scholarly journals Big Data Analytics in Healthcare

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Ashwin Belle ◽  
Raghuram Thiagarajan ◽  
S. M. Reza Soroushmehr ◽  
Fatemeh Navidi ◽  
Daniel A. Beard ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Care Delivery ◽  
Healthcare Delivery ◽  
Big Data Analytics ◽  
Healthcare Systems ◽  
Medical Data ◽  
Unstructured Data ◽  
Process Of Care ◽  
Multimodal Data

The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.

scholarly journals Big Data Analytics for Improved Care Delivery in the Healthcare Industry

2019 ◽  
Vol 15 (10) ◽  
pp. 40
Author(s):  
Gunasekar Thangarasu ◽  
Kayalvizhi Subramanian
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Care Delivery ◽  
Big Data Analytics ◽  
Healthcare Services ◽  
Healthcare Systems ◽  
Big Data Analysis ◽  
Unstructured Data ◽  
Effective Practices ◽  
Healthcare Industry

<p class="0abstract">The big data analytics plays a pivotal role in the field of healthcare services and research to facilitate better service to the patients. It has provided tools to accumulate, manage, analysis the structured and unstructured data produced by the healthcare systems. Recently the utilization of big data analytics has been increased in the healthcare industry for assisting the process of diagnosing diseases and care delivery. However, the adoption and research development of big data analysis in the healthcare industry is still slow down due to facing some fundamental problems inherent within the big data paradigm. In this study, addresses these problems which focus on the upcoming and promising areas of medical research and proposed a novel big data analytics approach using Apache Spark. The proposed approach will improve care delivery in the healthcare industry. Big data analytics can continually evaluate clinical data in order to improve the effective practices of physicians and improved patient care.</p>

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.

Architecture of modern platforms for big data analytics

2021 ◽  
pp. 67-74
Author(s):  
Liudmyla Zubyk ◽  
Yaroslav Zubyk
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Business Processes ◽  
Service Providers ◽  
Big Data Analytics ◽  
Business Value ◽  
Third Party ◽  
Unstructured Data ◽  
Business Decision ◽  
World Industry

Big data is one of modern tools that have impacted the world industry a lot of. It also plays an important role in determining the ways in which businesses and organizations formulate their strategies and policies. However, very limited academic researches has been conducted into forecasting based on big data due to the difficulties in capturing, collecting, handling, and modeling of unstructured data, which is normally characterized by it’s confidential. We define big data in the context of ecosystem for future forecasting in business decision-making. It can be difficult for a single organization to possess all of the necessary capabilities to derive strategic business value from their findings. That’s why different organizations will build, and operate their own analytics ecosystems or tap into existing ones. An analytics ecosystem comprising a symbiosis of data, applications, platforms, talent, partnerships, and third-party service providers lets organizations be more agile and adapt to changing demands. Organizations participating in analytics ecosystems can examine, learn from, and influence not only their own business processes, but those of their partners. Architectures of popular platforms for forecasting based on big data are presented in this issue.

Big Data Analytics in the Healthcare Industry

2020 ◽  
pp. 160-178
Author(s):  
Arulkumar Varatharajan ◽  
Selvan C. ◽  
Vimalkumar Varatharajan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Human Services ◽  
Big Data Analytics ◽  
Healthcare Industry ◽  
Multimodal Data ◽  
Design Structure ◽  
Costs Of Treatment ◽  
The Way

Big Data has changed the way we manage, analyze and impact the data information in any industry. A champion among the most promising zones where it will, in general, be associated with takeoff progress is therapeutic medicinal administrations. Administration examinations can diminish costs of treatment, foresee flare-ups of pestilences, keep up a key separation from preventable diseases and improve individual fulfillment overall. The chapter depicts the beginning field of a huge information investigation in human services, talks about the advantages, diagrams a design structure and approach, portrays models revealed in the writing, quickly examines the difficulties, and offers ends. A continuous examination which targets the utilization of tremendous volumes of remedial data information while combining multimodal data information from various sources is discussed. Potential locales of research inside this field which can give noteworthy impact on medicinal administrations movement are in like manner dissected.

scholarly journals SIDeKa: The Role of Information Technology for Knowledge Creation

2017 ◽  
Vol 2 (6) ◽  
pp. 570
Author(s):  
Cungki Kusdarjito
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Data Collection ◽  
Knowledge Creation ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Unstructured Data ◽  
Village Level ◽  
The Village

The advancement of big data analytics is paving the way for knowledge creation based on very huge and unstructured data. Currently, information is scattered and growth tremendously, containing many information but difficult to be interpreted. Consequently, traditional approaches are no longer suitable for unstructured data but very rich in information. This situation is different from the role of previous information technology in which information is based on structured data, stored in the local storage, and in more advanced form, information can be retrieved through internet. Meanwhile, in Indonesia data are collected by many institutions with different measurement standard. The nature of the data collection is top-down, carried out by survey which is expensive yet unreliable and stored exclusively by respective institution. SIDeKa (Sistem Informasi Desa dan Kawasan/Village and Regional Information System), which are connected nationally, is proposed as a system of data collection in the village level and prepared by local people. Using SIDeKa, data reliability and readiness can be improved at the local level. The goals of the SIDeKa is not only local people have information in their hand such as poverty level, production, commodity price, the area of cultivated land, and the outbreak of diseases in their village, but also they have information from the neighboring villages or event at the national level. For government, data reliability will improve the policy effectiveness. This paper discusses the implementation and role of SIDeKa for knowledge creation in the village level, especially for the agricultural activities which has been initiated in 2015.Keywords: big data analytics; SIDeKa;  unstructured data.

Towards a Better and Smarter Healthcare and IoT

2019 ◽  
pp. 191-228
Author(s):  
Navin Kumar
Keyword(s):  
Big Data ◽  
Quality Of Care ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Systems ◽  
Healthcare Organizations ◽  
Healthcare Data ◽  
Value Based Care ◽  
Rich Data

The amount of healthcare data continues to exponentially grow everyday. The complexity of this data further limits the analytical capabilities of traditional healthcare systems. With value-based care, it is far more imminent for healthcare organizations to control the costs and to improve the quality of care in order to sustain their business. The purpose of the chapter is to gain insights into complexities and challenges that exist in current healthcare systems and how big data analytics and IoT can play a pivotal role to positively influence the quality of care and patient outcomes. The chapter also provides solutions and strategies for building cloud-based data asset that can deliver rich data analytics to both the healthcare systems and the patients.

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