Skill UI

This skill processes raw mentions of conditions and diagnoses extracted from clinical notes. It performs synonym deduplication, filters out negated or hypothetical mentions, and assigns a structured clinical status (active, resolved, or historical). The output is a clean, reconciled problem list, structured for USCDI Problem or FHIR Condition elements.

This skill facilitates reversible de-identification (pseudonymization) of clinical text, allowing sensitive Protected Health Information (PHI) to be masked for processing while retaining the ability to restore the original data later under strict authorization. It captures a mapping key and provides a function to re-identify the text, crucial for workflows like GDPR compliance, patient recontact, or auditing. Crucially, the re-identification mapping must be stored separately and securely.

This skill structures free-text adverse event narratives into the mandatory ICH E2B(R3) format. It populates critical fields required for regulatory submissions (like FAERS/EudraVigilance), including suspect drugs, reactions (mapped via MedDRA), seriousness criteria, and reaction outcomes. Use it when generating Individual Case Safety Reports (ICSRs) from clinical narratives.

This skill enhances raw clinical entity recognition by assigning three critical axes: Negation, Temporality, and Uncertainty (ConText). It determines the clinical status of a mention (e.g., affirmed, negated, historical, or uncertain), ensuring that basic NER results are transformed into clinically faithful assertions before grounding to standards like FHIR. Essential for accurate problem list generation and differential diagnosis support.

Utilize zero-shot NER to extract custom entity types (e.g., Drug, Symptom, Device) from complex clinical or biomedical text without requiring any labeled training data or fine-tuning. OpenMed wraps GLiNER/GLiNER2, allowing users to define labels on the fly. The workflow involves building a model index and then running inference via CLI or Python API, making it ideal for rapid prototyping or unstable schemas.

A comprehensive guide for embedding OpenMed-powered tools into Electronic Health Records (EHRs) like Epic or Cerner. It covers the entire SMART on FHIR workflow, including OAuth2 PKCE authorization, granular scoping (e.g., patient/DocumentReference.rs), secure token handling, and processing clinical notes locally on-device for de-identification and Named Entity Recognition (NER).

This skill interfaces with the modern v2 REST API of ClinicalTrials.gov, allowing users to search for clinical studies matching specific conditions, interventions, and recruitment statuses. It is essential for building patient-to-trial matching features, generating a corpus of trial records for biomedical NER, or finding open studies for a given diagnosis or drug. It uses cursor pagination and requires no API key.

This technique segments unstructured clinical documents (such as HPI, PMH, and A&P sections) into contextually rich, canonical chunks. This is crucial for improving the precision of downstream NLP tasks like Named Entity Recognition (NER) and de-identification. By identifying and normalizing headers and mapping them to LOINC codes, the system ensures that the source context (e.g., historical vs. active) is maintained, leading to significantly more accurate results.

Provides a hardened FastAPI endpoint for OpenMed's core functionalities: clinical Named Entity Recognition (NER), Personally Identifiable Information (PII) extraction, and de-identification. Designed for self-hosted microservices, it handles inference requests, model lifecycle management, and supports dynamic batching, ensuring secure, on-device processing of sensitive clinical data.

This skill applies consistent date shifting to clinical text, ensuring that absolute dates are obscured while critically preserving the temporal intervals between medical events. It is essential for longitudinal research or survival analysis where time-to-event data must be preserved under HIPAA Safe Harbor rules. It maintains the integrity of time differences (e.g., length of stay, visit gaps) by applying a stable, per-patient offset.

This skill converts free-text radiology narratives (CT, MRI, X-ray, etc.) into structured, machine-readable data. It automatically identifies and segments key report sections (Technique, Findings, Impression) and extracts critical clinical details such as anatomy, measurements, laterality, BI-RADS/Lung-RADS scores, and follow-up recommendations. It is crucial for building structured medical records and decision support systems.

Generates highly accurate, structured summaries from complex clinical notes, such as discharge summaries, SBAR reports, or problem lists. The core function is to anchor every generated claim to a specific source span within the original text, guaranteeing verifiability and eliminating hallucination. Essential for clinical documentation drafting and chart abstraction.