Notes and comments

NTE often follows ORC, OBR, OBX, PID, PV1, or MSH groups to add human-readable notes. Position depends on the message profile; keep the same order your trading partner documents for wellness or screening feeds.

Field comment repeats in ER7 with ~ between occurrences. In Python, assign a list of formatted text strings:

from zato.hl7v2.v2_9 import NTE

nte = NTE()
nte.comment = [
    'Strength targets noted',
    'Mobility plan updated',
]

comment_type is a CWE bound to HL70364. Use identifier, text, and coding system name together for clear display in coaching or screening tools:

from zato.hl7v2.v2_9 import NTE, CWE

nte = NTE()
nte.comment_type = CWE(
    identifier='COACH',
    text='Coaching note',
    name_of_coding_system='HL70364',
)

coded_comment carries optional HL70611 tags as repeating CWE entries. Supply a list when you need more than one structured label on the same note row.

Populate entered_by with XCN and align entered_date_time with the same event:

from zato.hl7v2.v2_9 import NTE, XCN

nte = NTE()
nte.entered_by = XCN(
    person_identifier='COACH01',
    family_name='Rivera',
    given_name='Maria',
)
nte.entered_date_time = '20260404103000'

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.