parsing-hl7v2-messages
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 r
Profile →npx skills add maziyarpanahi/openmed --skill parsing-hl7v2-messages --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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)
- When to use
- HL7 v2 structure in one minute
- Quick start
- Whole-message segment-aware de-identification
- Workflow
- Hand-off to / from OpenMed
- Edge cases & gotchas
- Standards & references
What does the parsing-hl7v2-messages skill do?
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 r
How do I install it?
Run `npx skills add maziyarpanahi/openmed --skill parsing-hl7v2-messages --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From maziyarpanahi/openmed, a repository with 4,851 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.