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University of Utah Researchers can now access not just Electronic Medical Record (EMR) from the University of Utah Health but EMR from all over United States through TriNetX Research Network.

TriNetX datasets provide researchers access to de-identified patient data from networks of healthcare organizations (HCO) and other data providers.

What is TriNetX

TriNetX is the global health research network that revolutionizes clinical research and enables discoveries through the generation of real-world evidence.

TriNetX has partnered with healthcare organizations (HCOs) spanning 24 countries and thousands of sites, and with data providers covering 99% of US health plans to deploy a linked and continually updated global health research network representing over 300 million patients.

  • TriNetX datasets are comprised of clinical patient data such as demographics, diagnoses, procedures, labs, and medications.

    The data in TriNetX datasets are:

    • Primarily from HCOs electronic medical record (EMR) systems
    • Collected for the primary purpose of providing care to patients
  • Data in TriNetX datasets comes from HCOs and other data providers. The data these entities provide primarily comes from:

    • EMR systems         o Structured data

            o Unstructured data processed by Natural Language Processing (NLP) technology

    • Cancer registries
    • Other sources (e.g., genomic data from third party genomic testing labs)

Accessing TriNetX Data

Researchers at the University of Utah can request for TriNetX account by completing this form

Before registration, the following resources will help familiarize you with TriNetX:

After registration, Researchers can access additional training modules via the TriNetX platform.

TriNetX is a self-service web-based data exploration tool which helps clinical researchers to define a patient cohort using inclusion and exclusion criteria and to explore cohort attributes.

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Why Use TriNetX?

The TriNetX platform helps investigators at University of Utah with:

Protocol Design and Feasibility

Determine if a sufficient population matches a protocol. Investigate attributes and comorbidities of a cohort. Analyze inclusion/exclusion criteria and the impact of changes.

Site Selection for Patient Recruitment

Locate study sites based upon the availability of eligible patients matching a protocol. Predict the arrival rate of newly eligible patients and engage the right contact within the clinical trials office at study sites.

Generation of Real-World Evidence

Explore and compare cohorts. Compare outcomes of interest. Characterize drug efficacy and burden of illness.

Collaboration with Peers

Participate in multi-site research across organizations. Pursue grant-based research funding. Strengthen relationships between healthcare organizations and sponsors.