Big Data in Laboratory Medicine – Ready for AI?

Abstract

Laboratory medicine is a digital science. Every large hospital produces a variety of data each day - from simple numerical results from e.g. sodium measurements to highly complex output of “-omics” analyses, as well as quality control results and meta-data. Processing, connecting, storing, and ordering extensive parts of these individual data requires big data techniques. Though overshadowed in recent years by the term “artificial intelligence”, the big data concept remains fundamental for any sophisticated data analysis. To make laboratory medicine data optimally usable for clinical and research purposes, they need to be FAIR: findable, accessible, interoperable, and reusable. This can be achieved for example by automated recording, connection of devices, efficient ETL processes, careful data governance, and modern data security solutions. The possibilities for research are endless: Enriched with clinical data they serve projects to gain pathophysiological insights, improve patient care, or they can be used to develop reference intervals. Nevertheless, big data in laboratory medicine does not come without challenges: The growing number of analyses and data derived from them is a demanding task to be taken care of. Laboratory medicine experts are and will be needed to drive this development, take an active role in the ongoing digitalization, and provide guidance for their clinical colleagues engaging with the laboratory data in research.