summarizing-clinical-notes

# 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: ```python 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; it is decision support, not a clinical decision. - **PHI in the summary.** A summary can re-introduce identifiers the model missed in the source. Run the *output* through `openmed.extract_pii` or an `openmed.eval` leakage gate before display or storage. ## Standards & references - HL7 C-CDA Discharge Summary / Continuity of Care Document section structure: https://www.hl7.org/ccdasearch/ - Joint Commission discharge summary required elements (CAMH / record of care): https://www.jointcommission.org/ - SBAR handoff communication (IHI): https://www.ihi.org/resources/tools/sbar-tool-situation-background-assessment-recommendation - Weed LL, problem-oriented medical record (POMR) — the origin of problem-oriented summaries: N Engl J Med, 1968. - FDA Clinical Decision Support Software guidance (device vs. non-device CDS): https://www.fda.gov/regulatory-information/search-fda-guidance-documents/clinical-decision-support-software