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A review of the literature on big data analytics in healthcare

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  • Panagiota Galetsi
  • Korina Katsaliaki

Abstract

Big data analytics (BDA) is of paramount importance in healthcare aspects such as patient diagnostics, fast epidemic recognition, and improvement of patient management. The objective of this profiling study is (a) to provide an overview of the BDA publication dynamics in the healthcare domain and (b) to discuss this scientific field through related examples. A sampling literature review has been conducted. A total of 804 papers have been identified and content analysis has been performed to mine knowledge in the domain for the years 2000–2016. The findings show that co-authors’ backgrounds are from the subject areas of medicine and computer sciences. Most articles are experimental in nature and use modeling and machine learning techniques to exploit clinical data, for health monitoring and prediction purposes. Many articles are relevant to the medical specialties of neurology/neurosurgery/neuropsychiatry, medical oncology, and cardiology. Well-cited papers investigate the identification and management of high-risk/cost patients, the use of big data, Hadoop and cloud computing in genomics, and the development of mobile applications for disease management. Important is also the research about improving disease prediction by investigating patients' medical results using advanced analysis (such as segmentation and predictive modelling, machine learning, visualisation, etc.).

Suggested Citation

  • Panagiota Galetsi & Korina Katsaliaki, 2020. "A review of the literature on big data analytics in healthcare," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 71(10), pages 1511-1529, October.
    • Handle: RePEc:taf:tjorxx:v:71:y:2020:i:10:p:1511-1529
    DOI: 10.1080/01605682.2019.1630328
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    Cited by:

    1. Bag, Surajit & Dhamija, Pavitra & Singh, Rajesh Kumar & Rahman, Muhammad Sabbir & Sreedharan, V. Raja, 2023. "Big data analytics and artificial intelligence technologies based collaborative platform empowering absorptive capacity in health care supply chain: An empirical study," Journal of Business Research, Elsevier, vol. 154(C).
    2. Ana Cecilia Quiroga Gutierrez & Daniel J. Lindegger & Ala Taji Heravi & Thomas Stojanov & Martin Sykora & Suzanne Elayan & Stephen J. Mooney & John A. Naslund & Marta Fadda & Oliver Gruebner, 2023. "Reproducibility and Scientific Integrity of Big Data Research in Urban Public Health and Digital Epidemiology: A Call to Action," IJERPH, MDPI, vol. 20(2), pages 1-15, January.
    3. Basile, L.J. & Carbonara, N. & Panniello, U. & Pellegrino, R., 2024. "The role of big data analytics in improving the quality of healthcare services in the Italian context: The mediating role of risk management," Technovation, Elsevier, vol. 133(C).
    4. Barros, Oscar & Weber, Richard & Reveco, Carlos, 2021. "Demand analysis and capacity management for hospital emergencies using advanced forecasting models and stochastic simulation," Operations Research Perspectives, Elsevier, vol. 8(C).
    5. Gupta, Brij B. & Gaurav, Akshat & Kumar Panigrahi, Prabin, 2023. "Analysis of security and privacy issues of information management of big data in B2B based healthcare systems," Journal of Business Research, Elsevier, vol. 162(C).
    6. Natalia Menshutina & Elena Guseva & Diana Batyrgazieva & Igor Mitrofanov, 2021. "Information System for Selection of Conditions and Equipment for Mammalian Cell Cultivation," Data, MDPI, vol. 6(3), pages 1-17, February.
    7. Badiee, Aghdas & Moshtari, Mohammad & Berenguer, Gemma, 2024. "A systematic review of operations research and management science modeling techniques in the study of higher education institutions," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).
    8. Galetsi, Panagiota & Katsaliaki, Korina & Kumar, Sameer, 2022. "The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19," Social Science & Medicine, Elsevier, vol. 301(C).

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