Skill UI

A comprehensive guide for achieving compliance with the EU MDR 2017/745. It covers crucial aspects including medical device classification (Annex VIII), developing technical documentation (Annex II/III), planning clinical evaluation (CER), and establishing post-market surveillance (PMS/PSUR) systems. Ideal for regulatory specialists preparing for Notified Body audits or needing to gap-check technical files.

This guide provides comprehensive documentation and code examples for integrating OpenEvidence webhooks. Learn how to subscribe to real-time events (e.g., query completion, evidence updates, new citations) to keep clinical decision support systems fresh. Use these webhooks to trigger downstream audit workflows and ensure immediate data synchronization.

Compose comprehensive clinical reports—case studies, diagnostics, trials, and patient notes—while ensuring CARE/ICH-GCP compliance, HIPAA-safe documentation, and mandatory AI-generated scientific schematics for each deliverable.

Formats and structurally validates local treatment-plan documentation after clinical decisions are supplied and verified by authorized professionals; not for clinical decision-making.

A comprehensive troubleshooting guide for Abridge clinical documentation integration. Covers common errors in authentication, EHR connectivity, audio streaming, note generation, and FHIR validation. Essential for developers and technical users debugging AI-powered clinical workflows.

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.

This guide details the process of setting up authentication and integration credentials for the Abridge clinical AI platform. It covers prerequisites for connecting to major EHR systems (Epic, Athena) using FHIR R4 APIs and implements OAuth 2.0 (SMART on FHIR) flows, ensuring secure, HIPAA-compliant data handling for ambient documentation.

A comprehensive guide detailing procedures for upgrading Abridge API versions and migrating between disparate Electronic Health Record (EHR) systems. It covers zero-downtime API versioning strategies (e.g., canary rollout) and outlines high-risk, multi-step procedures for ensuring seamless clinical documentation data continuity during EHR cutovers.

This module provides robust webhook handling for Abridge, managing critical clinical documentation lifecycle events. It ingests events such as note completion, encounter status changes, signing, and quality alerts. By implementing signature verification and idempotency checks, this system ensures reliable, secure integration of clinical data into external platforms like EHRs.

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 automates the creation of comprehensive Model Cards for OpenMed clinical NER or de-identification models. It gathers data from various OpenMed evaluation reports (e.g., `GateReport`, `fairness_report`, `error_report`) to structure critical governance information, including intended use, quantitative performance metrics, subgroup analysis, limitations, and required medical-device disclaimers. It ensures documentation is reproducible and compliant for clinical AI deployment.