No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
A review on the impact of data visualization in the field of healthcare
ResearchGate has not been able to resolve any citations for this publication.
It is widely acknowledged that knowledge management is critical to an organization's survival and growth. Every day, higher education institutions that are considered knowledge centers generate massive volumes of data. When this data is analyzed using appropriate computational methods and technology, it can provide knowledge to improve organizational performance and students' academic experience. Healthcare organizations create massive volumes of data as a result of the usage of digital technologies to manage patient information and the organization's operations. When used successfully, this data aids in the creation of information that improves patient health and everyday organizational functioning, as well as the prevention of unfavorable public health scenarios such as the spread of infectious illnesses. This is where big data analytics comes in, providing rational methods for navigating enormous quantities of data to disclose knowledge that assists businesses and analysts in making faster and better decisions. Higher education, like healthcare, creates large amounts of heterogeneous data that hides useful knowledge. As a result, the strategies used by healthcare companies to improve their performance using big data are replicable in the education domain as well. This article examines the use of big data for knowledge management in healthcare using case studies incorporating various analytics and draws parallels to be applied in higher education. As a result, it highlights the possibility of adapting analytics technology and tools from healthcare to higher education with appropriate revisions and adaptations.
In the digital information world, visualizing information in public spaces has been implemented in various formats and for application contexts such as advertisement, useful information provision, and provision of critical information in the cases of accidents, natural disasters, etc. Among the different types of information displays, in this research work, the focus is given to the ones that extend the experience of people visiting cultural heritage institutions. To this end, the design and implementation of an interactive display case that aims to overcome the “non-touch policy” of museums are presented. This novel display allows visitors to get engaged with artifacts and information through touch-based interaction with the ambition to extend the target audience and impact of museum content. The conducted study demonstrates that the interactive display case is an effective solution for providing relevant information to visitors, enhancing their engagement with exhibits, and improving their overall experience. The proposed solution is user-friendly, engaging, and informative, making it ideal for museums and other public exhibit spaces.
This study aims to determine the dominant chemical elements that may improve the monitoring of the productivity and efficiency of heavy engines in 2015-2021 in the company. The method used is usually Scheduled Oil Sampling. This article proposes a new approach. The research problems are analyzing the recorded chemical elements that are produced by heavy engines and visualizing them through the Tableau program. The basic design of the study is learning the given data after visualization and using the Principal Component Analysis. This method is to obtain chemical elements that affect engine wear during each engine's use in the 2015-2021 period. Because there are three categories in each element in the oil sample, namely wear metals, contaminants, and oil additives, a technique is needed to obtain these elements using Principal Component Analysis. Therefore, Oil Sampling Analysis through data exploration using Tableau resulted in a new approach to data analysis of elements recorded by heavy vehicles. The main findings as a result of the analysis are given by the visualization of Tableau, in which there are five machines analyzed to obtain the main components that cause engine wear. From the visualization results, it is shown that there is one engine coded MSD 012 that experienced wear and tear in 2018 and 2019. This shows where two main components, Ca and Mg, dominate engine wear. These results have been confirmed with the related companies. The company then carried out further studies on the machine to get special treatment because of these results.
Biomedical databases often have restricted access policies and governance rules. Thus, an adequate description of their content is essential for researchers who wish to use them for medical research. A strategy for publishing information without disclosing patient-level data is through database fingerprinting and aggregate characterisations. However, this information is still presented in a format that makes it challenging to search, analyse, and decide on the best databases for a domain of study. Several strategies allow one to visualise and compare the characteristics of multiple biomedical databases. Our study focused on a European platform for sharing and disseminating biomedical data. We use semantic data visualisation techniques to assist in comparing descriptive metadata from several databases. The great advantage lies in streamlining the database selection process, ensuring that sensitive details are not shared. To address this goal, we have considered two levels of data visualisation, one characterising a single database and the other involving multiple databases in network-level visualisations. This study revealed the impact of the proposed visualisations and some open challenges in representing semantically annotated biomedical datasets. Identifying future directions in this scope was one of the outcomes of this work.
Nowadays, healthcare management systems are adopting various techniques that facilitate the achievement of the goals of evidence-based medical practice. This review explores different visualization techniques and their importance in healthcare contexts. We performed a thorough search on databases such as the SLD portal, PubMed, and Google Scholar to obtain relevant studies. We selected recent articles published between 2018 and 2021 on visualization techniques in healthcare. The field of healthcare generates massive volumes of data that require visualization techniques to make them easily comprehensible and to guide their efficient presentation. Visualization in healthcare involves the effective presentation of information through graphics, images, and videos. Big data systems handle a massive amount of information and require visualization techniques to present it in a comprehensible manner. The significance of visualization techniques in healthcare is not confined to healthcare practitioners and healthcare management but encompasses all the stakeholders; patients can benefit from the visualization of his/her data for a better understanding of their condition. In short, visualization techniques have demonstrated their benefits in the healthcare sector and can be extended to the payer and the patient. They have also had a positive impact on the quality of the healthcare provided as well as patient safety.
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.
A completely detachable fabric frame holder, for the purpose of holding the frame firmly, which is commercially available in different sizes, enabling the artist to work on the fabric with ease. The work on fabric can be embroidery or other artworks such as painting, aari work, zardosi work etc. The stand consists of a rigid base with two vertical sliding arms, each having a holder, which can be tightened to hold the frame. The arms can move horizontally along provided dovetail joints, and can be locked in place, so as to adjust it to the different sizes of the fabric frames. The stand promotes women, especially from the rural areas, to carry out craft on the cloth, without much stress in holding the hoop in their hand, so as to help promote not only the artistry of women, but also to improve small scale trade in rural areas, for economic development.
Most visualization techniques have been designed on the assumption that the data to be represented are free from uncertainty. Yet this is rarely the case. Recently the visualization community has risen to the challenge of incorporating an indication of uncertainty into visual representations, and in this article we review their work. We place the work in the context of a reference model for data visualization, that sees data pass through a pipeline of processes. This allows us to distinguish the visualization of uncertainty—which considers how we depict uncertainty specified with the data—and the uncertainty of visualization—which considers how much inaccuracy occurs as we process data through the pipeline. It has taken some time for uncertain visualization methods to be developed, and we explore why uncertainty visualization is hard—one explanation is that we typically need to find another display dimension and we may have used these up already! To organize the material we return to a typology developed by one of us in the early days of visualization, and make use of this to present a catalog of visualization techniques describing the research that has been done to extend each method to handle uncertainty. Finally we note the responsibility on us all to incorporate any known uncertainty into a visualization, so that integrity of the discipline is maintained.