scholarly journals Prospects of Big Data Analytics in Africa Healthcare System

2018 ◽  
Vol 10 (6) ◽  
pp. 114
Author(s):  
Akindele Akinnagbe ◽  
K.Dharini Amitha Peiris ◽  
Oluyemi Akinloye
Keyword(s):  
Big Data ◽  
Positive Impact ◽  
Research Question ◽  
Health Sector ◽  
Big Data Analytics ◽  
Healthcare Systems ◽  
Ethical Challenges ◽  
Big Data Applications ◽  
The World ◽  
The Impact

Big data is having a positive impact in almost every sphere of life, such as in military intelligence, space science, aviation, banking, and health. Big data is a growing force in healthcare. Even though healthcare systems in the developed world are recording some breakthroughs due to the application of big data, it is important to research the impact of big data in developing regions of the world, such as Africa. Healthcare systems in Africa are, in relative terms, behind the rest of the world. Platforms and technologies used to amass big data such as the Internet and mobile phones are already in use in Africa, thereby making big data applications to be emerging. Hence, the key research question we address is whether big data applications can improve healthcare in Africa especially during epidemics and through the public health system. In this study, a literature review is carried out, firstly to present cases of big data applications in healthcare in Africa, and secondly, to explore potential ethical challenges of such applications. This review will provide an update on the application of big data in the health sector in Africa that can be useful for future researchers and health care practitioners in Africa.

scholarly journals Prospects and Challenges of Population Health with Online and other Big Data in Africa

2019 ◽  
Vol 6 (1) ◽  
pp. 57-63 ◽  
Author(s):  
Rowland Edet ◽  
Bolarinwa Afolabi
Keyword(s):  
Health Care ◽  
Big Data ◽  
Population Health ◽  
Data Analytics ◽  
Healthcare Service ◽  
Positive Impact ◽  
Health Care Service ◽  
Big Data Analytics ◽  
Big Data Applications ◽  
The Impact

Big data analytics offers promises to many health care service challenges and can provide answers to many population health issues. Big data is having a positive impact in almost every sphere of life in more advanced world while developing countries are striving to meet up. Even though healthcare systems in the developed world are recording some breakthroughs due to the application of big data, it is important to research the impact of big data in developing regions of the world, such as Africa and identify its peculiar needs. The purpose of this review was to summarize the challenges faced by big data analytics and the prospects that big data opens in health care services in Africa. The systematic review examined the key research questions to address whether big data applications can improve healthcare service delivery in Africa especially during epidemics or health crises and through the population health system. The paper examined prospects and challenges that are associated with the use of big data and healthcare service in relation to population health needs through influencing factors. In this study, literatures are reviewed to present cases of big data applications in healthcare in Africa and to understand the prospect and challenges of such applications to population health.

Analysis of Big Data Analytics in Healthcare Sector: Applications and Tools

2020 ◽  
Vol 17 (12) ◽  
pp. 5605-5612
Author(s):  
A. Kaliappan ◽  
D. Chitra
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Health Sector ◽  
Big Data Analytics ◽  
High Volume ◽  
Healthcare Sector ◽  
Genome Database ◽  
Tremendous Amount ◽  
The Impact ◽  
Different Sources

In today’s world, an immense measure of information in the form of unstructured, semi-structured and unstructured is generated by different sources all over the world in a tremendous amount. Big data is the termed coined to address these enormous amounts of data. One of the major challenges in the health sector is handling a high-volume variety of data generated from diverse sources and utilizing it for the wellbeing of human. Big data analytics is one of technique designed to operate with monstrous measures of information. The impact of big data in healthcare field and utilization of Hadoop system tools for supervising the big data are deliberated in this paper. The big data analytics role and its theoretical and conceptual architecture include the gathering of diverse information’s such as electronic health records, genome database and clinical decisions support systems, text representation in health care industry is investigated in this paper.

An experimental investigation of the impact of using big data analytics on customers’ performance measurement

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marwa Rabe Mohamed Elkmash ◽  
Magdy Gamal Abdel-Kader ◽  
Bassant Badr El Din
Keyword(s):  
Big Data ◽  
Performance Measurement ◽  
Data Analytics ◽  
Positive Impact ◽  
Big Data Analytics ◽  
Unstructured Data ◽  
Future Research ◽  
Web Based ◽  
Content Type ◽  
The Impact

Purpose This study aims to investigate and explore the impact of big data analytics (BDA) as a mechanism that could develop the ability to measure customers’ performance. To accomplish the research aim, the theoretical discussion was developed through the combination of the diffusion of innovation theory with the technology acceptance model (TAM) that is less developed for the research field of this study. Design/methodology/approach Empirical data was obtained using Web-based quasi-experiments with 104 Egyptian accounting professionals. Further, the Wilcoxon signed-rank test and the chi-square goodness-of-fit test were used to analyze data. Findings The empirical results indicate that measuring customers’ performance based on BDA increase the organizations’ ability to analyze the customers’ unstructured data, decrease the cost of customers’ unstructured data analysis, increase the ability to handle the customers’ problems quickly, minimize the time spent to analyze the customers’ data and obtaining the customers’ performance reports and control managers’ bias when they measure customer satisfaction. The study findings supported the accounting professionals’ acceptance of BDA through the TAM elements: the intention to use (R), perceived usefulness (U) and the perceived ease of use (E). Research limitations/implications This study has several limitations that could be addressed in future research. First, this study focuses on customers’ performance measurement (CPM) only and ignores other performance measurements such as employees’ performance measurement and financial performance measurement. Future research can examine these areas. Second, this study conducts a Web-based experiment with Master of Business Administration students as a study’s participants, researchers could conduct a laboratory experiment and report if there are differences. Third, owing to the novelty of the topic, there was a lack of theoretical evidence in developing the study’s hypotheses. Practical implications This study succeeds to provide the much-needed empirical evidence for BDA positive impact in improving CPM efficiency through the proposed framework (i.e. CPM and BDA framework). Furthermore, this study contributes to the improvement of the performance measurement process, thus, the decision-making process with meaningful and proper insights through the capability of collecting and analyzing the customers’ unstructured data. On a practical level, the company could eventually use this study’s results and the new insights to make better decisions and develop its policies. Originality/value This study holds significance as it provides the much-needed empirical evidence for BDA positive impact in improving CPM efficiency. The study findings will contribute to the enhancement of the performance measurement process through the ability of gathering and analyzing the customers’ unstructured data.

scholarly journals Does big data analytics influence frontline employees in services marketing?

2017 ◽  
Vol 23 (3) ◽  
pp. 623-644 ◽  
Author(s):  
Saradhi Motamarri ◽  
Shahriar Akter ◽  
Venkat Yanamandram
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Service Providers ◽  
Research Question ◽  
Big Data Analytics ◽  
Frontline Employees ◽  
Skill Levels ◽  
Content Type ◽  
Knowledge Intensive ◽  
The Impact

Purpose Big data analytics (BDA) helps service providers with customer insights and competitive information. It also empowers customers with insights about the relative merits of competing services. The purpose of this paper is to address the research question, “How does big data analytics enable frontline employees (FLEs) in effective service delivery?” Design/methodology/approach The research develops schemas to visualise service contexts that potentially benefit from BDA, based on the literature drawn from BDA and FLEs streams. Findings The business drivers for BDA and its level of maturity vary across firms. The primary thrust for BDA is to gain customer insights, resource optimisation and efficient operations. Innovative FLEs operating in knowledge intensive and customisable settings may realise greater value co-creation. Practical implications There exists a considerable knowledge gap in enabling the FLEs with BDA tools. Managers need to train, orient and empower FLEs to collaborate and create value with customer interactions. Service-dominant logic posits that skill asymmetry is the reason for service. So, providers need to enhance skill levels of FLEs continually. Providers also need to focus on market sensing and customer linking abilities of FLEs. Social implications Both firms and customers need to be aware of privacy and ethical concerns associated with BDA. Originality/value Knitting the BDA and FLEs research streams, the paper analyses the impact of BDA on service. The research by developing service typology portrays its interplay with the typologies of FLEs and BDA. The framework portrays the service contexts in which BD has major impact. Looking further into the future, the discussion raises prominent questions for the discipline.

scholarly journals Big data analytics in the health sector: challenges and potentials

2019 ◽  
Vol 24 (1) ◽  
pp. 23 ◽  
Author(s):  
Marina Jovanovic Milenkovic ◽  
Aleksandra Vukmirovic ◽  
Dejan Milenkovic
Keyword(s):  
Big Data ◽  
Healthcare System ◽  
Data Analytics ◽  
Research Question ◽  
Health Sector ◽  
Big Data Analytics ◽  
Health Data ◽  
Vital Role ◽  
Healthcare Sector ◽  
Health Data Analytics

Research Question: The introduction of the Big Data concept in the healthcare sector points to a major challenge and potential. Motivation: Our goal is to indicate the importance of analyzing and processing large amounts of data that go beyond the typical ways of storing and processing information. Тhе data have their own characteristics: volume, velocity and variety. There are different structures. Analysis of these data is possible with the Big Data concept. Its importance is most evident in the health sector, because the preservation of the health status of the population depends on adequate data analysis. Idea: The idea of the paper is that big health data analytics contributes to a better quality provision of health services. The process is more efficient and effective. Data: Health analytics suggests that more and more resources are being utilized globally. In order to achieve improvements, health analytics and Big data concepts play a vital role in overcoming the obstacles, working more efficiently and aiming at providing adequate medical care. Tools: The Big data concept will help identify patients with developed chronic diseases. Big data can identify outbreaks of flu or other epidemics in real time. In this way, they are managed by the healthcare system, reducing overall healthcare costs over time, and increasing revenues. Findings: A key policy challenge is to improve the outcomes of the healthcare system,  data collection and analysis, security, storage and transfers. Big data are the potential to improve quality of care, improve predictions of diseases, improve the treatment methods, reduce costs. Contribution: This paper points to the challenges and potentials of Big Health Data analytics and formulates good reasons to apply the Big Data concept in healthcare.

Navigating the Ethics of Big Data in Public Health

2019 ◽  
pp. 353-367
Author(s):  
Effy Vayena ◽  
Lawrence Madoff
Keyword(s):  
Public Health ◽  
Big Data ◽  
Credit Card ◽  
Health Sector ◽  
Public Health Research ◽  
Ethical Challenges ◽  
The Public ◽  
Health Community ◽  
Digital Disease Detection ◽  
The Public Sphere

“Big data,” which encompasses massive amounts of information from both within the health sector (such as electronic health records) and outside the health sector (social media, search queries, cell phone metadata, credit card expenditures), is increasingly envisioned as a rich source to inform public health research and practice. This chapter examines the enormous range of sources, the highly varied nature of these data, and the differing motivations for their collection, which together challenge the public health community in ethically mining and exploiting big data. Ethical challenges revolve around the blurring of three previously clearer boundaries: between personal health data and nonhealth data; between the private and the public sphere in the online world; and, finally, between the powers and responsibilities of state and nonstate actors in relation to big data. Considerations include the implications for privacy, control and sharing of data, fair distribution of benefits and burdens, civic empowerment, accountability, and digital disease detection.

scholarly journals Putting the world back to work: An expert system using big data and artificial intelligence in combating the spread of COVID-19 and similar contagious diseases

Work ◽  
2020 ◽  
Vol 67 (3) ◽  
pp. 557-572
Author(s):  
Said Tkatek ◽  
Amine Belmzoukia ◽  
Said Nafai ◽  
Jaafar Abouchabaka ◽  
Youssef Ibnou-ratib
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Expert System ◽  
Population Monitoring ◽  
High Definition ◽  
Contagious Diseases ◽  
The World ◽  
General Guide ◽  
The Impact ◽  
Back To Work

BACKGROUND: To combat COVID-19, curb the pandemic, and manage containment, governments around the world are turning to data collection and population monitoring for analysis and prediction. The massive data generated through the use of big data and artificial intelligence can play an important role in addressing this unprecedented global health and economic crisis. OBJECTIVES: The objective of this work is to develop an expert system that combines several solutions to combat COVID-19. The main solution is based on a new developed software called General Guide (GG) application. This expert system allows us to explore, monitor, forecast, and optimize the data collected in order to take an efficient decision to ensure the safety of citizens, forecast, and slow down the spread’s rate of COVID-19. It will also facilitate countries’ interventions and optimize resources. Moreover, other solutions can be integrated into this expert system, such as the automatic vehicle and passenger sanitizing system equipped with a thermal and smart High Definition (HD) cameras and multi-purpose drones which offer many services. All of these solutions will facilitate lifting COVID-19 restrictions and minimize the impact of this pandemic. METHODS: The methods used in this expert system will assist in designing and analyzing the model based on big data and artificial intelligence (machine learning). This can enhance countries’ abilities and tools in monitoring, combating, and predicting the spread of COVID-19. RESULTS: The results obtained by this prediction process and the use of the above mentioned solutions will help monitor, predict, generate indicators, and make operational decisions to stop the spread of COVID-19. CONCLUSIONS: This developed expert system can assist in stopping the spread of COVID-19 globally and putting the world back to work.

Big Data and Sustainable Consumption: A Review and Research Agenda

2021 ◽  
pp. 097226292110225
Author(s):  
Shobhana Chandra ◽  
Sanjeev Verma
Keyword(s):  
Big Data ◽  
Consumer Behaviour ◽  
Business Models ◽  
Sustainable Consumption ◽  
Big Data Analytics ◽  
Consumption Behaviour ◽  
Consumer Experiences ◽  
Systematic Understanding ◽  
The Impact

Big data (BD) is making advances in promoting sustainable consumption behaviour and has attracted the attention of researchers worldwide. Despite the increased focus, the findings of studies on this topic are fragmented, and future researchers need a systematic understanding of the existing literature for identification of the research scope. This study offers a systematic review of the role of BD in promoting sustainable-consumption behaviour with the help of a bibliometric analysis, followed by a thematic analysis. The findings suggest that businesses deploy BD to create sustainable consumer experiences, predict consumer buying patterns, design and alter business models and create nudges for sustainable consumption, while consumers are forcing businesses to develop green operations and supply chains to reduce the latter’s carbon footprint. The major research gaps for future researchers are in the following areas: the impact of big data analytics (BDA) on consumerism, the role of BD in the formation of sustainable habits and consumer knowledge creation for sustainable consumption and prediction of green consumer behaviour.

scholarly journals Increased Emergency Calls during the COVID-19 Pandemic in Saudi Arabia: A National Retrospective Study

Healthcare ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 14
Author(s):  
Ahmed Al-Wathinani ◽  
Attila J. Hertelendy ◽  
Sultana Alhurishi ◽  
Abdulmajeed Mobrad ◽  
Riyadh Alhazmi ◽  
...  
Keyword(s):  
Saudi Arabia ◽  
Retrospective Study ◽  
Emergency Medical Services ◽  
Indirect Effect ◽  
Mobile Application ◽  
Healthcare Systems ◽  
Emergency Calls ◽  
The World ◽  
Staffing Patterns ◽  
The Impact

The coronavirus 2019 (COVID-19) pandemic has a direct and indirect effect on the different healthcare systems around the world. In this study, we aim to describe the impact on the utilization of emergency medical services (EMS) in Saudi Arabia during the COVID-19 pandemic. We studied cumulative data from emergency calls collected from the SRCA. Data were separated into three periods: before COVID-19 (1 January–29 February 2020), during COVID-19 (1 March–23 April 2020), and during the Holy Month of Ramadan (24 April–23 May 2020). A marked increase of cases was handled during the COVID-19 period compared to the number before pandemic. Increases in all types of cases, except for those related to trauma, occurred during COVID-19, with all regions experiencing increased call volumes during COVID-19 compared with before pandemic. Demand for EMS significantly increased throughout Saudi Arabia during the pandemic period. Use of the mobile application ASAFNY to request an ambulance almost doubled during the pandemic but remained a small fraction of total calls. Altered weekly call patterns and increased call volume during the pandemic indicated not only a need for increased staff but an alteration in staffing patterns.

scholarly journals Big Data Analytics for Search Engine Optimization

2020 ◽  
Vol 4 (2) ◽  
pp. 5 ◽  
Author(s):  
Ioannis C. Drivas ◽  
Damianos P. Sakas ◽  
Georgios A. Giannakopoulos ◽  
Daphne Kyriaki-Manessi
Keyword(s):  
Big Data ◽  
Cultural Heritage ◽  
Search Engine ◽  
Data Analytics ◽  
User Behavior ◽  
Big Data Analytics ◽  
Data Types ◽  
Search Engine Optimization ◽  
The Impact ◽  
The Web

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.

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