Work

Practical Implications for Using Laboratory Data: Research over Federated Networks

Year: 2026

Type: book-chapter

Abstract: INTRODUCTION: Semantic standardization is essential, but not sufficient, to enable research over real-world data networks. Even when harmonized, differences in data availability, granularity, and clinical practice can introduce subtle biases that affect downstream analyses and AI model development. METHODS: This work explores laboratory data availability and testing practices using LOINC-coded data from TriNetX, a global federated research network. Significant LOINC part variations were identified using statistical dispersion assessment. RESULTS: Data from 172 healthcare institutions was evaluated. Representative examples were further analysed demonstrating the relevance of specimen and method availability, as well as property and component country-specific differences. CONCLUSIONS: Acknowledging data variability is key to minimizing biases introduced during AI model development and research over clinical real-world data.

Source: Studies in health technology and informatics

DOI: 10.3233/shti260399

Authors: David Rubio Ruiz, Aída Muñoz Monjas, Paula Bermejo Bernardo, David Perez-Rey, Matvey B. Palchuk

Institutions: TriNetX (United States), Universidad Politécnica de Madrid

Language: English

Topic: Clinical Laboratory Practices and Quality Control

Subfield: Physiology

Field: Medicine

Domain: Health Sciences

Open Access status: hybrid

OpenAlex ID: W7161958505

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