Big Data in Laboratory Medicine-FAIR Quality for AI?

Abstract

Laboratory medicine is a digital science. Every large hospital produces a wealth 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 metadata. Processing, connecting, storing, and ordering extensive parts of these individual data requires Big Data techniques. Whereas novel technologies such as artificial intelligence and machine learning have exciting application for the augmentation of laboratory medicine, the Big Data concept remains fundamental for any sophisticated data analysis in large databases. 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 (Extract, Transform, Load) processes, careful data governance, and modern data security solutions. Enriched with clinical data, laboratory medicine data allow a gain in pathophysiological insights, can improve patient care, or can be used to develop reference intervals for diagnostic purposes. Nevertheless, Big Data in laboratory medicine do 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.

Keywords: FAIRification; artificial intelligence; clinical chemistry; digitalization; interoperability.

Figure 1

Patients’ data is entered into the patient data management system (PDMS), predominantly manually, while information about samples collected as well as about analyses conducted is entered into the laboratory information system (LIS), either manually or automatically. PDMS and LIS are connected and exchange parts of their stored data. Both systems feed a “data lake” comprising various types of data, which can be provided to researchers for Big Data applications.