Vol. 1 No. 2 (2025), Review Article
Vol. 1 No. 2 (2025)

TriNetX and Real-World Evidence: A Critical Review of Its Strengths, Limitations, and Bias Considerations in Clinical Research

Review Article
Published 2025-03-22
Mahmoud Nassar
Department of Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA. image/svg+xml
https://orcid.org/0000-0002-5401-9562
Hazem Abosheaishaa
Internal Medicine Department, Icahn School of Medicine at Mount Sinai, NYC H+H Queens, New York, NY, USA. image/svg+xml
https://orcid.org/0000-0002-5581-8702
Khaled Elfert
Division of Gastroenterology, West Virginia University School of Medicine, , Morgantown, WV, USA. image/svg+xml
https://orcid.org/0000-0001-5554-6252
Azizullah Beran
Division of Gastroenterology and Hepatology, Indiana University, Indianapolis, IN, USA. image/svg+xml
https://orcid.org/0000-0002-4161-942X
Abdellatif Ismail
Department of Internal Medicine, University of Maryland Medical Center Midtown, Baltimore, MD, USA. image/svg+xml
https://orcid.org/0009-0006-1854-4569
Mouhand Mohamed
Division of Gastroenterology and Hepatology, May Clinic, Rochester, MN, USA. image/svg+xml
https://orcid.org/0000-0002-4761-8014
Anoop Misra
National Diabetes, Obesity and Cholesterol Foundation (N-DOC), New Delhi, Delhi, India. image/svg+xml
Muhammed Amir Essibayi
Department of Neurological Surgery, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA. image/svg+xml
https://orcid.org/0000-0001-8325-2382
David J. Altschul
Department of Neurological Surgery, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA. image/svg+xml
https://orcid.org/0000-0002-5130-1378
Ahmed Y. Azzam
Montefiore-Einstein Cerebrovascular Research Lab, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA. image/svg+xml
https://orcid.org/0000-0002-4256-0159
TriNetX Strengths, Limitations, and Bias Considerations in Clinical Research
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Keywords

TriNetX
Clinical Research
Medical Research
Research Database
Real-World Evidence

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How to Cite

1.
Nassar M, Abosheaishaa H, Elfert K, et al. TriNetX and Real-World Evidence: A Critical Review of Its Strengths, Limitations, and Bias Considerations in Clinical Research. ASIDE Int Med. 2025;1(2):24-32. doi:10.71079/ASIDE.IM.03222516
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Abstract

Introduction: The increasing utilization of real-world data platforms in medical research necessitates a comprehensive understanding of their methodological strengths and limitations. TriNetX has emerged as a significant platform for exploring large healthcare datasets. This review aims to critically evaluate the methodological framework and limitations of TriNetX, assess the impact of electronic health record coding accuracy on data reliability, and analyze the platform's capacity for generating generalizable real-world evidence in clinical research.

Methods: We conducted a comprehensive review examining TriNetX's data architecture, quality metrics, and research applications, focusing on data integrity, platform architecture, and the external validity of research findings.

Results: The analysis reveals significant methodological considerations. TriNetX's reliance on retrospective data introduces biases such as selection bias and confounding variables. The coding accuracy of electronic health records, which have not been independently validated, is a critical determinant of data reliability. The demographic representation is limited, affecting the generalizability of results.

Discussion: Despite its extensive use, TriNetX's effective utilization requires careful consideration of its inherent limitations. The platform's data, predominantly from insured populations in academic and acute care settings, may not fully represent broader demographic groups. Addressing these methodological constraints is crucial for enhancing the reliability and applicability of research findings derived from TriNetX.

Conclusions: TriNetX is a valuable resource for healthcare research. However, its limitations must be acknowledged, and future research should focus on standardizing data collection and enhancing data validation processes to mitigate platform-specific biases and improve the quality and applicability of the findings.

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