THEORETICAL FOUNDATIONS AND INTERNATIONAL EXPERIENCE OF USING BIG DATA TECHNOLOGIES IN THE HEALTH INSURANCE SYSTEM

• Inakov Sh. A.

  School of Public Health, Tashkent State Medical University

  Author

• Majidov R. A.

  School of Public Health, Tashkent State Medical University

  Author

This article examines the theoretical foundations and international experience of applying Big Data technologies in the health insurance system. The effective use of Big Data in healthcare depends not only on the quality of analytical models, but also on the infrastructure for collecting, storing, processing, and integrating large-scale heterogeneous datasets. Modern healthcare generates massive volumes of data from electronic health records (EHR/EMR), laboratory diagnostics, medical imaging, insurance claims, pharmaceutical databases, and continuous streams from Internet of Things (IoT) devices. The rapid growth of global digital data highlights the increasing importance of advanced data-driven approaches for improving decision-making in health insurance. The study reviews key components of Big Data infrastructure, including cloud computing, distributed computing clusters (Hadoop, Apache Spark), and machine learning methods, and discusses their role in risk assessment, cost prediction, fraud detection, and personalized insurance services. Based on international practices, the article emphasizes that Big Data integration into health insurance can enhance efficiency, transparency, and quality of medical services through evidence-based management and proactive risk evaluation.

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