Skill UI

This provides a local development loop for integrating the OpenEvidence clinical decision support API. Developers can build health-tech tools and quickly iterate using mock evidence queries, citation responses, and clinical summaries. It supports toggling between mock mode for rapid development and sandbox mode for validation against the real platform, ensuring development uses de-identified data.

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 guide outlines the strict multi-environment setup for the OpenEvidence clinical AI system, crucial for maintaining HIPAA compliance. It details how to configure and validate separate environments (Development, Staging, Production) to manage different data classifications—synthetic, de-identified, and full PHI. The process emphasizes environment validation checks, BAA adherence, and robust audit logging to prevent regulatory violations and PHI exposure.

Provides comprehensive observability for OpenEvidence, a system delivering critical clinical evidence queries. It monitors key metrics such as query response latency (p95), evidence freshness, citation accuracy, and compliance audit logs (HIPAA). Essential for ensuring timely, accurate, and regulatory-compliant patient care information retrieval.

This guide provides comprehensive strategies for optimizing API interactions with OpenEvidence. It covers implementing robust caching mechanisms (TTL management), efficient batch retrieval of citations, connection pooling, and handling rate limits. By applying these techniques, users can significantly reduce query latency, ensuring smooth system performance even when handling complex multi-condition queries or large citation batches.

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.

This guide provides essential security protocols and coding best practices for integrating with OpenEvidence, a platform processing Protected Health Information (PHI). It covers mandatory topics such as HIPAA compliance, API key management, webhook signature verification, input validation, and data redaction, ensuring all interactions are secure and audit-compliant.

This guide provides a comprehensive roadmap for migrating and upgrading integrations with the OpenEvidence clinical AI platform. It addresses critical API changes, such as schema restructuring (e.g., flat answers to structured evidence), version detection, and updated citation formats. Developers can use this knowledge to implement robust migration logic, ensuring that downstream clinical applications maintain high levels of accuracy and safety when accessing evolving medical evidence.

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.