Skill UI

This skill provides comprehensive guidelines and optimization strategies for managing API usage and reducing costs when interacting with OpenEvidence. It covers best practices such as implementing caching, batching requests, selecting appropriate API tiers, and monitoring usage dashboards to ensure efficient and cost-effective data retrieval in healthcare applications.

This module provides minimal working examples for the OpenEvidence API, designed for clinical intelligence applications. Users can perform complex clinical queries to get treatment recommendations, check potential drug interactions based on patient context, and search for authoritative medical guidelines from major health organizations (e.g., ACC/AHA). Ideal for developing evidence-based healthcare decision support systems.

This deep dive guide provides comprehensive strategies and detailed checklists for migrating to the OpenEvidence platform. It covers various migration methods, such as parallel run, Strangler fig, and Big bang approaches. The guide assists technical teams in planning the transition by addressing key areas including API mapping, data migration plans, rollback procedures, and performance baselines, ensuring a secure and smooth transition for healthcare IT systems.

This comprehensive checklist guides the secure and compliant deployment of OpenEvidence, a clinical decision support system. It covers critical requirements for handling Protected Health Information (PHI), including HIPAA-grade security, robust API integration best practices, mandatory monitoring setup, and resilience testing. Use this guide to ensure your healthcare-adjacent system meets the highest standards of compliance and reliability.

This guide demonstrates how to implement robust rate limiting and retry logic for critical healthcare APIs using a token bucket pattern. It covers managing specific service limits (e.g., clinical queries, evidence synthesis) and implementing strategies like exponential backoff and batch processing to ensure graceful degradation and reliable data retrieval in clinical decision support systems.

This comprehensive reference architecture guides the integration of OpenEvidence into clinical decision support systems (CDS). It is designed for healthcare platforms needing evidence-based query processing, deterministic citation tracking, and full audit logging. Key considerations include HIPAA compliance, optimizing for sub-second response times in point-of-care workflows, and establishing robust data models and event pipelines.

This SDK provides comprehensive, production-ready patterns for interacting with the OpenEvidence clinical evidence API. It enables developers to efficiently query medical literature, retrieve clinical guidelines, and generate evidence-based recommendations. Key features include a singleton client for consistent authentication, robust error wrappers (handling retries, invalid keys), and structured data models designed to ensure audit compliance and detailed citation provenance in complex healthcare applications.

Perform comprehensive security audits on Bluetooth Low Energy (BLE) devices, identifying potential vulnerabilities in IoT, healthcare, and smart home applications. This skill uses Python to discover devices, enumerate GATT services, and flag unencrypted or unauthenticated read/write access to sensitive data fields, helping organizations validate security controls.

Conducts comprehensive, sector-specific threat landscape assessments by profiling key threat actors, mapping attack vectors, and detailing associated MITRE ATT&CK TTPs (covering financial, healthcare, energy, etc.). Utilizing sophisticated data analysis via Python stacks and integrating real-time threat intelligence feeds (ISAC, CVEs), this tool generates highly actionable intelligence for risk management, security investment prioritization, and high-level board-level reporting on industry exposure.

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.

A comprehensive guide detailing architectural patterns for developing robust Electronic Medical Record (EMR) and Electronic Health Record (EHR) systems. Covers critical areas like patient safety protocols, structured clinical workflows, drug interaction checking, audit trail implementation, and WCAG accessibility standards. Essential for building highly reliable and compliant healthcare IT solutions.

A comprehensive guide to building HIPAA, GDPR, and other global compliance systems for healthcare applications. This pattern outlines best practices for protecting Protected Health Information (PHI) and Personally Identifiable Information (PII) by implementing robust access controls, Row-Level Security (RLS), and mandatory audit trails. Essential for any feature handling patient or clinical data.