Exploring Factors Influencing Big Data and Analytics Adoption in Healthcare Management

2022 ◽  
pp. 1433-1449
Author(s):  
Sampson Abeeku Edu ◽  
Divine Q. Agozie
Keyword(s):  
Big Data ◽  
Data Management ◽  
Patient Management ◽  
Technological Development ◽  
Healthcare Management ◽  
Quality Cost ◽  
Healthcare Data ◽  
Management Quality ◽  
Factors Influencing ◽  
Quality Decision

Demand for improvement in healthcare management in the areas of quality, cost, and patient care has been on the upsurge because of technology. Incessant application and new technological development to manage healthcare data significantly led to leveraging on the use of big data and analytics (BDA). The application of the capabilities from BDA has provided healthcare institutions with the ability to make critical and timely decisions for patients and data management. Adopting BDA by healthcare institutions hinges on some factors necessitating its application. This study aims to identify and review what influences healthcare institutions towards the use of business intelligence and analytics. With the use of a systematic review of 25 articles, the study identified nine dominant factors driving healthcare institutions to BDA adoption. Factors such as patient management, quality decision making, disease management, data management, and promoting healthcare efficiencies were among the highly ranked factors influencing BDA adoption.

Exploring Factors Influencing Big Data and Analytics Adoption in Healthcare Management

2020 ◽  
pp. 413-428
Author(s):  
Sampson Abeeku Edu ◽  
Divine Q. Agozie
Keyword(s):  
Big Data ◽  
Data Management ◽  
Patient Management ◽  
Technological Development ◽  
Healthcare Management ◽  
Quality Cost ◽  
Healthcare Data ◽  
Management Quality ◽  
Factors Influencing ◽  
Quality Decision

Demand for improvement in healthcare management in the areas of quality, cost, and patient care has been on the upsurge because of technology. Incessant application and new technological development to manage healthcare data significantly led to leveraging on the use of big data and analytics (BDA). The application of the capabilities from BDA has provided healthcare institutions with the ability to make critical and timely decisions for patients and data management. Adopting BDA by healthcare institutions hinges on some factors necessitating its application. This study aims to identify and review what influences healthcare institutions towards the use of business intelligence and analytics. With the use of a systematic review of 25 articles, the study identified nine dominant factors driving healthcare institutions to BDA adoption. Factors such as patient management, quality decision making, disease management, data management, and promoting healthcare efficiencies were among the highly ranked factors influencing BDA adoption.

scholarly journals Healthcare Data Management and Analytics using Big Data Tools

2019 ◽  
Vol 8 (12) ◽  
pp. 3725-3728
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Management ◽  
Data Analytics ◽  
Data Cleaning ◽  
Health Sector ◽  
Big Data Analytics ◽  
Heterogeneous Information ◽  
Healthcare Data ◽  
Structured Information

Data have been expanding enormously in latest years, enormous amounts of structured, unstructured and semi-structured information have been produced in various areas around the globe, collectively known as big data. The health sector has produced enormous amounts of heterogeneous information that must be handled and analyzed. In this paper, we discuss about the characteristics of data generated by healthcare and how to manage this data using big data tools. We also explore tools to analyse this data and discuss the implementations of this data. A conceptual architecture of big data analytics is also given, which includes data cleaning, data injection, data management, data mining, data visualization and data analysis.

scholarly journals DaLiF: a data lifecycle framework for data-driven governments

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Syed Iftikhar Hussain Shah ◽  
Vassilios Peristeras ◽  
Ioannis Magnisalis
Keyword(s):  
Big Data ◽  
Data Management ◽  
Good Governance ◽  
Business Community ◽  
Data Driven ◽  
Governmental Organizations ◽  
Data Lifecycle ◽  
The Government ◽  
Data Ecosystem ◽  
Governance Transparency

AbstractThe public sector, private firms, business community, and civil society are generating data that is high in volume, veracity, velocity and comes from a diversity of sources. This kind of data is known as big data. Public Administrations (PAs) pursue big data as “new oil” and implement data-centric policies to transform data into knowledge, to promote good governance, transparency, innovative digital services, and citizens’ engagement in public policy. From the above, the Government Big Data Ecosystem (GBDE) emerges. Managing big data throughout its lifecycle becomes a challenging task for governmental organizations. Despite the vast interest in this ecosystem, appropriate big data management is still a challenge. This study intends to fill the above-mentioned gap by proposing a data lifecycle framework for data-driven governments. Through a Systematic Literature Review, we identified and analysed 76 data lifecycles models to propose a data lifecycle framework for data-driven governments (DaliF). In this way, we contribute to the ongoing discussion around big data management, which attracts researchers’ and practitioners’ interest.

Revisiting the Factors Influencing Teaching Choice Framework: Exploring What Fits with Virtual Teaching

2021 ◽  
pp. 097215092110153
Author(s):  
Sudhir Rana ◽  
Amit Kumar Singh ◽  
Shubham Singhania ◽  
Shubhangi Verma ◽  
Moon Moon Haque
Keyword(s):  
Higher Education ◽  
Technological Development ◽  
Learning Experience ◽  
New Technology ◽  
Business Management ◽  
Teaching Efficacy ◽  
Influential Factors ◽  
Cross Sectional ◽  
Factors Influencing ◽  
Virtual Teaching

The present study revisits the Factors Influencing Teaching Choice (FIT-Choice) framework and explores what motivates business management academicians in teaching virtually. The revisit is based on a quantitative cross-sectional research design using 256 responses collected from in-service business management academicians teaching post-graduate business courses in India, through a structured questionnaire. The exercise of revisiting the FIT-Choice framework in the context of virtual teaching in business management courses led us to find four new variables, that is, task demand and expert career, teaching efficacy, knowledge assimilation and institutional utility value, as well as suggest revising teaching and learning experience, task returns and values. The results reveal that some additional factors motivating business academicians are teaching efficacy, content expertise, learning of new technology, futuristic growth and opportunities, alternative career opportunities and personal branding. The study provides suggestions to the apex bodies, regulators of higher education and institutions to take a call on motivational and influential factors while drafting the job requirements in business schools. Finally, the study emphasizes the importance of infrastructural and technological development required to be achieved by higher education institutions.

scholarly journals Big Data Analytics in urology: the story so far and the road ahead

2021 ◽  
Vol 13 ◽  
pp. 175628722199813
Author(s):  
B. M. Zeeshan Hameed ◽  
Aiswarya V. L. S. Dhavileswarapu ◽  
Nithesh Naik ◽  
Hadis Karimi ◽  
Padmaraj Hegde ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Early Stage ◽  
Big Data Analytics ◽  
Positive Trend ◽  
The Road ◽  
Healthcare Data ◽  
Urological Disorders ◽  
Different Sources

Artificial intelligence (AI) has a proven record of application in the field of medicine and is used in various urological conditions such as oncology, urolithiasis, paediatric urology, urogynaecology, infertility and reconstruction. Data is the driving force of AI and the past decades have undoubtedly witnessed an upsurge in healthcare data. Urology is a specialty that has always been at the forefront of innovation and research and has rapidly embraced technologies to improve patient outcomes and experience. Advancements made in Big Data Analytics raised the expectations about the future of urology. This review aims to investigate the role of big data and its blend with AI for trends and use in urology. We explore the different sources of big data in urology and explicate their current and future applications. A positive trend has been exhibited by the advent and implementation of AI in urology with data available from several databases. The extensive use of big data for the diagnosis and treatment of urological disorders is still in its early stage and under validation. In future however, big data will no doubt play a major role in the management of urological conditions.

FDM: Fuzzy-Optimized Data Management Technique for Improving Big Data Analytics

2021 ◽  
Vol 29 (1) ◽  
pp. 177-185
Author(s):  
Gunasekaran Manogaran ◽  
P. Mohamed Shakeel ◽  
S. Baskar ◽  
Ching-Hsien Hsu ◽  
Seifedine Nimer Kadry ◽  
...  
Keyword(s):  
Big Data ◽  
Data Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Management Technique
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