BIG DATA IN MEDICINE: FROM MEDICAL RECORDS TO EPIDEMIC PREDICTION

ABSTRACT

Big Data analytic’s combination into healthcare medicine indicates transformation in public health institutions and healthcare delivery. Big data analytic’s uses immense datasets from wearable technology, genomic sequencing, environmental origins to enable more precise diagnosis, create individual treatment plans, helps to detect earley disease outbreaks. Artificial intelligence can process vast amounts of medical data and uncover hidden patterns, helping scientists make new discoveries. Big data technologies have the potential to transform healthcare for the better, although there are significant obstacles to overcome. This article explores the benefits and challenges of using big data in three key areas of healthcare: medical records management, personalized treatment, and epidemic forecasting.

Keywords: Big Data, medical records, epidemic forecasting, artificial intelligence, АI, machine learning, personalized treatment.

Introduction

Modern medicine is undergoing a fundamental transformation due to the growth in the volume of digital information. Analytical processing of these large and complex medical data sets known as Big Data in healthcare.

Big Data in healthcare facilities plays a crucial role in combining, storing and generating huge volume of information which requires a specific technology and method for its transformation. It is a tool that processes and allows an organization to create, operate and manage very large data sets.

Big Data is gathered from many different sources, each with its own format and purpose. These sources are often managed by different departments, and the main goal for healthcare organizations is to effectively manage, process, and analyze this information. This includes information from electronic medical records, hospital systems, imaging centers, labs, and pharmacies. It also covers doctor's notes, genomic data, real-time monitoring from medical devices, and health information provided by patients themselves. Proper use of the data will allow healthcare organizations to predict diseases, provides a crucial tool for preventing epidemics and enhances clinical decision-making.

Analysis of medical records:

A medical record is an organized file of a patient’s health information and the treatment they receive. The digital transformation of healthcare has shifted medical documentation from static paper files to Electronic Medical Records (EMRs).

Hospitals have widely adopted digital tools such as e-health platforms including eGov.kz in Kazakhstan which is national electronic government platform that contains key information about patients, including: medical appointments and hospitalizations, vaccination records, laboratory results, electronic prescriptions and so on. In addition, Kazakhstan has specialized medical information systems that

contain more detailed clinical data than the eGov platform used in hospitals and clinics.

Analysis of health records can change medicine by understanding how diseases actually progress in different types of people allow to detect hidden health patterns. Estimating an individual’s likelihood of developing certain health complications creates opportunities for early prevention rather than late intervention.

And also, medical records analysis can match patients with the most effective treatments by comparing an individual’s health profile with thousands of similar past cases. This allows healthcare providers to identify which therapeutic approaches have produced the best outcomes for people with comparable clinical characteristics. Big Data in epidemic prediction.

Forecasting disease outbreaks has always been a complex task. In the past, epidemiologists mainly depended on historical records and mathematical models. But they were often limited -updates arrived slowly and laboratory confirmations took time. Today, the rise of Big Data has changed this situation entirely.

• Digital health signals: Patterns in online searches (for example, sudden increases in queries about fever or cough) and discussions on social networks can reveal the first signs of an outbreak before patients reach hospitals.
• Mobility data: Information from mobile networks and air travel records makes it possible to trace how people move, helping to predict how infections might spread between regions.
• Environmental information: Weather conditions, air quality, and temperature are also analyzed, since they influence the transmission of many infectious diseases

A clear example of this approach was observed during the COVID-19 pandemic in Kazakhstan. In early 2020, health authorities began integrating data from various digital sources, including the Unified National Electronic Health System (UNEHS), the eGov platform, and mobile operator data. These datasets were used to trace infection clusters, monitor hospital capacity, and manage the distribution of medical supplies.

Using Big data in Kazakhstan’s E-gov System Kazakstan is actively implementing big data technologies in its E-government system. This process is based on a consistent and scientifically sound strategy.

Key areas of implementation:

1.Unified data system - all government information resources, for example, taxes, law enforcement, social services, are integrated into a centralized platform.

2.Proactive services - using data analysis, the system proactively identifies citizen needs and automatically offers appropriate services.

3.Analysis for decision-making-public processes in urban development, employment, and infrastructure are analyzed using anonymized data.

4. Automated control - special algorithms monitor suspicious transactions in public procurement and other areas.

Currrent challenges:

1. Ensuring data guality and reliability

2. Preventing systemic errors in algorithms

3. Protecting information privacy

4. Maintaining the stability of the centralized system

Thus, Kazakhstan is creating a comprehensive data-driven governance system, where E-gov becomes a toolfor analyzing information and optimizing public services.

Big Data in E-Government: A Comparative Look at Kazakhstan and the United States

The U.S. follows a digital governance model built on decentralization and transparency, unlike countries that rely on centralized platforms. Federal, state, and local agencies manage their own data systems but frequently make them open to the public. The government’s national open-data portal, launched in 2009, now offer more than 250,000 datasets on healthcare, education, climate, and transport. This openness allows citizens, journalists, and technology companies to explore public information and develop digital tools that strengthen accountability and stimulate innovation. Rather than having a single nationwide E-gov platform, the United States operates a network of interconnected digital systems, regulated by legal acts protecting open access to information. This structure encourages creativity and civic involvement but sometimes results in fragmentation and inconsistency among different institutions.

Common Goals, Different Paths

Both the United States and Kazakhstan apply big data to modernize governance and improve efficiency, yet their methods differ. Kazakhstan prioritizes integration and automation, building a unified, proactive government platform, while the U.S. model values openness and collaboration, empowering citizens and businesses to use data independently. Each approach has its strengths: Kazakhstan’s centralization ensures speed and consistency, while the American system promotes innovation and transparency. Despite their differences, both face shared challenges — protecting privacy, ensuring data reliability, and reducing algorithmic bias. In both nations, big data has already become a cornerstone of modern digital governance.

Conclusion

Big data is opening up entirely new horizons for medicine. In fact, we are only at the beginning of this journey.

Today, physicians no longer make decisions based solely on their experience and limited information. They now have at their disposal the collective experience of millions of cases, a cast knowledge base that helps them to see the full picture.

This is the true revolution in approach: we are gradually moving away from the “treat disease when it appears” model to a continuous effort to maintain health. Data helps us to predict risks, prevent diseases, and select individualized treatment for each patient.

References:

1. Meyer, P., & Mayer, A. (2021). Big data in medical science and healthcare. Springer.
2. Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), https://doi.org/10.1038/nature21056
3. Ginsburg, G. S., & Phillips, K. A. (2018). Precision medicine: From science to value. Health Affairs, 37(5), 694–701. https://doi.org/10.1377/hlthaff.2017.1624
4. World Health Organization (WHO). (2021). Global strategy on digital health 2020–2025. World Health Organization. https://www.who.int/publications/i/item/9789240020924
5. Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future — Big data, machine learning, and clinical medicine. The New England Journal of Medicine, 375(13), 1216–1219. https://doi.org/10.1056/NEJMp1606181
6. Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. The New England Journal of Medicine, 380(14), 1347–1358. https://doi.org/10.1056/NEJMra1814259
7. Жумагулов Б.С., Иванова С.П. Большие данные и искусственный интеллект в системе e-government: опыт Казахстана // Информационные технологии и безопасность. – 2022. – Т. 12, № 3. – С. 112-125.
8. Нургалиева А.М. Правовые аспекты использования больших данных в государственном секторе Республики Казахстан // Право и государство. – 2020. – № 4(85). – С. 89-97.