Problem: Clinical trial site identification is slow and costly, and 30-40% trial sites under-enroll patients, wasting time and millions of dollars per study.

ArangoDB's platform is a central hub for developers and database administrators, offering insights into multi-model databases, graph databases, and distributed systems. Through articles, tutorials, and documentation, ArangoDB offers insights into building scalable and flexible data solutions. Developers can learn about NoSQL databases, graph algorithms, and distributed query processing to handle diverse data models and use cases effectively.

ArangoDB

PSI, a clinical research organization, built SYNETIC™ using the Arango Contextual Data Platform to unify fragmented clinical trial data across investigators, institutions, protocols, and historical outcomes. By combining graph, vector, document, and search capabilities, the platform creates a trusted, explainable context layer that enables AI-driven site recommendations. The result: clinical trial site identification dropped from six weeks to minutes, reducing non-enrolling sites and saving millions of dollars per study. The system also provides explainable AI outputs with rationale, supporting evidence, and confidence levels—critical for regulated healthcare environments.

How PSI Reduced Clinical Trial Site Identification From Weeks to Minutes With a Unified, Trusted Context Layer • Arango

The Hidden Cost of Clinical Trial Site Selection

When Critical Knowledge Is Trapped in Silos

Why PSI Needed a Contextual Data Platform

Building SYNETIC™: PSI’s AI-enabled Knowledge Hub

Why Explainable AI Matters in Clinical Trials

The Future: Natural Language Access to Clinical Research Knowledge

A New Foundation for Contextual AI in Clinical Research

From weeks to minutes, your team can move faster too.