/parsing-hl7v2-messages

name: parsing-hl7v2-messages
description: "Decodes pipe-delimited HL7 v2.x messages (ADT, ORU, MDM, ORM) into structured segments/fields/components and surfaces OBX-5 and NTE-3 free-text narrative for OpenMed. Use before OpenMed processing when ingesting HL7 v2 feeds from an interface engine, lab/results system, or ADT stream and you need the embedded clinical note text de-identified and analyzed. Flatten OBX/NTE text then call openmed.deidentify and openmed.analyze_text; segment-aware redaction is available via openmed.interop.hl7v2. Trigger keywords: HL7, HL7 v2, ADT, ORU, OBX, MSH, PID, pipe-delimited, interface engine, Mirth, lab results."
license: Apache-2.0
metadata:
  project: OpenMed
  category: data-ingestion
  pairs: before
  version: "1.0"

Parsing HL7 v2 Messages for OpenMed

HL7 v2.x is the workhorse of hospital interfacing — ADT (admit/discharge/ transfer), ORU (observation results), MDM (document management), and ORM (orders) messages flow continuously between EHR, lab, radiology, and ancillary systems. The clinical *narrative* you want for NLP is buried in **OBX-5** (observation value) and **NTE-3** (notes/comments) fields, wrapped in a pipe-and-caret encoding. This skill decodes that envelope and hands the free text to OpenMed.

When to use

• You receive HL7 v2 messages from an interface engine (Mirth/NextGen Connect,

Rhapsody, Cloverleaf) and want to mine embedded note/result text.

• A lab feed (ORU^R01) carries impression/comment narrative in OBX/NTE.
• An MDM^T02 transcription message carries a full report in OBX-5.
• You need a de-identified, structured feed into `openmed.analyze_text`.

HL7 v2 structure in one minute

A message is **segments** separated by `\r` (carriage return). Each segment is 3-letter-named, then **fields** split by `|`, **components** by `^`, **repetitions** by `~`, **sub-components** by `&`, with `\` as escape. The encoding characters are declared in **MSH-1** (the field separator) and **MSH-2** (`^~\&`). Field positions are **one-based**, and MSH is special: MSH-1 *is* the separator, so MSH-2 is the first real field.

MSH|^~\&|LAB|HOSP|EHR|HOSP|20240302101500||ORU^R01|MSG0001|P|2.5
PID|1||MRN12345^^^HOSP^MR||DOE^JANE^Q||19700115|F|||1 FAKE ST^^SPRINGFIELD^IL^62704
OBR|1||ORD9|CBC^Complete Blood Count
OBX|1|TX|IMPRESSION||Mild leukocytosis; clinically correlate.||||||F
NTE|1||Patient reports fatigue x1 week. Dr. Smith notified.

Quick start

Parse the envelope and pull narrative from OBX-5 / NTE-3, then hand off:

import openmed
from openmed.interop.hl7v2 import parse_hl7v2

raw = open("results.hl7", encoding="utf-8").read()
msg = parse_hl7v2(raw)               # -> HL7Message (segments preserved)

narrative_chunks = []
for seg in msg.segments:
    if seg.name == "OBX":
        # OBX-2 is the value type; OBX-5 is the observation value.
        value_type = seg.get_field(2)
        if value_type in {"TX", "FT", "CE", "ST"}:
            narrative_chunks.append(seg.get_field(5) or "")
    elif seg.name == "NTE":
        narrative_chunks.append(seg.get_field(3) or "")

# Decode component delimiters into plain text before NLP.
flat = "\n".join(c.replace("^", " ").replace("&", " ") for c in narrative_chunks if c)

# Hand the narrative to OpenMed.
deid = openmed.deidentify(flat, method="replace", policy="hipaa_safe_harbor")
result = openmed.analyze_text(deid.text, output_format="dict")

`HL7Segment.get_field(position)` uses one-based HL7 positions and returns `None` for absent fields. `HL7Message.segment_names()` lists segments in order.

Whole-message segment-aware de-identification

When you need to redact the *entire* message (structured PID/NK1/GT1 fields *and* OBX/NTE free text) while preserving HL7 framing, use the bundled redactor instead of hand-rolling it:

from openmed.interop.hl7v2 import redact_hl7v2

safe = redact_hl7v2("results.hl7")   # path or message text
# PID-3 hashed, PID-5 name surrogated, PID-7 DOB date-shifted, OBX-5/NTE-3
# free text masked via openmed.deidentify — delimiters and segment order kept.

`redact_hl7v2` applies `DEFAULT_FIELD_MAP` (PID, PD1, NK1, GT1, IN1/IN2, OBX, NTE). Extend or override it with `field_map={("ZPS", 4): {"action": "hash"}}` for site-specific Z-segments, and pass `date_shift_days=` for a fixed, interval-preserving shift.

Workflow

1. **Frame-split safely.** Real feeds use `\r`, `\r\n`, or MLLP framing (`\x0b`…`\x1c\r`). `parse_hl7v2` auto-detects the segment separator; strip MLLP control bytes before parsing. 2. **Read encoding from MSH** — never assume `|^~\&`. The adapter derives the delimiter set from MSH-1/MSH-2 (`HL7V2Encoding.from_msh_segment`). 3. **Locate narrative.** OBX-5 (gated by OBX-2 value type), NTE-3, and report-bearing segments. Concatenate repetitions (`~`) and components (`^`). 4. **De-identify**, then **analyze** with OpenMed. 5. **Rejoin** results to the patient/encounter via PID-3 (patient id) and PV1-19 (visit number) — but redact those identifiers in anything you persist.

Hand-off to / from OpenMed

• **To OpenMed:** flattened OBX-5/NTE-3 text → `openmed.deidentify` →

`openmed.analyze_text`.

• **Adapter:** `openmed.interop.hl7v2` provides `parse_hl7v2`,

`redact_hl7v2`, `HL7Message`, `HL7Segment`, `HL7V2Encoding`, `HL7FieldRule`, and `DEFAULT_FIELD_MAP` for segment-aware de-id that preserves message framing. It is parse-and-redact only — not a conformance validator.

• **Re-link by id, not by PHI:** carry PID-3/PV1-19 as keys, but store hashed

or surrogate values (the default `redact_hl7v2` hashes PID-3).

Edge cases & gotchas

• **MLLP wrapper.** Messages off a TCP MLLP listener are framed with `\x0b`

(start) and `\x1c\r` (end). Strip these before `parse_hl7v2`.

• **Escape sequences.** `\F\`, `\S\`, `\T\`, `\R\`, `\E\` encode literal

delimiters, and `\.br\` is a line break inside OBX text. Unescape before NLP.

• **Repeating OBX.** A single result can span many OBX segments (one