OpenMed is local-first healthcare AI software that extracts clinical information and removes personally identifying details from clinical text on hardware controlled by the user. Healthcare developers use its Python runtime, Apple Silicon and mobile SDKs, and browser support for on-device clinical NER and PII de-identification.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add maziyarpanahi/openmed --skill summarizing-clinical-notesgit clone --depth 1 https://github.com/maziyarpanahi/openmedWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/maziyarpanahi/openmed/summarizing-clinical-notes)<a href="https://agentmods.dev/skills/maziyarpanahi/openmed/summarizing-clinical-notes"><img src="https://agentmods.dev/badge/skills/maziyarpanahi/openmed/summarizing-clinical-notes/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/maziyarpanahi/openmed/summarizing-clinical-notes"><img src="https://agentmods.dev/badge/skills/maziyarpanahi/openmed/summarizing-clinical-notes.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00129 | $0.01661 |
| Opus 5.5 | $0.00052 | $0.00664 |
| Sonnet 5.5 | $0.00026 | $0.00332 |
| Haiku 4.5 | $0.00013 | $0.00166 |
Grade A, and why
summarizing-clinical-notes scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 138 lines · 129 tokens per session scan A e996ca1e8023
summarizing-clinical-notes is a skill published in the GitHub repository maziyarpanahi/openmed (5,700 stars, last pushed yesterday), licensed Apache-2.0. It adds 129 tokens to every session and 1,661 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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