Big Data in Laboratory Medicine-FAIR Quality for AI?

Area of Science:

• Digital Health
• Laboratory Medicine
• Bioinformatics

Background:

• Laboratory medicine generates extensive digital data daily, including numerical results, -omics analyses, quality control data, and metadata.
• Processing and analyzing this data requires advanced Big Data techniques.

Purpose of the Study:

• To highlight the fundamental role of Big Data in laboratory medicine.
• To emphasize the importance of FAIR data principles (findable, accessible, interoperable, reusable) for optimal data utilization.
• To discuss the challenges and future directions of Big Data in laboratory medicine.

Main Methods:

• Application of Big Data techniques for processing, connecting, storing, and organizing laboratory data.
• Implementation of FAIR data principles through automated recording, device integration, efficient ETL processes, data governance, and security solutions.
• Integration of laboratory data with clinical data for enhanced analysis.

Main Results:

• Big Data techniques are essential for sophisticated analysis of large laboratory medicine databases.
• Adhering to FAIR data principles enables optimal usability of laboratory data for clinical and research purposes.
• Enriched laboratory data, combined with clinical information, can lead to improved pathophysiological insights and patient care.

Conclusions:

• Big Data is fundamental to modern laboratory medicine, enabling advanced data analysis and insights.
• Achieving FAIR data is critical for maximizing the value of laboratory medicine data.
• Laboratory medicine experts must actively participate in digitalization to guide the effective use of Big Data.