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 results.
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Low
Low-risk findings worth noting
Low
Low-risk findings.
1 low severity finding. Worth noting, but not necessarily harmful.
W011: Third-party content exposure detected (indirect prompt injection risk).
What this means
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
Why it was flagged
The skill’s required workflow ingests outsider-authored HL7 message body text (e.g., OBX-5 / NTE-3 segments from an external interface feed) and flattens it into free text (`flat`) that is then passed to the LLM via `openmed.analyze_text(deid.text, ...)`, creating an indirect prompt-injection path.
- Repository
- maziyarpanahi/openmed
- Commit
80da98c
- Audited
- Security analysis
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