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Query Builder

Build precise patient cohorts in seconds with intuitive, flexible tools.

Precision cohort building at your fingertips

If you can describe it, you can build it — then retrieve de-identified patient counts aligned to your criteria in seconds.

The TriNetX LIVE™ Query Builder makes building precise patient cohorts simple, allowing you to search for and select codes and clinical concepts across diagnoses, medications, procedures, labs, and more, then apply temporal logic, exclusion criteria, or qualifications you need.

With the power to filter by data source, geography, demographics, country, and more, Query Builder puts full control of cohort creation into your hands.

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How can it help you?

Explore how TriNetX LIVE™ Query Builder supports your research.

Design study protocols with confidence

Build and test inclusion/exclusion criteria impact on patient populations, identify optimal sites, and reduce protocol amendments before they occur.

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Create precise patient cohorts for real-world evidence generation

Compare treatment patterns, analyze outcomes, and support value stories with robust population-level insights.

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Accelerate academic research with customizable patient cohorts

Analyze population characteristics for institutional studies and generate publication-ready insights without specialized programming skills.

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Take our Real-World Impact Assessment

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 articles, news, and whitepapers

Real-World Data Are Already Transforming Healthcare Research. Here’s What Still Has to Change.
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Real-world data are already transforming healthcare research. Here’s what still has to change, from global access and data depth to genomics and AI.

From Real-World Data to Trustworthy Evidence: Designing Studies That the Data Can Support
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Good real-world data research isn’t about the most complicated method or the largest cohort. Start with the question, understand your data, and design studies it can actually support.

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Feasibility shouldn’t be a checkpoint at the end of protocol design. Here’s how AI-enabled tools help teams test eligibility criteria against real-world data while there’s still time to act.

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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.

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