Skill UI

This skill guides the creation of computable phenotypes and patient cohorts using the OHDSI/OMOP CDM standards. It allows users to define precise, executable criteria by combining standard concept sets (like PheKB) with advanced Natural Language Processing (NLP) features extracted from free clinical text. Use this when structured codes alone are insufficient to capture critical health information (e.g., symptoms, social context).

Utilizes OpenMed's analyze_text to perform sophisticated Named-Entity Recognition (NER) on clinical and biomedical text. This tool extracts structured entities such as diseases, drugs, anatomy, genes, and proteins. It provides precise character spans and model confidence scores, making it essential for building robust, privacy-first clinical Natural Language Processing (NLP) pipelines.

Use openmed.extract_pii to detect and retrieve specific types of Protected Health Information (PHI) or Personally Identifiable Information (PII) spans (names, dates, MRNs, etc.) from clinical text. Unlike deidentification, this function returns the original text and the detected spans, allowing users to audit, preview redactions, normalize labels, or route spans to a custom anonymizer without altering the source data. It is ideal for pre-processing and policy enforcement.

This skill facilitates the ingestion of unstructured clinical notes and reports from FHIR R4 EHR APIs (e.g., Epic, Cerner). It handles complex tasks including paginating large search results, decoding base64 attachments (PDF, text), and extracting narrative content. The extracted text is then passed to the OpenMed pipeline for HIPAA-compliant de-identification and subsequent NLP analysis, enabling structured data insights from raw clinical records.

This module converts diverse clinical inputs—including scanned images, faxes, CSV/TSV exports, and C-CDA XML—into a normalized, clean text format (`ExtractedDocument`). It supports on-device OCR (Tesseract/PaddleOCR) and structured redaction for tables. It is the crucial first stage for feeding de-identification and Named Entity Recognition (NER) pipelines.

This skill decodes complex pipe-delimited HL7 v2.x messages (including ADT, ORU, MDM) into structured segments and fields. It is essential for ingesting HL7 feeds from interface engines, allowing users to surface embedded clinical narratives (OBX-5 and NTE-3) for subsequent Natural Language Processing (NLP) analysis. It also provides segment-aware de-identification capabilities to redact PHI while preserving the native HL7 message structure.

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.

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).

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 tool summarizes long documents into concise summaries using advanced NLP techniques entirely on your local machine. It ensures maximum data privacy as no external network connections or APIs are required, making it ideal for offline environments and sensitive data processing.

Toolkit for comprehensive reading, editing, and creating Word documents (.docx) with visual quality control. Use for extracting content, precise typography, tables, diagrams, layout validation, and producing professional deliverables.

Learn to use SuperDoc CLI v1 to edit, query, and transform Word (.docx) documents. Covers stateful/stateless workflows, text replacement, formatting, comments, track changes, and history operations with JSON-based target addressing.