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Big data analytics in health sector: Theoretical framework, techniques and prospects

Author

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  • Galetsi, Panagiota
  • Katsaliaki, Korina
  • Kumar, Sameer

Abstract

Clinicians, healthcare providers-suppliers, policy makers and patients are experiencing exciting opportunities in light of new information deriving from the analysis of big data sets, a capability that has emerged in the last decades. Due to the rapid increase of publications in the healthcare industry, we have conducted a structured review regarding healthcare big data analytics. With reference to the resource-based view theory we focus on how big data resources are utilised to create organization values/capabilities, and through content analysis of the selected publications we discuss: the classification of big data types related to healthcare, the associate analysis techniques, the created value for stakeholders, the platforms and tools for handling big health data and future aspects in the field. We present a number of pragmatic examples to show how the advances in healthcare were made possible. We believe that the findings of this review are stimulating and provide valuable information to practitioners, policy makers and researchers while presenting them with certain paths for future research.

Suggested Citation

  • Galetsi, Panagiota & Katsaliaki, Korina & Kumar, Sameer, 2020. "Big data analytics in health sector: Theoretical framework, techniques and prospects," International Journal of Information Management, Elsevier, vol. 50(C), pages 206-216.
  • Handle: RePEc:eee:ininma:v:50:y:2020:i:c:p:206-216
    DOI: 10.1016/j.ijinfomgt.2019.05.003
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    Citations

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    Cited by:

    1. Anwar, Muhammad Azfar & Zong, Zupan & Mendiratta, Aparna & Yaqub, Muhammad Zafar, 2024. "Antecedents of big data analytics adoption and its impact on decision quality and environmental performance of SMEs in recycling sector," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
    2. Imran Ali & Devika Kannan, 2022. "Mapping research on healthcare operations and supply chain management: a topic modelling-based literature review," Annals of Operations Research, Springer, vol. 315(1), pages 29-55, August.
    3. Surajit Bag & Gautam Srivastava & Anass Cherrafi & Ahad Ali & Rajesh Kumar Singh, 2024. "Data‐driven insights for circular and sustainable food supply chains: An empirical exploration of big data and predictive analytics in enhancing social sustainability performance," Business Strategy and the Environment, Wiley Blackwell, vol. 33(2), pages 1369-1396, February.
    4. Singha, Sumanta & Arha, Himanshu & Kar, Arpan Kumar, 2023. "Healthcare analytics: A techno-functional perspective," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    5. Ayan Chatterjee & Debmallya Chatterjee, 2024. "A Journey of Business Analytics in Improving Supply Chain Performance: A Systematic Review of Literature," Management and Labour Studies, XLRI Jamshedpur, School of Business Management & Human Resources, vol. 49(2), pages 337-361, May.
    6. 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).
    7. Sharma, Rohit & Jain, Geetika & Paul, Justin, 2023. "Does the world need to change its vaccine distribution strategy for COVID-19?," Technovation, Elsevier, vol. 126(C).
    8. K. Katsaliaki & P. Galetsi & S. Kumar, 2022. "Supply chain disruptions and resilience: a major review and future research agenda," Annals of Operations Research, Springer, vol. 319(1), pages 965-1002, December.

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