A systematic review of literature on sustaining decision-making in healthcare organizations amid imperfect information in the Big Data era

Orlu, Glory Urekwere, Abdullah, Rusli Bin, Zaremohzzabieh, Zeinab, Jusoh, Yusmadi Yah, Asadi, Shahla ORCID: 0000-0002-8199-2122, Qasem, Yousef A. M., Nor, Rozi Nor Haizan and Mohd Nasir, Wan Mohd Haffiz bin (2023) A systematic review of literature on sustaining decision-making in healthcare organizations amid imperfect information in the Big Data era. Sustainability, 15 (21). p. 15476. doi:10.3390/su152115476

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first_pagesettingsOrder Article Reprints Open AccessSystematic Review A Systematic Review of Literature on Sustaining Decision-Making in Healthcare Organizations Amid Imperfect Information in the Big Data Era by Glory Urekwere Orlu 1ORCID,Rusli Bin Abdullah 1,2,*ORCID,Zeinab Zaremohzzabieh 2,Yusmadi Yah Jusoh 1ORCID,Shahla Asadi 3ORCID,Yousef A. M. Qasem 1ORCID,Rozi Nor Haizan Nor 1 andWan Mohd Haffiz bin Mohd Nasir 1 1 Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia 2 Institute for Social Science Studies, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia 3 School of Computing & Engineering, University of Gloucestershire, Cheltenham GL50 2RH, UK * Author to whom correspondence should be addressed. Sustainability 2023, 15(21), 15476; https://doi.org/10.3390/su152115476 Received: 1 June 2023 / Revised: 12 July 2023 / Accepted: 13 July 2023 / Published: 31 October 2023 Downloadkeyboard_arrow_down Browse Figures Versions Notes 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.

Item Type: Article
Article Type: Article
Uncontrolled Keywords: Big data analytics; Decision-making sustainability; Healthcare organizations; Imperfect information
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software > QA76.9 Other topics > QA76.9.B45 Big data
R Medicine > RA Public aspects of medicine
Z Bibliography. Library Science. Information Resources > ZA Information resources
Divisions: Schools and Research Institutes > School of Business, Computing and Social Sciences
Research Priority Areas: Applied Business & Technology
Depositing User: Rhiannon Goodland
Date Deposited: 09 Nov 2023 16:03
Last Modified: 16 May 2024 13:35
URI: https://eprints.glos.ac.uk/id/eprint/13419

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