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AI-powered clinical decision support for healthcare professionals.
• Delivers instant, cited answers to complex clinical questions using a retrieval-augmented generation pipeline grounded in peer-reviewed literature. • Facilitates ambient clinical documentation through the 'Visits' feature, automating the creation of medical notes from patient encounters. • Integrates directly into EHR workflows, enabling physicians to perform natural-language evidence searches without leaving the patient chart. • Provides an 'EvidenceGrade' feature that grades and visualizes the quality and strength of cited medical evidence in real-time. • Supports pharmaceutical and life sciences operations through the Open Vista suite, aiding in clinical trial matching and drug discovery intelligence.
Rigorous grounding in licensed peer-reviewed literature and specialty guidelines minimizes hallucinations compared to general-purpose LLMs. Deep EHR integration embeds the platform into the existing physician workflow, driving higher adoption than standalone tools. Outcome-based clinical features like EvidenceGrade increase trust by providing transparent, structured assessments of the evidence supporting every answer. Strategic partnerships with major bodies like NEJM, JAMA, and ASCO ensure access to high-fidelity, proprietary medical content. HIPAA compliance and Business Associate Agreements allow for the secure input of PHI in clinical environments.
Category: AI & Automation
Team Size: 100+
Visit WebsiteOpenEvidence is a specialized AI medical platform that aggregates, synthesizes, and visualizes peer-reviewed evidence to support clinical decision-making. Trusted by hundreds of thousands of physicians, it provides cited, evidence-based answers for complex clinical questions directly at the point of care. The platform integrates seamlessly with major EHR systems like Epic and partners with prestigious medical journals to ensure the highest clinical rigor.
OpenEvidence was founded in 2021 by Daniel Nadler and Zachary Ziegler to organize and expand the world's collective medical knowledge. Inspired by the emergence of large language models, the founders collaborated with physicians and computer scientists to build a purpose-built AI platform that solves the critical challenge of accessing valid, unbiased medical information at the point of care.