/summarizing-clinical-notes

name: summarizing-clinical-notes
description: "Produces structured, citation-anchored summaries of clinical notes — one-liner, hospital course, and problem-oriented views — where every claim cites a source span so nothing is hallucinated. Use after de-identifying notes when the user wants a discharge summary draft, handoff/SBAR, problem list, or chart-abstraction summary. De-identify FIRST with openmed.deidentify, then anchor summary claims to entity spans from openmed.analyze_text. Trigger keywords: summarize note, discharge summary, hospital course, problem-oriented, one-liner, SOAP, SBAR, handoff, chart abstraction."
license: Apache-2.0
metadata:
  project: OpenMed
  category: clinical-nlp
  pairs: after
  version: "1.0"

Summarizing Clinical Notes with Span Citations

A clinical summary is only useful if it is *faithful*: every statement must trace back to something the chart actually says. The failure mode for note summarization is the confident hallucination — an invented dose, a fabricated allergy, a discharge diagnosis that was never made. This skill produces summaries where **each line cites the source span** that supports it, so a clinician can verify in one glance and catch any fabrication.

> **Not a medical device.** OpenMed and this skill assist documentation; they > do not diagnose, triage, or make autonomous clinical decisions. Every summary > is a *draft for clinician review and editing*. Surface that disclaimer in any > UI that renders these summaries.

When to use

• Drafting a discharge summary, transfer note, or SBAR/handoff from a long

encounter.

• Building a problem-oriented view (problem list with supporting evidence).
• Generating a "one-liner" (the single-sentence patient summary) for rounds.
• Chart abstraction where reviewers need quick, verifiable evidence pointers.

Quick start

De-identify before anything else, extract entities to anchor against, then compose the summary with citations:

import openmed

note = """\
HPI: 68M with HTN, T2DM presents with 3 days of productive cough and fever to
38.9C. CXR shows RLL infiltrate. Started on ceftriaxone and azithromycin.
Hospital course: improved on IV antibiotics, transitioned to PO. Discharged on
amoxicillin-clavulanate. Follow up with PCP in 1 week.
"""

# 1) ALWAYS de-identify before summarizing or sending text anywhere.
deid = openmed.deidentify(note, method="replace", policy="hipaa_safe_harbor")

# 2) Extract entities; their offsets become your citation anchors.
ner = openmed.analyze_text(deid.text, output_format="dict")
spans = {
    (e["start"], e["end"]): e["text"]
    for e in ner["entities"]
}

# 3) Compose the summary. Every bullet references a (start, end) span so a
#    reviewer can click back to the exact evidence.
def cite(start, end):
    return f"[{start}:{end}] {deid.text[start:end]!r}"

# Example problem-oriented line, grounded in detected spans:
# "Community-acquired pneumonia (RLL infiltrate) — treated with ceftriaxone +
#  azithromycin." with cite(...) anchors for each entity.

`analyze_text` returns entities as `{"text", "label", "confidence", "start", "end", "metadata"}`; the `start`/`end` offsets index the de-identified text, giving you exact, verifiable citation anchors.

Workflow

1. **De-identify** with `openmed.deidentify`. Summaries are often shared or logged; PHI must be gone before this stage. Keep the mapping (`keep_mapping=True`) only if a downstream clinician must re-identify in a controlled context — never persist the mapping with the summary. 2. **Extract grounding spans** with `openmed.analyze_text` (problems, meds, labs, procedures). These define the *allowed evidence set*: a summary claim that cannot point at a span is unsupported. 3. **Resolve context** with `openmed.clinical` (negation, temporality, subject) so "no chest pain" and "father had MI" are not summarized as active patient problems. See `resolving-clinical-context`. 4. **Compose by view:**

• **One-liner:** age/sex + key chronic problems + reason for encounter.
• **Hospital course:** ordered problems → intervention → response, each line

citing the spans it summarizes.

• **Problem-oriented:** group entities into problems; attach supporting

med/lab/procedure spans under each. 5. **Enforce citation coverage.** Reject or flag any output sentence with zero span citations. This is the anti-hallucination gate — keep it strict. 6. **Mark it a draft.** Render the medical-device disclaimer and require human sign-off before the summary enters the record.

Hand-off to / from OpenMed

• **From OpenMed:** consumes `openmed.deidentify(...)` output (de-identified

text + entity spans) and `openmed.analyze_text(...)` (`PredictionResult` dict). Entity `start`/`end` offsets are the citation anchors.

• **To OpenMed:** the summary text itself can be re-run through

`openmed.analyze_text` for a coded problem list, or through `openmed.eval` leakage gates to confirm no PHI leaked into the generated summary.

• **Citation rendering:** `analyze_text(..., output_format="html")` produces a

span-highlighted view of the source — handy for a click-to-evidence UI.

Edge cases & gotchas

• **Hallucination is the failure mode.** If your summary backbone is an LLM,

constrain it to the entity/span set and require a citation per sentence; do not let it introduce facts (doses, diagnoses, dates) absent from the spans.

• **Negation & family history.** Always run context resolution first; "denies",

"ruled out", "FH of" must not become patient problems.

• **Copy-forward / note bloat.** EHR notes carry stale copy-pasted blocks. Cite

the most recent supporting span and prefer the current encounter's text.

• **Conflicting statements.** When the chart contradicts itself (two different

discharge diagnoses), surface both with citations rather than silently picking one.

• **No autonomous action.** Never auto-finalize, auto-sign, or auto-route a

summary; i