TriNetX Terrain
Inform portfolio and country-level strategy with integrated site and investigator data.
Introducing TriNetX Terrain
TriNetX Terrain is a new analytics and intelligence layer within the TriNetX LIVE™ platform that transforms complex, multi-source real-world data — spanning EHR records, clinical notes, genomic profiles, and site performance data — into actionable intelligence for clinical trial feasibility and site identification.
Clinical trial feasibility has long been plagued by a fundamental data gap: the information used to estimate patient availability and select sites rarely reflects the patients who will actually enroll. Sponsors and CROs rely on site surveys that capture investigator recall, historical enrollment data from prior studies under different protocols, and high-level database queries that cannot account for the nuances of molecularly defined or multi-criteria eligibility requirements.
TriNetX Terrain provides a connected evidence layer that allows clinical operations teams to test feasibility hypotheses against real-world patient data — including genomic and biomarker profiles and unstructured clinical notes — before study activation. Terrain surfaces not just how many patients exist, but where they exist, which sites have the data coverage to support confidence in the estimate, and which protocol criteria are most likely to constrain enrollment. The result is evidence-based site selection that reduces the risk of activation failures, missed enrollment targets, and costly late-stage protocol changes.
How can it help you?
See what you can achieve with TriNetX Terrain.
TriNetX Terrain is built on TriNetX’s federated network and a set of strategic data partnerships that no single source can replicate. That includes DrugDev Golden Numbers for site and investigator matching, clinical trial payments data as a real-world enrollment signal, trial history, and TriNetX’s own site mastering process, all brought together into a single, clean dataset sponsors can trust.
TriNetX Terrain enables you to explore this data in a new workspace inside TriNetX LIVE™ where site addresses, patient counts, trial history, investigator credentials, and performance data are directly accessible without having to create a query or export a CSV.
Common workflows like verifying a shortlisted site’s patient population, assessing investigator experience across a therapeutic area, or exporting data to run overlap analysis against your own site and investigator lists are faster and more direct than before.
What's included
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Robust Site and Investigator Data: TriNetX Terrain draws on a combination of TriNetX’s own federated EHR network and a curated set of external data partnerships. Site records are deduplicated and normalized through TriNetX’s site mastering process and matched using DrugDev Golden Numbers, the industry-standard universal identifier for global sites and investigators.
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Real-time patient population modeling against protocol criteria; site-level patient density mapping to identify where eligible patients actually exist; multi-variable eligibility assessment combining structured EHR data, clinical notes intelligence, and genomic/biomarker profiles.
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What-if scenario modeling to pressure-test protocol amendments before they are implemented.
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Discover where your team loses clinical momentum and see how personalized real-world data can accelerate your study timelines.
Want to learn more?
Explore our latest insights
The most important questions aren’t about the AI. They’re about the data beneath it, the scientific rigor behind it, the integration architecture around it, and the outcomes it has produced.
The era of the blockbuster drug and one-size-fits-all medicine is ending. What’s replacing it demands a fundamentally different approach to finding the right patients before a trial begins.
In this session, TriNetX and Parexel share a modern feasibility workflow designed for clinical operations teams, including non-medical users empowered through natural language and AI translation.
Even when the data foundation is right, even when the AI is producing high-quality insights, why are so many clinical operations teams not seeing the productivity gains the technology promised?
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