Skill UI

Generates publication-ready clinical decision support documents for pharma and clinical research, covering biomarker-stratified cohort analysis, evidence-graded treatment recommendations, statistical comparisons, and regulatory-ready LaTeX/PDF outputs.

Provide REST API access to ClinPGx, the successor to PharmGKB, for querying gene-drug interactions, CPIC/DPWG/FDA guidelines, allele functions, and evidence that supports precision dosing and pharmacogenomics-driven clinical decisions.

Query NCBI ClinVar for variant clinical significance, mixing gene/condition searches, API/FTP downloads, and VCF annotations to support genomic medicine interpretation workflows.

This library provides comprehensive patterns for building robust Clinical Decision Support Systems (CDSS) integrated into Electronic Medical Records (EMR). It ensures patient safety by implementing critical checks such as drug interaction screening, weight/age/renal-adjusted dose validation, and standardized clinical scoring (e.g., NEWS2). The engine uses pure functions, guaranteeing high testability and reliability for critical medical insights.

An automated verification system designed for critical healthcare application deployments (EMR/EHR). This harness enforces stringent patient safety standards by running multiple mandatory test gates, including Clinical Decision Support System (CDSS) accuracy, Protected Health Information (PHI) leakage prevention, and data integrity checks. A single failure in critical gates automatically blocks deployment, ensuring regulatory compliance and optimal patient safety in the CI/CD pipeline.

Generates professional, publication-ready clinical decision support (CDS) documents for pharmaceutical and clinical research settings. Capabilities include biomarker-stratified patient cohort analyses, evidence-based treatment recommendation reports (with GRADE grading), and advanced statistical analyses (e.g., hazard ratios, survival curves). Outputs are optimized for regulatory submissions and medical guidelines.

This skill provides comprehensive support for generating high-stakes clinical documentation, including case reports (CARE guidelines), diagnostic summaries (radiology/pathology), clinical trial reports (ICH-E3), and patient progress notes (SOAP/H&P). It mandates adherence to strict regulatory standards (HIPAA, FDA, ICH-GCP) and integrates AI-generated scientific schematics to ensure the final output is accurate, compliant, and publication-ready.

PyHealth is a comprehensive Python toolkit designed for building deep-learning pipelines in the clinical and healthcare domains. It supports diverse data modalities, including EHR records, physiological signals, and medical images. The framework follows a stable 5-stage pipeline (Dataset → Task → Model → Trainer → Metrics), enabling users to perform complex tasks like predicting mortality, optimizing drug recommendations, staging sleep, or cross-mapping medical codes (ICD/ATC/RxNorm) on datasets like MIMIC-III/IV and OMOP.

This workflow converts complex clinical encounter notes into plain-language, patient-facing After-Visit Summaries (AVS). It structures key information such as diagnosis, next steps, and medications. The system supports multi-language translation and facilitates pushing the final, readable summary directly to patient portals using FHIR standards.

This skill generates structured, professional summaries for clinical case presentations, handovers, and educational documentation. It supports established medical formats like SBAR and SOAP notes, ensuring all necessary components—from subjective complaints to recommendations—are systematically included. Designed for healthcare professionals to improve documentation clarity and facilitate seamless patient transitions. Note: This output is strictly for educational and documentation purposes and must not replace professional clinical judgment.

This skill facilitates detailed queries against the 1000 Genomes Project cohort (3,202 individuals). Users can find variants or individuals carrying specific genetic variants within defined regions. It supports querying relatedness, allele frequencies, and various clinical annotations (e.g., AlphaMissense, ClinVar), making it an essential tool for advanced genetic research and population studies.