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

被引:3
作者
Orlu, Glory Urekwere [1 ]
Bin Abdullah, Rusli [1 ,2 ]
Zaremohzzabieh, Zeinab [2 ]
Jusoh, Yusmadi Yah [1 ]
Asadi, Shahla [3 ]
Qasem, Yousef A. M. [1 ]
Nor, Rozi Nor Haizan [1 ]
Nasir, Wan Mohd Haffiz bin Mohd [1 ]
机构
[1] Univ Putra Malaysia, Fac Comp Sci & Informat Technol, Serdang 43400, Selangor, Malaysia
[2] Univ Putra Malaysia, Inst Social Sci Studies, Serdang 43400, Selangor, Malaysia
[3] Univ Gloucestershire, Sch Comp & Engn, Cheltenham GL50 2RH, England
关键词
big data analytics; decision-making sustainability; healthcare organizations; imperfect information; DATA ANALYTICS; UNCERTAINTY;
D O I
10.3390/su152115476
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:19
相关论文
共 88 条
[1]   A Group Decision Making Framework Based on Neutrosophic TOPSIS Approach for Smart Medical Device Selection [J].
Abdel-Basset, Mohamed ;
Manogaran, Gunasekaran ;
Gamal, Abduallah ;
Smarandache, Florentin .
JOURNAL OF MEDICAL SYSTEMS, 2019, 43 (02)
[2]   Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991-2020) [J].
Alizadehsani, Roohallah ;
Roshanzamir, Mohamad ;
Hussain, Sadiq ;
Khosravi, Abbas ;
Koohestani, Afsaneh ;
Zangooei, Mohammad Hossein ;
Abdar, Moloud ;
Beykikhoshk, Adham ;
Shoeibi, Afshin ;
Zare, Assef ;
Panahiazar, Maryam ;
Nahavandi, Saeid ;
Srinivasan, Dipti ;
Atiya, Amir F. ;
Acharya, U. Rajendra .
ANNALS OF OPERATIONS RESEARCH, 2024, 339 (03) :1077-1118
[3]   ''You Get Reminded You're a Sick Person": Personal Data Tracking and Patients With Multiple Chronic Conditions [J].
Ancker, Jessica S. ;
Witteman, Holly O. ;
Hafeez, Baria ;
Provencher, Thierry ;
Van de Graaf, Mary ;
Wei, Esther .
JOURNAL OF MEDICAL INTERNET RESEARCH, 2015, 17 (08)
[4]   Big Data for Health [J].
Andreu-Perez, Javier ;
Poon, Carmen C. Y. ;
Merrifield, Robert D. ;
Wong, Stephen T. C. ;
Yang, Guang-Zhong .
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2015, 19 (04) :1193-1208
[5]  
Bag S., 2021, IEEE T ENG MANAGE, DOI DOI 10.1109/TEM.2021.3101590
[6]   R-Ensembler: A greedy rough set based ensemble attribute selection algorithm with kNN imputation for classification of medical data [J].
Bania, Rubul Kumar ;
Halder, Anindya .
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2020, 184
[7]   Toward a Literature-Driven Definition of Big Data in Healthcare [J].
Baro, Emilie ;
Degoul, Samuel ;
Beuscart, Regis ;
Chazard, Emmanuel .
BIOMED RESEARCH INTERNATIONAL, 2015, 2015
[8]  
Basha S.M., 2019, Int. J. Comput. Intell. Control, V11, P235
[9]   Why policymakers should care about "big data" in healthcare [J].
Bates, David W. ;
Heitmueller, Axel ;
Kakad, Meetali ;
Saria, Suchi .
HEALTH POLICY AND TECHNOLOGY, 2018, 7 (02) :211-216
[10]  
Belle A, 2015, BIOMED RES INT, V2015, DOI [10.1155/2015/634108, 10.1155/2015/370194]