Diagnosis related group

DRG carries the diagnosis related group classification for a patient encounter - the DRG code, approval status, grouper review details, financial calculations, coder information, and various status fields. It appears in ADT, BAR, and DFT messages for reimbursement and billing workflows.

Create the segment and access its fields directly:

from zato.hl7v2.v2_9 import DRG, CNE

drg = DRG()
drg.diagnostic_related_group = CNE(
    identifier='462',
    text='ROUTINE WELLNESS',
    name_of_coding_system='HL70055'
)
drg.drg_approval_indicator = 'Y'
drg.effective_weight = '1.2'

code = drg.diagnostic_related_group
approved = drg.drg_approval_indicator
weight = drg.effective_weight

The diagnostic_related_group field uses the CNE (coded with no exceptions) datatype with HL7 table 0055:

from zato.hl7v2.v2_9 import DRG, CNE

drg = DRG()
drg.diagnostic_related_group = CNE(
    identifier='462',
    text='ROUTINE WELLNESS',
    name_of_coding_system='HL70055'
)

Use the monetary amount and charge fields with the MO (money) datatype:

from zato.hl7v2.v2_9 import DRG, MO

drg = DRG()
drg.effective_weight = '1.05'
drg.monetary_amount = MO(quantity='7500.00')
drg.total_charge = MO(quantity='7500.00')
drg.basic_charge = MO(quantity='6800.00')

The name_of_coder field uses the XPN (extended person name) datatype:

from zato.hl7v2.v2_9 import DRG, XPN

drg = DRG()
drg.name_of_coder = XPN(
    family_name='Chen',
    given_name='Lisa',
    middle_name='M'
)
drg.grouper_software_name = 'ACME GROUPER'
drg.grouper_software_version = '4.2'

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.