IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i21p15476-d1271490.html
   My bibliography  Save this article

A Systematic Review of Literature on Sustaining Decision-Making in Healthcare Organizations Amid Imperfect Information in the Big Data Era

Author

Listed:
  • Glory Urekwere Orlu

    (Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia)

  • Rusli Bin Abdullah

    (Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
    Institute for Social Science Studies, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia)

  • Zeinab Zaremohzzabieh

    (Institute for Social Science Studies, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia)

  • Yusmadi Yah Jusoh

    (Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia)

  • Shahla Asadi

    (School of Computing & Engineering, University of Gloucestershire, Cheltenham GL50 2RH, UK)

  • Yousef A. M. Qasem

    (Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia)

  • Rozi Nor Haizan Nor

    (Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia)

  • Wan Mohd Haffiz bin Mohd Nasir

    (Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia)

Abstract

The significance of big data analytics (BDA) has benefited the health sector by leveraging the potential insights and capabilities of big data in decision making. However, every implementation of BDA within the healthcare field faces difficulties due to incomplete or flawed information that necessitates attention and resolution. The purpose of this systematic literature review is to accomplish two main objectives. Firstly, it aims to synthesize the various elements that contribute to imperfect information in BDA and their impact on decision-making processes within the healthcare sector. This involves identifying and analyzing the factors that can result in imperfect information in BDA applications. Secondly, the review intends to create a taxonomy specifically focused on imperfect information within the context of BDA in the health sector. The study conducted a systematic review of the literature, specifically focusing on studies written in English and published up until February 2023. We also screened and retrieved the titles, abstracts, and potentially relevant studies to determine if they met the criteria for inclusion. As a result, they obtained a total of 58 primary studies. The findings displayed that the presence of uncertainty, imprecision, vagueness, incompleteness, and complexity factors in BDA significantly impacts the ability to sustain effective decision-making in the healthcare sector. Additionally, the study highlighted that the taxonomy for imperfect information in BDA provides healthcare managers with the means to utilize suitable strategies essential for successful implementation when dealing with incomplete information in big data. These findings have practical implications for BDA service providers, as they can leverage the findings to attract and promote the adoption of BDA within the healthcare sector.

Suggested Citation

  • Glory Urekwere Orlu & Rusli Bin Abdullah & Zeinab Zaremohzzabieh & Yusmadi Yah Jusoh & Shahla Asadi & Yousef A. M. Qasem & Rozi Nor Haizan Nor & Wan Mohd Haffiz bin Mohd Nasir, 2023. "A Systematic Review of Literature on Sustaining Decision-Making in Healthcare Organizations Amid Imperfect Information in the Big Data Era," Sustainability, MDPI, vol. 15(21), pages 1-19, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15476-:d:1271490
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/21/15476/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/21/15476/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Galetsi, P. & Katsaliaki, K. & Kumar, S., 2019. "Values, challenges and future directions of big data analytics in healthcare: A systematic review," Social Science & Medicine, Elsevier, vol. 241(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wen-Jing Suo & Chai-Lee Goi & Mei-Teh Goi & Adriel K. S. Sim, 2022. "Factors Influencing Behavioural Intention to Adopt the QR-Code Payment: Extending UTAUT2 Model," International Journal of Asian Business and Information Management (IJABIM), IGI Global, vol. 13(2), pages 1-22, August.
    2. 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).
    3. Secundo, Giustina & Riad Shams, S.M. & Nucci, Francesco, 2021. "Digital technologies and collective intelligence for healthcare ecosystem: Optimizing Internet of Things adoption for pandemic management," Journal of Business Research, Elsevier, vol. 131(C), pages 563-572.
    4. 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).
    5. 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).
    6. Cherif, Emna & Bezaz, Nora & Mzoughi, Manel, 2021. "Do personal health concerns and trust in healthcare providers mitigate privacy concerns? Effects on patients’ intention to share personal health data on electronic health records," Social Science & Medicine, Elsevier, vol. 283(C).
    7. Dal Mas, Francesca & Massaro, Maurizio & Rippa, Pierluigi & Secundo, Giustina, 2023. "The challenges of digital transformation in healthcare: An interdisciplinary literature review, framework, and future research agenda," Technovation, Elsevier, vol. 123(C).
    8. Galetsi, Panagiota & Katsaliaki, Korina & Kumar, Sameer, 2023. "Exploring benefits and ethical challenges in the rise of mHealth (mobile healthcare) technology for the common good: An analysis of mobile applications for health specialists," Technovation, Elsevier, vol. 121(C).
    9. Siala, Haytham & Wang, Yichuan, 2022. "SHIFTing artificial intelligence to be responsible in healthcare: A systematic review," Social Science & Medicine, Elsevier, vol. 296(C).
    10. Carboni, Chiara & Wehrens, Rik & van der Veen, Romke & de Bont, Antoinette, 2022. "Conceptualizing the digitalization of healthcare work: A metaphor-based Critical Interpretive Synthesis," Social Science & Medicine, Elsevier, vol. 292(C).
    11. Daniele Piovani & Stefanos Bonovas, 2022. "Real World—Big Data Analytics in Healthcare," IJERPH, MDPI, vol. 19(18), pages 1-3, September.
    12. Wei-Chih Lu & I-Ching Tsai & Kuan-Chung Wang & Te-Ai Tang & Kuan-Chen Li & Ya-Ci Ke & Peng-Ting Chen, 2021. "Innovation Resistance and Resource Allocation Strategy of Medical Information Digitalization," Sustainability, MDPI, vol. 13(14), pages 1-20, July.
    13. 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.
    14. Cruz, Taylor M. & Paine, Emily Allen, 2021. "Capturing patients, missing inequities: Data standardization on sexual orientation and gender identity across unequal clinical contexts," Social Science & Medicine, Elsevier, vol. 285(C).
    15. Cruz, Taylor Marion, 2022. "The social life of biomedical data: Capturing, obscuring, and envisioning care in the digital safety-net," Social Science & Medicine, Elsevier, vol. 294(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15476-:d:1271490. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.