SynthesisFrontiers in medicine2025
Global trends of big data analytics in health research: a bibliometric study.
Synthesis in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Adherent-InvasiveMicroorganisms · 2026Article
- How big data analytics improves hospital environmental performance through supply chain innovation, decision quality, and risk taking.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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: (1) the application of big data analytics in health decision-making; (2) challenges in the technological management of health and medical big data; (3) integration of machine learning with health monitoring; (4) privacy and ethical issues in health and medical big data; (5) data integration in precision medicine; and (6) 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.