parsing-hl7v2-messages

Extract clinical narrative from HL7 v2 messages by parsing pipe-delimited segments and collecting free-text content from OBX-5 and NTE-3 fields. This handles laboratory results, reports, notes, and observations from common message feeds.

Yes, you can parse HL7 ORU and ADT feeds from interface engines. The parsing supports common message types including ORU, ADT, MDM, and ORM feeds originating from laboratory systems, EHRs, and clinical document workflows.

HL7 message parsing handles escape sequences and MLLP by applying delimiter-aware segment parsing. This accurately decodes fields, components, repetitions, and sub-components across common HL7 v2 message versions while preserving message structure.

De-identify free text in HL7 feeds using segment-aware redaction that removes identifiers while preserving HL7 framing, segment order, and selected linkage keys. This ensures privacy-conscious processing before clinical analysis.

The best way to process laboratory results for entity extraction is to parse ORU feeds, extract impressions and comments from OBX-5 and NTE-3, de-identify the narrative, and pass it to OpenMed for entity extraction and clinical analysis.

Yes, HL7 v2 parsing preserves segment order during de-identification. The segment-aware redaction process removes identifiers and free text while maintaining the original HL7 framing, segment order, and selected linkage keys.