TL;DR

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.

Solution: PSI built SYNETIC™, an AI-enabled knowledge engine powered by the Arango Contextual Data Platform, to unify fragmented clinical research data and create a trusted, explainable context layer connecting investigators, institutions, protocols, and historical outcomes.

Result: PSI reduced site identification from six weeks to minutes, helping clinical teams identify higher-performing sites faster, reduce non-enrolling institutions, and save millions of dollars per clinical trial.

The Hidden Cost of Clinical Trial Site Selection

Clinical trials are a race against time—and not just because patients are waiting.

Bringing a drug to market can take 10–15 years, and the operational cost of running a clinical trial can reach $160 per minute. In that environment, delays compound quickly, and inefficient decisions become extremely expensive.

One of the most critical and difficult decisions in clinical research is trial site selection: determining which hospitals and investigators are most likely to successfully recruit eligible patients and execute a study protocol.

Yet the industry consistently faces a costly reality: 40–50% of selected trial sites underperform or never enroll a single patient.

Starting a clinical trial site can cost around $30,000 or more per site, and large trials often involve hundreds of sites. When multiple sites underperform, the financial impact can quickly reach millions of dollars per study.

The challenge isn’t a lack of data. Clinical research organizations already have enormous amounts of information from historical studies.

The real problem is fragmented business context.

When Critical Knowledge Is Trapped in Silos

For many organizations, the knowledge required to make site selection decisions is scattered across dozens of systems.

Information about investigators, institutions, protocols, patient populations, and historical trial outcomes often exists in different databases, documents, spreadsheets, and internal tools.

Even when teams have access to this information, understanding how it connects is extremely difficult.

At PSI, decades of operational knowledge had accumulated across systems and internal processes. Creating a study proposal could involve manually reconciling multiple versions of long documents while ensuring compliance with regulatory requirements across dozens of countries.

What PSI needed wasn’t simply better analytics. They needed a way to connect fragmented data and preserve the relationships between it.

Why PSI Needed a Contextual Data Platform

Clinical research decisions depend on understanding relationships between many types of information at once, to name just a few:

• investigators and institutions
• study protocols and historical outcomes
• patient populations and recruitment patterns
• standards of care on national and institutional levels
• regulatory requirements across countries

Traditional data systems struggle to represent these complex relationships.

PSI needed a way to unify structured data, documents, and expert knowledge into a single contextual data layer that could preserve how everything connects.

The Arango Contextual Data Platform provided that foundation.

By combining graph, vector, document, key value and search capabilities in one platform, PSI could build a unified knowledge environment where relationships between data points remain intact.

This unified, current, and trusted business context allows teams to analyze past studies, identify patterns, and make more informed decisions.

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

Using the Arango Contextual Data Platform, PSI connects information across hundreds of thousands of historical projects, linking investigators, institutions, protocols, and trial outcomes into a single contextual model.

Instead of searching across multiple disconnected systems, AI agents and PSI teams can now analyze relationships across their entire knowledge base.

This allows researchers to identify patterns that were previously difficult to detect. For example, which investigators consistently perform well under specific protocol conditions or which institutions recruit patients most effectively in certain patient populations.

Source: PSI 

Why Explainable AI Matters in Clinical Trials

In healthcare and life sciences, decisions must be transparent and defensible.

AI systems cannot simply generate recommendations. Researchers must understand why those recommendations were made and what evidence supports them.

SYNETIC™ addresses this challenge by providing explainable insights, including:

• the rationale behind site recommendations
• supporting evidence from historical data
• confidence levels for predictions
• visibility into missing information or knowledge gaps

This level of transparency allows clinical teams to trust AI-generated decisions while maintaining the accountability required in regulated environments.

Source: PSI 

From Six Weeks to Minutes

Before SYNETIC™, identifying the final list of trial sites could take up to six weeks of research and coordination.

Today, PSI teams can generate highly informed site recommendations in just a few clicks.

By identifying sites more likely to recruit patients successfully, PSI can reduce the number of non-enrolling institutions in a study.

Given the cost of activating sites, improving site selection accuracy can save millions of dollars per clinical trial while also accelerating study timelines.

Source: PSI 

The Future: Natural Language Access to Clinical Research Knowledge

PSI continues to expand SYNETIC™ by enabling natural language access to its knowledge base.

Rather than navigating complex dashboards, researchers will increasingly be able to ask questions conversationally and interrogate large datasets directly.

Because the contextual data layer already connects PSI’s enterprise knowledge, new AI capabilities can be introduced without rebuilding the system architecture.

This approach allows PSI to continue evolving its technology while maintaining a unified, current, and trusted source of business context.

A New Foundation for Contextual AI in Clinical Research

PSI’s experience highlights an important lesson for data-driven organizations.

Successful AI initiatives depend not only on algorithms, but on the quality and completeness of the contextual data that supports them.

By unifying fragmented knowledge into a trusted and explainable contextual data layer, PSI transformed one of the most complex operational challenges in clinical research.

The Arango Contextual Data Platform enables organizations to connect enterprise data into a unified, current, and trusted business context—unlocking faster decisions, better outcomes, and measurable cost savings.

FAQ: Clinical Trial Data, AI, and the Contextual Data Layer