Global trends of big data analytics in health research: a bibliometric study

Background: The field of “Big Health,” which encompasses the integration of big data in healthcare, has seen rapid development in recent years. As big data technologies continue to transform healthcare, understanding emerging trends and key advancements within the field is essential.

Methods: We retrieved and filtered articles and reviews related to big data analytics in health research from the Web of Science Core Collection, including SCI Expanded and SSCI, covering the period from 2009 to 2024. Bibliometric and co-citation analyses were conducted using VOSviewer and CiteSpace.

Results: A total of 13,609 papers were analyzed, including 10,702 original research and 2,907 reviews. Co-occurrence word analysis identified six key research areas: the application of big data analytics in health decision-making; challenges in the technological management of health and medical big data; integration of machine learning with health monitoring; privacy and ethical issues in health and medical big data; data integration in precision medicine; and the use of big data in disease management and risk assessment. The co-word burst analysis results indicate that topics such as personalized medicine, decision support, and data protection experienced significant growth between 2015 and 2020. With the advancement of big data technologies, research hotspots have gradually expanded from basic data analysis to more complex application areas, such as the digital transformation of healthcare, digital health strategies, and smart health cities.

Conclusion: This study highlights the growing impact of big data analytics in healthcare, emphasizing its role in decision-making, disease management, and precision medicine. As digital transformation in healthcare advances, addressing challenges in data integration, privacy, and machine learning integration will be crucial for maximizing the potential of big data technologies in improving health outcomes.


Big data involves the comprehensive analysis and processing of all available data, avoiding the simplifications inherent in random sampling surveys. Big data is traditionally defined by five characteristics: volume, velocity, variety, value, and veracity . Advancements in technology and deeper applications have broadened the characteristics of big data to include variability, visualization, verifiability, value density, and viability . These characteristics not only highlight the challenges associated with volume and velocity in big data but also emphasize the potential to extract accurate, reliable, and valuable information from complex and variable data sets . As information technology continues to advance rapidly, big data has permeated various aspects of life, establishing an increasingly close connection with health. The emergence of the big data era presents an opportunity to manage dynamic health conditions, address health issues promptly, and develop personalized medical strategies . Clearly, health and medical big data have become critical areas of research.


Health and medical big data encompass all data related to medical care and health outcomes generated throughout the medical process . “This includes electronic health records, medical monitoring records, biometric data, public health information, and health insurance data. In healthcare and medical fields, big data can be extensively utilized for clinical decision support, pharmaceutical development, disease monitoring, and health management. This utilization involves various big data analytics techniques, including data structuring, image analysis, and intelligent detection . Consequently, the technologies associated with the application of big data are crucial to advancing the healthcare industry.


Recent studies have extensively explored the transformative role of big data analytics in medical practice. For instance, Lorenzo et al. emphasized the transformative potential of big data in predicting oncology patient outcomes and enhancing personalized treatment. Another pivotal study by Dong et al. demonstrated how big data facilitates real-time epidemic tracking, significantly aiding rapid and effective public health responses. Furthermore, Kindle et al. explored the incorporation of big data analytics into clinical decision-making systems, discovering that data-based models significantly enhanced diagnostic precision and the efficiency of patient care. These studies highlight the essential role of advanced analytics in improving medical services and outcomes. Despite advancements, further detailed analyses and updates on the global implementation of big data and its long-term impact across various medical fields are still needed. Our research aims to address this issue through bibliometric methods.


Bibliometric research, which analyzes the characteristics of literature, serves as a technique for examining the distribution structure, quantitative relationships, and evolutionary patterns of relevant information within publications. This approach is used to assess research output and trends across various fields . VOSviewer, a Java-based software, enables the construction and visualization of bibliometric networks, such as citation coupling, co-citation analysis, author co-citation, and co-occurrence word analysis based on scientific publications . Bibliometric analysis using VOSviewer has been applied in various medical fields, including surgery , oncology , and nutrition , to gain deeper insights. Several bibliometric studies focusing on big data have been published, addressing topics such as infectious diseases , HIV , and critical care . Two studies have focused on bibliometric research in the healthcare industry using big data, analyzing articles published before 2016 . Big data research in healthcare has garnered significant attention both domestically and internationally, prompting more researchers to use big data analytics tools to address medical issues. Since 2016, there has been a growing number of studies on the application of big data technologies in healthcare, which require further exploration through bibliometric analysis . In addition, CiteSpace is used to detect the knowledge structure, the evolution of research hotspots, and the burst trends of citations in the literature. This study aims to employ VOSviewer and CiteSpace for an updated and comprehensive analysis of publications on health big data analytics.


A bibliometric analysis was conducted using VOSViewer version 1.6.19 and CiteSpace 6.2.R4 to investigate research on big data analytics in healthcare. The bibliometric methodology involved d five stages: study design, data collection, data analysis, data visualization, and interpretation .


Two investigators independently conducted a literature search. We searched the Web of Science (WoS) Core Collection, including SCI Expanded and SSCI, for studies published between 2009 and November 28, 2024. WoS was selected because of its extensive use in bibliometric studies and its superior coverage of high-impact journals . The search strategy employed was “TS = Topic.” The search formulas used were TS = (big data) and TS = (health OR healthcare OR clinical OR medical OR medicine OR medical care).


We excluded non-English articles, duplicate literature, letters, meeting abstracts, news items, editorials, comments, and retracted publications. There were no restrictions on publication date. illustrates the process and results of literature screening. A total of 13,609 documents were included in the analytic sample The extracted literature information for visualization and bibliometric analysis includes the publication year, journal title, authorship, WoS category, manuscript type, publication country/region, publication organization, total citations, and H5-index.
2.5 Statistical analysis

The exported bibliographic file was first imported into Occurrence  software for deduplication, data cleaning, and synonym consolidation. Subsequently, information such as publication date, authors, institutions, journals, and keywords was extracted. Bibliometric methods utilize mathematics, statistics, and philology to quantitatively analyze elements such as journal titles, publication years, countries/regions, organizations, authorship, citation counts, and H5-index. This study employed VOSviewer (version 1.6.19) to map keyword co-occurrence, citations, publications, bibliographic coupling in countries and institutions, as well as thematic and trend topic networks. CiteSpace 6.2.R4 was also used to detect bursts in keywords and references.


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