A Systematic Review of Knowledge Visualization Approaches Using Big Data Methodology for Clinical Decision Support (original) (raw)
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Big Data in Health and the Importance of Data Visualization Tools
Journal of Intelligent Systems with Applications
Big data concepts are increasing with their spatial speed, from personal information to extensive volume data. Since the human brain perceives visual data faster, the data must be processed and displayed appropriately. As in all areas of life, the size of the data obtained in the health sector has increased rapidly. Data storage and security have gained importance with the excessive increase in data. Big data, data mining, and visualization tools have become increasingly important to process and use data for valuation purposes. Therefore, the visualization of data and the use of analysis tools play a significant role in data processing and decision-making in the development of the health sector. The importance of data visualization tools in the health sector will become increasingly indispensable. There are many software tools developed for these purposes. This study's literature review explained the basic concepts of big data and data visualization. Research in the health secto...
Usability of Big Data Analytics Within Clinical Decision Support Systems
International Journal of Engineering Applied Sciences and Technology
The adoption of electronic health record systems and other digital technologies such as Magnetic Resonance Imaging (MRI) techniques, automated laboratory tests, and body sensors have brought the era of big data technology into the healthcare industry. The use of big data technologies has the potential to provide medical organizations with powerful tools to gather and analyze large data volumes and to use this information to their advantage. However, special skills, systems, and capabilities are required to be able to analyze and extract useful information from big data. The objective of this paper was to explore the literature regarding the usability of big data analytics in supporting medical decision making. This information will guide healthcare organizations in understanding how they can adopt the utilization of big data to enhance decision making. A systematic review of evidence-based research articles from within the past five years was used to gather information in regards to this topic. The articles were derived from scientific databases. Based on the literature review, big data and big data analytics has the capability to improve decision making in the healthcare sector, predict disease outbreaks as well as the trends and patterns of the spread of such diseases, predict occurrence of medical phenomenon's such as hospital readmission, reoccurrence of diseases, and risk of infection among others. Moreover, big data analytics has the capability to help healthcare organizations to streamline processes within the healthcare setting. However, the process of integrating big data analytics in the healthcare setting follows distinct phases. Healthcare organizations also have to consider the challenges associated with adopting big data analytics. Nevertheless, based on the literature, big data analytics has the capability to improve delivery systems and outcomes within the healthcare sector.
The Challenge of Big Data in Public Health: An Opportunity for Visual Analytics
Public health (PH) data can generally be characterized as big data. The efficient and effective use of this data determines the extent to which PH stakeholders can sufficiently address societal health concerns as they engage in a variety of work activities. As stakeholders interact with data, they engage in various cognitive activities such as analytical reasoning, decision-making, interpreting, and problem solving. Performing these activities with big data is a challenge for the unaided mind as stakeholders encounter obstacles relating to the data’s volume, variety, velocity, and veracity. Such being the case, computer-based information tools are needed to support PH stakeholders. Unfortunately, while existing computational tools are beneficial in addressing certain work activities, they fall short in supporting cognitive activities that involve working with large, heterogeneous, and complex bodies of data. This paper presents visual analytics (VA) tools, a nascent category of computational tools that integrate data analytics with interactive visualizations, to facilitate the performance of cognitive activities involving big data. Historically, PH has lagged behind other sectors in embracing new computational technology. In this paper, we discuss the role that VA tools can play in addressing the challenges presented by big data. In doing so, we demonstrate the potential benefit of incorporating VA tools into PH practice, in addition to highlighting the need for further systematic and focused research.
Journal of the American Medical Informatics Association, 2018
ObjectiveThis article reports results from a systematic literature review related to the evaluation of data visualizations and visual analytics technologies within the health informatics domain. The review aims to (1) characterize the variety of evaluation methods used within the health informatics community and (2) identify best practices.MethodsA systematic literature review was conducted following PRISMA guidelines. PubMed searches were conducted in February 2017 using search terms representing key concepts of interest: health care settings, visualization, and evaluation. References were also screened for eligibility. Data were extracted from included studies and analyzed using a PICOS framework: Participants, Interventions, Comparators, Outcomes, and Study Design.ResultsAfter screening, 76 publications met the review criteria. Publications varied across all PICOS dimensions. The most common audience was healthcare providers (n = 43), and the most common data gathering methods we...
A Bibliometric Analysis and Visualization of Medical Big Data Research
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Visual Analytics to Support Medical Decision-Making Process
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Examining Big Data in Medicine: Applications, Challenges and Benefits
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Advances in technology have created and will continue to create changes in medical science and practice. In recent years, the medical community has seen massive amount of information, which is collectively known as "big data." Literatures from various sources were reviewed to explore the application of data analytics in medicine, the benefits and challenges associated with its use. With recent advances in digital technology, the ability to gather and examine big data has become far cheaper and faster. With the promise of data-driven knowledge and intelligent decision-making, big-data projects are going on in every branch of medicine. However, the process of engaging stakeholders to benefits from the information provided by big data remain a major issue. This paper presents how big data and big data analytics are used in medicine.
Big Data in Healthcare Management: A Review of Literature
American Journal of Theoretical and Applied Business, 2018
A systematic literature review of papers on big data in healthcare published between 2010 and 2015 was conducted. This paper reviews the definition, process, and use of big data in healthcare management. Unstructured data are growing very faster than semi-structured and structured data. 90 percentages of the big data are in a form of unstructured data, major steps of big data management in healthcare industry are data acquisition, storage of data, managing the data, analysis on data and data visualization. Recent researches targets on big data visualization tools. In this paper the authors analysed the effective tools used for visualization of big data and suggesting new visualization tools to manage the big data in healthcare industry. This article will be helpful to understand the processes and use of big data in healthcare management.
Informatics for Health and Social Care, 2022
Data visualization tools have the potential to support decision-making for public health professionals. This review summarizes the science and evidence regarding data visualization and its impact on decision-making behavior as informed by cognitive processes such as understanding, attitude, or perception.An electronic literature search was conducted using six databases, including reference list reviews. Search terms were pre-defined based on research questions.Sixteen studies were included in the final analysis. Data visualization interventions in this review were found to impact attitude, perception, and decision-making compared to controls. These relationships between the interventions and outcomes appear to be explained by mediating factors such as perceived trustworthiness and quality, domain-specific knowledge, basic beliefs shared by social groups, and political beliefs.Visualization appears to bring advantages by increasing the amount of information delivered and decreasing the cognitive and intellectual burden to interpret information for decision-making. However, understanding data visualization interventions specific to public health leaders' decision-making is lacking, and there is little guidance for understanding a participant's characteristics and tasks. The evidence from this review suggests positive effects of data visualization can be identified, depending on the control of confounding factors on attitude, perception, and decision-making.