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Related Experiment Videos

Practical Implications for Using Laboratory Data: Research over Federated Networks.

David Rubio Ruiz1,2, Aída Muñoz Monjas1,2, Paula Bermejo Bernardo1

  • 1Biomedical Informatics Group, Universidad Politécnica de Madrid, Madrid, Spain.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

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Semantic standardization is crucial for real-world data research, but variations in lab data availability and testing practices can still introduce biases. Understanding these differences is key to reliable AI model development.

Area of Science:

  • Biomedical Informatics
  • Health Data Science
  • Clinical Research Informatics

Background:

  • Semantic standardization is vital for real-world data research networks.
  • Harmonized data can still contain biases due to availability, granularity, and clinical practice variations.
  • These biases impact downstream analyses and artificial intelligence (AI) model development.

Purpose of the Study:

  • To explore laboratory data availability and testing practices.
  • To investigate variations in LOINC-coded data within a global federated research network.
  • To identify and assess biases in real-world clinical data.

Main Methods:

  • Utilized LOINC-coded laboratory data from the TriNetX global federated research network.
  • Employed statistical dispersion assessment to identify significant variations in LOINC parts.
Keywords:
InteroperabilityLOINCLaboratory dataReal-World dataSecondary use

Related Experiment Videos

  • Analyzed data from 172 healthcare institutions, with representative examples further examined.

    • Main Results:

    • Identified significant variations in laboratory data availability and testing practices across institutions.
    • Demonstrated the relevance of specimen and method availability in data analysis.
    • Highlighted country-specific differences in property and component data.

    Conclusions:

    • Acknowledging and addressing data variability is crucial for minimizing biases in AI model development.
    • Understanding variations in real-world clinical data is essential for accurate research outcomes.
    • Variability in laboratory data impacts the reliability of AI models trained on this data.