Real-world big-data studies in laboratory medicine: Current status, application, and future considerations.

Abstract

With the recent developments in information technology, real world big data studies (RWBDSs) have attracted increasing attention in the field of medicine. In RWBDSs, clinical laboratory data is an important part of the wider scope of real-world medical data, and its standardized use is critical for the generation of high-quality real-world evidence. To improve the core functioning and competitiveness of clinical laboratories as well as provide high-quality medical services for patients, it is important to construct an information analysis model and perform RWBDSs. However, among the majority of developing countries, as well as in some developed countries, due to the poorly developed neglect of data formatting standards information construction and the lack of consideration for, and experience with, the ideas and methods of RWBDSs, many clinical laboratories are unable to make use of the vast amount of data stored in their systems. Additionally, in the literature, there remain many areas that require improvements, such as the correct misuse of research methods, appropriate unreasonable data presentation methods, and optimal opaque methods for data cleaning, storage, and mining. In this review, we describe both the advantages and disadvantages of RWBDSs in laboratory medicine. In addition, we summarize the current application and methods of RWBDS in laboratory medicine from seven different perspectives: the establishment of a reference interval, patient data-based real time quality control, diagnostic or prognostic modeling, epidemiological investigation, laboratory management, analysis of sources of variations for analytes, and external quality assessment. Finally, we discuss the future prospects of this research. This review can provide the basis for clinical laboratories to carry out real world research; additionally, it promotes and standardizes RWBDS in laboratory medicine.