mcpbeat

Clinical Note Extract Skill

anthropics/clinical-note-extract-skill

Extract structured data from clinical notes with span-level provenance and null-safety. Use when users say "extract [variables] from this note", "abstract this chart", "pull structured data from these notes", "what does this note say about [field]", or when building a chart-abstraction, registry, or cohort dataset from unstructured clinical text.

10k tokens
context cost
the whole folder, loaded on every use
13
files
ships runnable scripts
0
copies elsewhere
how many repositories repackaged it
371
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/anthropics/healthcare --skill clinical-note-extract-skill

What comes with it

32 649 bytes besides the instruction
README.md
assets/sample-note.md
assets/sample-schemas/ade.json
assets/sample-schemas/pft.json
references/01-define-schema.md
references/02-extract.md
references/03-review.md
references/assertion-classes.md
references/failure-modes.md
references/rules.md
scripts/batch.ts
workflows/extract-batch.js

The instruction itself

9 sections, as written by the author

Clinical Note Extraction

Structured extraction from clinical notes against a user-defined schema, with span citations for every value and explicit nulls for every absence. One note or many — the path is the same: an isolated no-tools worker extracts each note, then a deterministic validation pass verifies spans and codes.

This is the extraction primitive that care-gap reasoning, adverse-event detection, trial-eligibility screening, prior-auth evidence assembly, and registry abstraction sit on.

Steps

1  Define schema   — references/01-define-schema.md
2  Extract         — workflows/extract-batch.js (one isolated worker per note)
3  Validate        — span check + run each field's `check`
4  Report          — references/03-review.md

Step 1 — Define schema

Read references/01-define-schema.md. Turn the user's request into a schema: each field is {desc, finding?, check?}. desc says what to look for in the note's own terms; finding: true means classify assertion; check is how step 3 validates (open-ended — {kind: "terminology"|"range"|"date"|"pattern"|"enum"|..., ...params}). Confirm with the user before extracting.

Step 2 — Extract

However the user supplied notes — pasted text, file paths, a directory, PDFs, a FHIR connector, a database query — resolve each to plain text using whatever tools you have, then call the saved workflow with one {id, text} per note. The workflow's input contract is the only strict piece; how you get there is yours to figure out. It runs one note-extract-worker agent per note (no tools — note text is untrusted), each following references/rules.md, and returns one schema-enforced record per note:

Workflow({
  scriptPath: "<this skill dir>/workflows/extract-batch.js",
  args: {
    notes:  [{id, text}, ...],     // one or many
    schema: <the schema from step 1>,
    rules:  <Read references/rules.md verbatim>
  }
})

Workers have no tools — they return only what they read (value, span, presence/temporality/experiencer, null_reason, unit). All checks happen in step 3. Because note text rides inline in args, the workflow path tops out at a few dozen notes per call. For larger corpora, run bun <this skill dir>/scripts/batch.ts <notes-dir> <schema.json> records.jsonl instead — it reads files in trusted code and spawns one tool-disabled extraction per note with the same rules, then resume at step 3 over the resulting records.jsonl.

Step 3 — Validate

Runs here in the calling session. Deterministic — no model judgment. For every record:

  • Span check. For every non-null field, confirm span appears verbatim in that note's source text. Attach span_verified.
  • Run each field's check. Dispatch on check.kind:
  • terminology — dedupe (check.via, value) across all records, look each up via whatever connector answers to via, attach {code, code_status, display}. No connector for that viacode_status: "unvalidated", name it in the report.
  • rangevalue vs [min, max] and unit vs check.unit; attach range_flag.
  • date — confirm value parses as a date; attach date_ok.
  • pattern / enum — match; attach check_ok.
  • other / no check — nothing to attach.

A field is trustworthy when span_verified and its check (if any) passed. Adding a check kind = add a branch here; nothing upstream changes.

Step 4 — Report

Read references/03-review.md. Produce one row per (note, field): note_id | field | value | presence/temporality/experiencer | span | check. Below it, the completion summary: fields requested / populated / null, and per check.kind what passed vs flagged (name any terminology via that lacked a connector). Never let a failed check or unverified span pass silently.

Offer to write records + report to ~/.claude/data/healthcare/clinical-note-extract/<run-id>/. That directory is local working state, not an archive: do not copy it to shared drives or external systems without the user's explicit instruction, and tell the user it can be deleted once they have what they need — extracted records carry whatever PHI was in the source notes.

Output contract

Worker emits, per field: {value, span, location, presence?, temporality?, experiencer?, null_reason?, unit?} — only what it read. Step 3 attaches span_verified plus whatever the field's check produced (code/code_status/display for terminology, range_flag for range, etc.).

Optional — export as FHIR

If the user wants FHIR resources instead of flat records, the assertion axes map directly:

| record | FHIR |

|---|---|

| experiencer != patient | FamilyMemberHistory.condition (not Condition) |

| presence: absentverificationStatus: refuted; possibleunconfirmed; presentconfirmed | Condition.verificationStatus |

| temporality: historicalinactive; currentactive | Condition.clinicalStatus |

| temporality: hypothetical | no native field — omit, or use a RiskAssessment resource |

| value + terminology check result | Condition.code as a CodeableConcept ({text: value, coding: [{system, code, display}]}) |

| span + location | Condition.note or a provenance extension |

This is a deterministic transform over the validated records — no model call. Offer it when the user names FHIR as the target; otherwise the flat records are the default.

Prerequisites

Connectors for whatever check.via values the schema names. Missing ones don't block extraction — those fields stay unvalidated and the report names them.

How to use it

Copy the folder

Take anthropics/clinical-note-extract-skill from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.