Strategic issues of big data analytics applications for managing health-care sector: a systematic literature review and future research agenda

Abstract

PurposeThe proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.Design/methodology/approachFora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.FindingsBD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.Research limitations/implicationsThe proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.Originality/valueThere are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.

Towards the Use of Big Data in Healthcare: A Literature Review

“…The evolution of digital healthcare into mobile healthcare (mobile health) has also made it possible to manage information via apps that a patient can download directly to their smartphone or tablet [ 89 ], allowing them to monitor their health status independently and share it with their doctor [ 44 , 90 ]. Therefore, IoT allows a much better and timely diagnosis of the patient’s status and offers medical services via telemedicine, even in remote locations [ 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 ] still underdeveloped, where the number of specialists in health services is insufficient [ 92 ]. The use of telemedicine is of fundamental importance in this SARS-CoV-2 pandemic period for preventing and managing COVID-19 infection [ 93 ].…”

“…Enhancing decision-making and operational capabilities, reducing errors, and saving resources are the key benefits. In this view, BD is proving to be an important source with new characteristics, potential, and limitations [ 60 , 61 ].…”

Medical Big Data Risk Management: A Systematic Management Approach Based on Bayesian Belief Networks

“…On the legal side, regulatory policies regarding big data healthcare are changing due to the dynamic nature of laws [26], while relevant laws need to be further improved and there is a lack of efective protection of patients' or users' privacy [28]. Te policy aspects mainly include the structure of big data medical policy form, the responsibility and power of policy subjects, and the lack of detailed policies of big data medical [29]. In terms of safety regulation, there are problems such as no unifed authoritative national standards for smart medical safety regulation and no specialized organization for reviewing smart medical devices [26].…”

“…And based on the competitive nature of the information technology market, electronic medical record providers may be reluctant to provide data to big data software providers [32]. In addition, medical information systems are mainly concerned with issues such as medical data transmission and medical data security, which are proftable for hackers, who may be driven by proft to illegally hack into medical information systems and steal patients' personal health data [29]. Te MBDR dimension system is shown in Table 1.…”

A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy

“…In the recent past 'Big Data' (Ciampi et al 2020;Günther et al 2017;Mikalef et al 2016Mikalef et al , 2020aNelson and Olovsson 2016;Olszak and Mach-Król 2018;Wamba et al 2015;Zhao-hong et al 2018), 'Big Data and dynamic capabilities' (Rialti et al 2019), 'Big Data business models' (Huberty 2015;Wiener et al 2020), and 'Big Data assessment models' (Adrian et al 2016) have been explored. Researchers have also focused upon 'Big Data Analytics' (Adrian et al 2017;Al-Sai et al 2020;Bogdan and Lungescu 2018;Inamdar et al 2020;Maroufkhani et al 2019;Mikalef et al 2019a;Singh et al 2021;Sivarajah et al 2017), Artificial Intelligence (Alsheiabni et al 2020;Borges et al 2021;Keding 2020;Kitsios and Kamariotou 2021;Markus 2017), Business Analytics (Cao and Duan 2017), and Business Intelligence and Analytics (BI&A) (Chen et al 2012;Eggert and Alberts 2020;Lautenbach et al 2017;Llave 2017;Moreno et al 2019;Sang et al 2020).…”