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Updated: Jul 30, 2025

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
A global federated real-world data and analytics platform for research.
Matvey B Palchuk1,2, Jack W London3, David Perez-Rey4
1TriNetX, LLC, Cambridge, Massachusetts, USA.
The TriNetX network offers a secure, scalable platform for global electronic health record data, fostering collaboration between academic institutions and industry for biomedical research and clinical trials.
Area of Science:
- Biomedical Research
- Clinical Informatics
- Data Science
Background:
- The need for accessible, de-identified electronic health record (EHR) data is critical for advancing biomedical and clinical research.
- Existing data networks often face challenges in scalability, security, and fostering inter-organizational collaboration.
- Bridging the gap between healthcare organizations (HCOs) and industry partners (pharmaceutical companies, contract research organizations) is essential for efficient research.
Purpose of the Study:
- To describe the TriNetX network, a global platform for electronic health record data.
- To highlight its role in facilitating collaboration between academic and industry entities for research purposes.
- To demonstrate its scalability, performance, and sustainability.
Main Methods:
- Development of a technology platform with a robust security and governance model.
- Establishment of a network connecting academic/community healthcare organizations (HCOs) with industry participants.
- Incentivizing participation through data access, analytics, and trial opportunities.
Main Results:
- The TriNetX network has grown significantly, from 55 HCOs in 7 countries (2017) to over 220 HCOs in 30 countries (2022).
- Over 19,000 sponsored clinical trial opportunities have been initiated via the network.
- More than 350 peer-reviewed publications have utilized data from the TriNetX network.
Conclusions:
- The TriNetX network represents a proven and sustainable model for building and maintaining research-centric data networks.
- The academic-industry collaborative structure is effective for advancing clinical research and drug development.
- Continued growth and research output validate the network's approach to data sharing and collaboration.
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