OpenEvidence Reference Architecture

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Implements production-ready clinical AI architecture patterns and best-practice project layouts for OpenEvidence healthcare integrations.

The OpenEvidence Reference Architecture skill provides a comprehensive framework for designing and building secure, scalable clinical AI applications. It establishes a standardized project structure that separates concerns into dedicated layers for API logic, clinical services, HIPAA compliance, and EHR integrations. By utilizing this architecture, developers can ensure their healthcare applications follow industry best practices for PHI sanitization, audit logging, and FHIR-based workflows, significantly reducing the complexity of integrating OpenEvidence's clinical intelligence into existing medical environments.

Key Features

01OpenEvidence TypeScript client wrappers with built-in caching and monitoring
02Standardized six-layer architecture pattern for healthcare applications
03FHIR and CDS Hooks integration templates for EHR systems like Epic and Cerner
04Pre-configured HIPAA-compliant audit logging and PHI sanitization patterns
05Comprehensive directory structure for clinical validation and integration testing
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Use Cases

01Reviewing and refactoring healthcare project structures for scalability
02Designing new clinical decision support (CDS) tools using OpenEvidence
03Implementing secure EHR integrations for hospital-wide AI deployments