Real-world data (RWD) and the real-world evidence (RWE) derived from analysis of the data are a cornerstone of clinical advancements in today’s fast-paced research environment. However, integrating, harmonizing, and leveraging global datasets at scale presents significant challenges. To understand how TriNetX is tackling these issues head-on, we sat down with Matvey Palchuk, MD, MS, FAMIA, Chief Medical Informatics Officer at TriNetX to discuss five key ways the TriNetX team is enabling researchers and life sciences organizations with fit-for-purpose, research-ready data.
1. Transforming Data Harmonization and Interoperability
Q: Matvey, why is data harmonization so critical in today’s research environment?
Matvey Palchuk: Data fragmentation is one of the biggest barriers to effective RWE generation. With ~50 million individual mappings spanning over 2,300 crosswalks, TriNetX ensures seamless integration across diverse healthcare organizations. One example of this methodological approach, as detailed in an article published in International Journal of Medical Informatics, enhances the usability of laboratory data by standardizing units across different data sources, thereby improving research accuracy and efficiency.
Furthermore, TriNetX continues to advance its support for Common Data Models (CDMs), expanding its data interoperability and standardization efforts. As demonstrated in an article published in JAMIA, enhancements in CDM integration allow researchers to seamlessly work across multiple data environments, improving reproducibility and scalability of studies. This structured approach to data exchange accelerates research and ensures compatibility with leading analytic frameworks.
2. Expanding Data Access for Comprehensive Insights
Q: What sets TriNetX apart when it comes to data access?
Matvey Palchuk: Our network is vast and continuously growing. We provide access to a diverse and representative patient population, which is crucial for generating generalizable RWE insights to support global regulatory practices and advancements in clinical research. Researchers benefit from linked electronic health records (EHRs) and claims data for over 16 million patients, allowing for more robust longitudinal studies and a clearer understanding of patient journeys, healthcare utilization, and treatment outcomes.
3. Elevating Data Quality and Optimization
Q: Beyond access, how does TriNetX ensure data quality?
Matvey Palchuk: High-quality data is the foundation of reliable research. Our advanced optimization techniques reduce orphan medication facts (these also include non-meds such as durable medical equipment, nutritional supplements, etc.) to just ~1.5 percent of the global volume. We have refined our medication mapping pipelines and implemented sophisticated latency and completeness metrics to ensure the data in the TriNetX LIVE™ platform is accurate, timely, and regulatory grade.
TriNetX is also streamlining pharmacovigilance (PV) data operations, prioritizing the most valuable data sources to enhance drug safety monitoring. The ability to access and analyze harmonized global PV data enables regulatory agencies, healthcare providers, and pharmaceutical companies to detect adverse drug reactions faster and more reliably and support clinical decision-making.
4. Integrating Advanced Analytics and AI for Deeper Insights
Q: How does TriNetX leverage technology to enhance data analysis?
Matvey Palchuk: We’re heavily investing in AI-driven analytics, including natural language processing (NLP) and precision medicine applications. TriNetX is laying the foundation for sophisticated text search and NLP-powered fact extraction. The application of NLP in healthcare data research, as demonstrated in an article published in JMIR Medical Informatics, is revolutionizing how unstructured clinical data is utilized, allowing for deeper insights into patient conditions and treatment responses.
Additionally, through our “re:think genomics” initiative, we’re aiming to mature our seamless integration of molecular genomics data with phenotypic data in the TriNetX LIVE™ platform. This effort supports precision medicine approaches by enabling researchers to study genetic markers alongside clinical outcomes, a critical step in advancing personalized treatment strategies.
5. Driving Ethical Data Practices and Global Research Collaboration
Q: How does TriNetX ensure transparency, governance, and effective global collaboration in research?
Matvey Palchuk: Ethical and responsible data use is foundational to impactful research. An article published in Clinical and Translational Science emphasizes the responsibility that comes with big data, advocating for strategic approaches to effectively utilizing EHR data for research. At TriNetX, we’re committed to transparency, adhering to rigorous international regulatory standards and best practices, providing researchers with reliable and ethically sourced data.
Beyond governance, TriNetX is dedicated to fostering global research collaboration to achieve significant advancements in research. One example is the TriNetX Pediatric Collaboratory Network, which connects researchers worldwide to enhance pediatric studies and improve healthcare outcomes for children. An article published in JAMIA Open highlights how this initiative is improving the feasibility and efficiency of pediatric studies, ultimately leading to better healthcare solutions for children worldwide.
What’s Next for TriNetX
As TriNetX continues to expand its capabilities in 2025 and beyond, our focus remains on delivering high-quality, harmonized global RWD. By partnering with TriNetX, researchers and life sciences organizations gain more than just data—they gain a trusted ally in transforming RWD into actionable insights.
Explore how TriNetX’s harmonized global RWD can accelerate your research and unlock new opportunities in healthcare innovation.
About Matvey Palchuk
Matvey Palchuk, MD, MS, FAMIA is an expert in clinical informatics, semantic interoperability, and data quality with extensive experience in healthcare information management, data acquisition and interoperability, the design of user interfaces for point-of-care applications, information modeling, knowledge management, and quality measure reporting.
