Discussion: benefits and challenges associated with the use of Big Data Analytics in e-Healthcare industry

Discussion: benefits and challenges associated with the use of Big Data Analytics in e-Healthcare industry ORDER NOW FOR CUSTOMIZED AND ORIGINAL ESSAY PAPERS ON Discussion: benefits and challenges associated with the use of Big Data Analytics in e-Healthcare industry There are several benefits as well as challenges associated with the use of Big Data Analytics in the e-Healthcare industry. Pick one of the four concepts below and then identify the benefits and challenges associated with that concept. Do not simply list the benefits and challenges but detail them in a substantive, thorough post as it relates to that concept in the e-healthcare industry. Discussion: benefits and challenges associated with the use of Big Data Analytics in e-Healthcare industry 1. Data Gathering 2. Storage and Integration 3. Data Analysis 4. Knowledge Discovery and Information Interpretation Attached documents for reference Requirements: 1. Make your initial post with at least one scholarly reference. 2. Use information from your readings and other sources. Use proper citations and references in your post (scholarly references should match the content) 3. Need two response posts also 4. No Plagiarism a_systematic_review_of_big_data_potenial_to_make.pdf dash2019_article_bigdatainhealthcaremanagementa.pdf Review Applied Medical Informatics Vol. 41, No. 2 /2019, pp: 53-64 A Systematic Review of Big Data Potential to Make Synergies between Sciences for Achieving Sustainable Health: Challenges and Solutions Shirin AYANI1, Khadijeh MOULAEI2,*, Sarah DARWISH KHANEHSARI1, Maryam JAHANBAKHSH3, Faezeh SADEGHI4 Rayavaran Medical Informatics Company, Smart Hospital and Telemedicine Research Center, Tehran, Iran. 2 Iran University of Medical Sciences, School of Management and Medical Information, Khadijeh Moulaei, No.2, Corner of Hamsayegan Street, Valiasr Ave, Tehran, Iran. 3 Isfahan University of Medical Sciences, Isfahan, Iran. 4 Faran (Mehr Danesh) Non-governmental Institute of Virtual Higher Education, Department of English Language, Tehran, Iran. E-mail: Moulaei.kh@tak.iums.ac.ir 1 * Author to whom correspondence should be addressed; Tel: +982188783115; Fax: +982188661654 Received: January 9, 2019 /Accepted: May 16, 2019/ Published online: June 30, 2019 Abstract The importance of the healthcare industry, benefiting from the synergies between sciences, adds to the necessity of discovering knowledge, which is achievable with big data analytics tools. The purpose of this article is to examine the challenges and provide solutions for using big data in the healthcare industry. The methods of this article are derived from PRISMA guidelines and its models. A variety of databases and search engines including PubMed, Scopus, Elsevier, IEEE, Springer, Web of Science, Proquest, and Google Scholar were searched according to credible keywords. The results of the present study showed that the problems associated with the use of big data in the healthcare industry could be classified in four groups including “data gathering, storage and integration”, “data analysis”, “knowledge discovery and information interpretation”, and “infrastructure”. Although the results point to a high frequency of challenges in the “data gathering, storage and integration” group, the greatest weight of problems, due to their importance, appears to be visible in the “infrastructure” group. Considering the numerous benefits of using big data, it is imperative to identify the challenges and resolve them accurately. It is expected that all the barriers can be removed soon. Big data analytics tools will be able to offer the best possible strategies based on human individual and social conditions in the context of artificial intelligence methods. Keywords: Big data; Data analysis; Data integration; Internet Of Things (IOT); Medical informatics; Biological informatics Introduction At the end of the 1990s, in order to make the right decisions and gain a better understanding of market behaviors, the role of gathering data, integrating and interpreting business information was emphasized by the researchers. For this purpose, the term “Big Data” was introduced by Michael Cox and David Ellsworth in 1997 [1]. Big data is referred to as a set of data whose volume is beyond 53 [ Shirin AYANI, Khadijeh MOULAEI, Sarah DARWISH KHANEHSARI, Maryam JAHANBAKHSH, Faezeh SADEGHI the capabilities of current databases and technologies. Therefore, in order to analyze these data, databases with volume capabilities higher than terabyte and Exabyte are needed [2]. Big data separating factors from other data include volume (scale and size of data in storage), velocity (the speed in which this data is generated, produced, created, refreshed, and streamed), variety (multiple different forms of the data), veracity (uncertainty of the data that leads to confidence or trust in the data), and value (deriving business value and insights from the data) [3-6]. Discussion: benefits and challenges associated with the use of Big Data Analytics in e-Healthcare industry Additionally, the outcomes of big data analyses contribute to the identification of unknown patterns that show the causal relationships between different events in a wide range of information in the real world and ends in knowledge production [7]. Before the introduction of the concept of big data, with the emergence of information revolutionary age, it was possible to collect the data associated with healthcare activities in related centers [8], and healthcare providers who exploited health information systems like hospital information systems (HISs) started generating massive data [9]. While the information systems were used, specialists’ level of expectation went up, and another need was formed: how to understand the multidimensional causal relationships associated with individual and social health. Simultaneously, big data was introduced to the world and health researchers showed interest in this field [10-12]. Big data in the healthcare industry refers to a set of data related to diagnosis and treatment of diseases, contagious diseases, nutritional status, climate, political status, security of a country (especially war conditions), cultural status, social system, regional and vernacular status, metabolism and micronutrients (ions), genetics and cells, the economic status, the insurance companies’ bills and other things [1, 10, 13-15]. To collect these data, equipment, and tools such as the Internet, smartphones, social media, sensors and databases – which are related to the scientific societies and hospitals- are used as well as clinical and hospital information systems [14, 16]. After gathering data, it was possible to discover unknown patterns associated with some features carried out by the use of advanced analytics tools. These features that are used by advanced analytics tools include individual and social disease management, changes in habits and pathogenic conditions, prevention, diagnosis and treatment of diseases especially rare diseases, forecasting, individualization of health services, support and supervision of health social services [14]. One of the valuable advantages of analyzing data in the healthcare industry is the knowledge discovery beyond researchers’ imagination, which ends in the successful medical decision making of healthcare providers and producing clinical decision support systems [3, 17, 18]. Analysis of this data is done by the use of particular computing technologies, which requires specific hardware structures and operating platforms. At this time, operating platforms, hardware structures, and advanced technologies for using big data are acceptably reachable. Extensible Markup Language (XML), Web Services, Database Management Systems, Hadoop, SAP HANA and analytical software, are their examples [1, 14, 19, 20]. On the other hand, because of the considerable data volume, it is impossible to store and transfer data using traditional methods and technologies. Today, SQL, MySQL, and Oracle databases are widely used for implementing information systems, while for storing large data, Apache and Nosql databases are required [1]. The big data formation is performed in three stages of collecting, processing and visualizing data [15, 21]. Each step is accompanied by significant challenges that prevent successful implementation of big data operationally. Therefore, the purpose of this study was to survey the applications of big data in the healthcare industry in order to increase the synergy, as well as achieve sustainable health to survey challenges and provide suitable solutions. Material and Method The methods of this article are derived from PRISMA guidelines and its models (for further information see www.prisma-statement.org). 54 Appl Med Inform 41(2) June/2019 A Systematic Review of Big Data Potential to Make Synergies between Sciences for Achieving Sustainable Health: Challenges and Solutions Information Resources A variety of databases and search engines including Pubmed, Scopus, Elsevier, IEEE, Springer, Web of Science, Proquest and Google Scholar were searched according to credible keywords and the pre-specified search strategy mentioned below. Discussion: benefits and challenges associated with the use of Big Data Analytics in e-Healthcare industry The databases were searched from May 24, 2018, to July 30, 2018. Keywords and Search Strategy The keywords of this research used in the search strategy are as follows: Big Data, Data Sets, Big Data Analytics, Big Data Analytics Tools, Administrative Data, Structured Data, Unstructured Data, Business Process Analytics, Real-time Analytics, Information Technology Management, Health Care, Health Care Industries, E-Health Solutions, Social Health, Clinical Registries, Bioinformatics, Health Informatics, Medical Informatics, Sensor Informatics, Challenge, Solution, Problems. The applied search strategy was [Big data* AND (Business Process OR Data sets OR Real-time OR Administrative Data OR Unstructured Data OR Structured Data OR Information Technology Management OR Resource-based Theory) AND (Solutions* OR Challenges* OR Problems*) AND (Healthcare OR E-Health OR Social Health OR Public Health OR Clinical Registries OR Medical OR *Informatics OR Bioinformatics)]. Inclusion, Exclusion and Data Extraction The inclusion criteria were as follows: Full-text resources were available. The articles were published in the last 10 years. ? The articles were published in scientific and high-ranking journals. ? Big data challenges and their potential solutions in the healthcare industry were suggested. The exclusion criteria were as follows: ? The challenges were unrelated to the big data in the healthcare industry. ? The definitions were not clear and related to the challenges and their solutions. ? Concerning challenges and their solutions, the articles were not comprehensive. First, all the challenges introduced in the selected articles were extracted. The challenges were categorized into four groups: “data gathering, storage and integration”, “data analysis”, “knowledge discovery and information interpretation” and “infrastructure”. Then, to find or propose solutions for each challenge, the necessary examinations were carried out and solutions were put in different groups along with their related challenges. ? ? Results The search results are shown in Figure 1, and the results of these studies are illustrated in Table 1. In this table, the problems were grouped, and solutions to each problem were specified. Data Gathering, Storage and Integration Over the years, the volume of generated data has been increased significantly by healthcare organizations. These data are collected from various sources as well as by various tools and technologies such as information systems, cell phones, wireless sensors, RFID (radio frequency identification) and so on [22, 25]. Therefore, in order to create big data in the healthcare industry, heterogeneous sources and different formats are used [28]. For this reason, it is normal to face some problems such as noise, confounding factors, and inconsistencies in the gathered data collection [25]. Also, during data gathering, for some reasons such as inadequate storage space and gathering data from various sources, some valuable data can be possibly ignored or removed [22]. Ladha and his colleagues argued that lost data might cause the creation of invalid patterns. Therefore, three potential constraints in gathering data should be taken into account. First, some data [ Appl Med Inform 41(2) June/2019 55 Shirin AYANI, Khadijeh MOULAEI, Sarah DARWISH KHANEHSARI, Maryam JAHANBAKHSH, Faezeh SADEGHI may be missing or artificial. Discussion: benefits and challenges associated with the use of Big Data Analytics in e-Healthcare industry Second, in some cases, some found data could lead to the definition of ambiguous or contradictory variables. Third, some variables can act as confounding factors during the analysis. The measure of the error rate in each one of these three stages indicates the ineffectiveness of big data and presents the risk of its use by conducting scientific researches [11]. It is noteworthy that in order to avoid gathering data redundancy, it is necessary to identify and provide methods to prevent data abundance and redundant data storage [35]. Figure 1. Literature search criteria with inclusion and exclusion criteria 3.2. Data Analysis Analyzing big data, some errors occurring during the data gathering and those errors that were hidden in the databases were identified and corrected as much as possible. Current information systems in the healthcare industry are not integrated, so by gathering data in a big data repository, these errors can be identified [39]. Because of this and due to the lack of practical methods for accurate and rapid processing of massive volume of data, big data analysis brings up a critical issue [32]. However, since analyzing these health data leads to significant worthwhile outcomes, many researchers are trying to overcome these challenges [40, 41]. For example, Mathew and Pillai described the SAP HANA platform in data analysis as highly useful. SAP HANA utilizes some data mining methods for analyzing complex and large volumes of data[3]. It is also imperative to use some techniques such as networks, graphs, and charts for data analysis [42-44]. Note that if no pattern is extracted, it is necessary to re-formulate and repeat the analysis step[14]. The system analyst’s skill in recognizing the patterns, the definition of the rules, process modeling, error detection, and setting error threshold are important [45]. Evaluating the results of processing big data in order to validate the acquired patterns is also a significant challenge that must be done by professional multidisciplinary teams [1]. Another critical point is the need for a remarkable space of temporary memory that is accessible on unique hardware platforms while analyzing the data [13]. 56 Appl Med Inform 41(2) June/2019 A Systematic Review of Big Data Potential to Make Synergies between Sciences for Achieving Sustainable Health: Challenges and Solutions Table 1. Big data challenges and their solutions in the healthcare industry ID Challenge Challenge Group 1 Data gathering, Difficulty in gathering and integrating data storage and integration Lack of time priority in gathering data Data ambiguity 2 3 4 Solution Reference Creating distributed databases which are [2,22,23] interrelated and choosing a scientific manner to gather data from health centers nationally Having data gathering patterns so that data [11,18] interconnections are considered Using various scientific techniques to clarify [13] data and code them and defining flexible formats for high-clarity data gathering Heterogeneity of different Using semantic networks and ontological [10,24,25] sources of gathered data interpretations Artificial nature of some Using Loss data analysis and providing clear [11,26,27] data elements due to the definitions of variables loss of other relevant data Existence of noisy data Using preprocessing techniques (like PCA) [26] Massive data Centralized data monitoring, preventing [17,28,29] redundancy and filtering unnecessary data Data analysis Difficulty in analyzing big Using advanced computing technologies and [30,31] data processing parallelism Difficulty in extracting Using advanced data mining techniques to [13,23] patterns and models obtain valuable models and patterns Uncertainty about the Evaluating is done to confirm the accuracy and [13,23] accuracy of extracted value of the model information, patterns, and models Knowledge Interpretation of patterns Information interpretation after getting help [1] from experts in multidisciplinary or discovery and information interdisciplinary fields interpretation Difficulty in representing Defining flexible formats to represent the [23] knowledge interpreted information. Discussion: benefits and challenges associated with the use of Big Data Analytics in e-Healthcare industry The multidisciplinary or interdisciplinary specialists should document extracted knowledge Studying the The validity of explicit knowledge should be [26] generalizability of confirmed statistically and epidemiologically knowledge and the accuracy of explicit knowledge Infrastructure Absence of specific rules Defining a set of rules in the form of specific [13,32,33] and standards frameworks to achieve standards and to apply the rules in a correct way Immaturity of required Identifying and implementing modern [25,27,36] infrastructure technologies and then complying the existing infrastructure with them Lack of a stakeholder Making collective business policies for [32, 34] applicability of big data Data security (availability, Using advanced security standards and related [23,35,36] confidentiality, and technologies and then continuing the integrity] monitoring over data accuracy and quality Lack of proper bandwidth Using high bandwidth or special data transfer [26, 37] for data transfer protocols Lack of some important Applying information systems like transaction [38] information systems for processing systems, registration systems, and storing data digitally as in decision support systems Electronic Medical Records [ Appl Med Inform 41(2) June/2019 57 Shirin AYANI, Khadijeh MOULAEI, Sarah DARWISH KHANEHSARI, Maryam JAHANBAKHSH, Faezeh SADEGHI Knowledge Discovery and Information Interpretation One of the major challenges of big data is the interpretation of information and patterns after the analysis. At this point, the leading question is how new knowledge can be obtained from the aggregation of information; then how it can be documented, displayed, and verified [46]. Knowledge discovery during data analysis based on the Internet of Things is one of the biggest challenges that professionals encounter. Devices and software based on the Internet of Things generate substantial data streams, which result in the mass production of data. Researchers can use Artificial Intelligence and Machine Learning to interpret this information. Machine Learning algorithms and Intelligent Computing are considered achievable solutions for analyzing big data based on the Internet of Things [22, 47]. On the other hand, the simple representation of the knowledge extracted from big data is a serious issue. If it is not possible to demonstrate the novel knowledge, it is impossible to develop and apply it. Therefore, to eliminate the complexity of the discovered patterns and create relations between them, experts’ opinions in different subjects areas and from other perspectives are needed [35]. Identifying invalid patterns and accrediting extracted knowledge also needs multidisciplinary expert teams. Since a pattern may be taken from a specialized field to the others, it is not easy to perceive the relationships between them and to evaluate the results [35, 48]. Infrastructure Infrastructure refers to the use of a combination of hardware, software, and services that should be robust, supportive, and scalable [49]. The infrastructure of big data refers to all cases that can support its lifecycle on a large scale over time. In the infrastructure of big data, it is essential to be ensured of data security (including confidentiality, integrity, and availability) [50, 51). There are many challenges in healthcare that make it impossible to create a secure infrastructure; for example, high bandwidth is effective to the speed of data stream [32]. Communication channels based on highbandwidth help gathering and managing data and protecting its security [52]. On the other hand, storing and retrieving data from clinical and hospital information systems is a very complex, time-consuming, expensive endeavour, which requires a robust infrastructure. Databases and computing systems such as Mongo, Hadoop, and MapReduce can provide proper infrastructure for applying big data[53]. Concerning the characteristics of health data, the Mongo database can provide high performance; accessibility and scalability for big data. This can successfully create data repositories [54, 55]. The conceptual framework of big data analysis in the healthcare industry varies from the traditional data analysis frameworks; so, processing should be distributed and performed all across the nodes of the network. Therefore, instead of using a machine, the processing is broken down and carried out by variant machines, and their analytics can be performed in parallel with the help of MapReduce [56, 57]. Besides, open source platforms such as Hadoop, which operates in clo … Get a 10 % discount on an order above $ 100 Use the following coupon code : NURSING10

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