segmenting-clinical-sections
Split a clinical note into canonical sections (Chief Complaint, HPI, PMH, Medications, Allergies, Assessment & Plan, etc.) before running OpenMed NER or de-identification, so section context sharpens downstream precision. Use when the user has a free-text note or discharge summary and wants section-aware processing, header detection, mapping headers to LOINC document-section codes, or per-section NER/de-id. Covers heuristic header detection, normalization to canonical section labels, LOINC/SecTag framing, and why a finding in PMH is historical while the same finding in A&P is active. Hand-off:
npx skills add maziyarpanahi/openmed --skill segmenting-clinical-sections --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.
# Segmenting clinical sections A clinical note is not flat text — it is a sequence of named sections (Chief Complaint, HPI, Past Medical History, Medications, Allergies, Assessment & Plan). The **same phrase means different things in different sections**: "diabetes" in PMH is historical context, "diabetes" in Assessment & Plan is an active problem, and "penicillin" under Allergies is an adverse-reaction flag, not a current medication. Splitting the note into canonical sections **before** NER or de-identification gives every downstream OpenMed step the context it needs to be more precise — and lets you process sensitive sections under stricter policies. ## When to use - You have a free-text note, H&P, progress note, or discharge summary and are about to run NER (`extracting-clinical-entities`) or de-identification. - The user wants section detection, header parsing, LOINC section mapping, or per-section processing (e.g. "redact the Social History section harder"). - Downstream NER is over- or under-firing because it can't tell historical PMH mentions from active A&P problems. ## Quick start ```python import re import openmed # Synthetic note. note = """CHIEF COMPLAINT: chest pain. H
- When to use
- Quick start
- Workflow
- Hand-off to / from OpenMed
- Edge cases & gotchas
- Standards & references
What does the segmenting-clinical-sections skill do?
Split a clinical note into canonical sections (Chief Complaint, HPI, PMH, Medications, Allergies, Assessment & Plan, etc.) before running OpenMed NER or de-identification, so section context sharpens downstream precision. Use when the user has a free-text note or discharge summary and wants section-aware processing, header detection, mapping headers to LOINC document-section codes, or per-section NER/de-id. Covers heuristic header detection, normalization to canonical section labels, LOINC/SecTag framing, and why a finding in PMH is historical while the same finding in A&P is active. Hand-off:
How do I install it?
Run `npx skills add maziyarpanahi/openmed --skill segmenting-clinical-sections --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.