Skill UI

PyHealth delivers a full-stack Python toolkit for building, evaluating, and deploying clinical machine learning models, covering EHR datasets, medical coding systems, physiological signals, and healthcare-tailored architectures such as RETAIN, SafeDrug, and Transformer-based pipelines.

Leverages 78 public scientific, biomedical, materials, and economic REST APIs to find compounds, genes, pathways, clinical trials, patents, economic indicators, weather, and other structured data, returning raw JSON plus the databases/endpoints used.

This workflow implements Abridge's end-to-end clinical documentation pipeline. It captures ambient audio from patient encounters, performs real-time transcription, utilizes generative AI to structure the medical notes (e.g., SOAP, H&P), generates required medical codes (ICD-10, CPT), and finally inserts the structured data directly into the EHR system. Designed for primary clinical encounters, minimizing manual charting time.

This minimal working example demonstrates Abridge's ambient AI clinical documentation capabilities. It simulates a complete patient encounter session, allowing users to submit transcripts and receive a highly structured clinical note. Ideal for testing EHR connectivity, verifying AI workflow, or learning how structured medical data (SOAP notes, ICD/CPT codes) is automatically generated from free-text conversation.