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

Area of Science:

• Laboratory Medicine
• Medical Informatics
• Big Data Analytics

Background:

• Real-world big data studies (RWBDSs) are increasingly vital in medicine, with clinical laboratory data being a key component.
• Standardized use of this data is crucial for generating high-quality real-world evidence and improving laboratory services.
• Many clinical laboratories, especially in developing countries, struggle to utilize their data due to poor data standards and lack of RWBDS experience.

Purpose of the Study:

• To review the advantages and disadvantages of RWBDSs in laboratory medicine.
• To summarize current applications and methods of RWBDS in laboratory medicine across seven key areas.
• To provide a basis for clinical laboratories to conduct real-world research and promote standardized RWBDS practices.

Main Methods:

• Literature review of real-world big data studies in laboratory medicine.
• Analysis of RWBDS applications from seven distinct perspectives.
• Discussion of challenges in data formatting, research methods, data presentation, cleaning, storage, and mining.

Main Results:

• RWBDSs in laboratory medicine present both opportunities and limitations.
• Current applications span reference interval establishment, real-time quality control, diagnostic/prognostic modeling, epidemiology, lab management, analyte variation analysis, and external quality assessment.
• Identified areas for improvement include research methodology, data presentation, and data handling processes.

Conclusions:

• Standardized RWBDS implementation is essential for leveraging clinical laboratory data effectively.
• Addressing methodological and data management challenges will enhance the value of RWBDSs in laboratory medicine.
• This review offers guidance for laboratories aiming to conduct real-world research and adopt best practices.