Used in the HL7 continuation protocol to carry the remainder of a segment that was split across two messages. When a message is continued via DSC, the ADD segment in the continuation message contains the remaining fields of the last incomplete segment from the prior message. It is a low-level message-control mechanism and is rarely encountered in modern implementations.
from zato.hl7v2.v2_9 import ADD
add = ADD()
add.addendum_continuation_pointer = 'WELL-ADD-01'How to construct and work with real-world ADD segments.
Pointer token referencing follow-up coaching notes appended after the visit summary
from zato.hl7v2.v2_9 import ADD
add = ADD()
add.addendum_continuation_pointer = 'WELL-VISIT-ADD-2026-04'Token linking an extra OBX group with wearable metrics captured after the initial upload
from zato.hl7v2.v2_9 import ADD
add = ADD()
add.addendum_continuation_pointer = 'FIT-OBS-SUPP-55'Opaque pointer that the coaching platform resolves to supplemental meal-plan text
from zato.hl7v2.v2_9 import ADD
add = ADD()
add.addendum_continuation_pointer = 'NUTRI-COACH-NOTE-12'Step-by-step guides for working with HL7 v2 in Zato.
NTE carries inline narrative text. ADD only holds a continuation pointer so downstream systems retrieve or assemble addendum content referenced elsewhere in the workflow.
Use ADD when a finalized wellness or fitness document gains supplemental material after sign-off, and the interface models that material as a separate continuation bundle.
Assign a partner-agreed token string:
from zato.hl7v2.v2_9 import ADD
add = ADD()
add.addendum_continuation_pointer = 'ADD-TOKEN-77'Partner profiles define repetition. Many wellness feeds send a single ADD row per addendum batch, while others chain multiple ADD segments with distinct tokens.
No. ADD references additional content; the original OBX rows remain the authoritative observation set unless a separate order instructs replacement.
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.
Complete list of fields in the ADD segment, HL7 v2.9.
| # | Python name | Datatype | Usage | Repeatable | Table |
|---|---|---|---|---|---|
| 1 | addendum_continuation_pointer | ST | Optional | No | - |
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