'
)
pcr.product_explantation_date = (
    '20240801'
)

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.