Information Science in the Analytics of Healthcare Data

2022 ◽  
pp. 219-237
Author(s):  
Sofia Jonathan G.
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
Information Science ◽  
Medical Information ◽  
Mining Machine ◽  
Healthcare Organizations ◽  
Healthcare Data ◽  
Interdisciplinary Field ◽  
Quality Of Patient Care

Information science is an interdisciplinary field that deals with the effective collection, storage, retrieval, and use of information for better decision making through related technologies. Today, healthcare organizations are looking for more efficient and sophisticated means of collecting, managing, analyzing data, and delivering medical information to physicians, clinicians, and nurses. The role of information science in the healthcare domain is to improve the quality of patient care, reduce operational cost, and make the entire internal management process well organized for better decision making. Through the application of technology, data analytics and information science practitioners help drive data-informed healthcare decisions. Hence, this chapter covers the techniques that are useful for data analytics and information management in healthcare such as data mining, machine learning, cloud computing, and data visualization.

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.

scholarly journals Big Data, Decision Models, and Public Health

2020 ◽  
Vol 17 (18) ◽  
pp. 6723
Author(s):  
Chien-Lung Chan ◽  
Chi-Chang Chang
Keyword(s):  
Public Health ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Decision Models ◽  
Medical Decision ◽  
Mining Machine ◽  
Special Issue ◽  
Trade Offs

Unlike most daily decisions, medical decision making often has substantial consequences and trade-offs. Recently, big data analytics techniques such as statistical analysis, data mining, machine learning and deep learning can be applied to construct innovative decision models. With complex decision making, it can be difficult to comprehend and compare the benefits and risks of all available options to make a decision. For these reasons, this Special Issue focuses on the use of big data analytics and forms of public health decision making based on the decision model, spanning from theory to practice. A total of 64 submissions were carefully blind peer reviewed by at least two referees and, finally, 23 papers were selected for this Special Issue.

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.

Human Factors in the Development of Trend Detection and Tracking Techniques

2006 ◽  
pp. 273-279
Author(s):  
Chaomei Chen ◽  
Kaushal Toprani ◽  
Natasha Lobo
Keyword(s):  
Decision Making ◽  
Human Factors ◽  
Information Science ◽  
Trend Detection ◽  
Text Document ◽  
Working Environments ◽  
Detection And Tracking ◽  
Emerging Trends ◽  
Efficiency And Effectiveness

Trend detection has been studied by researchers in many fields, such as statistics, economy, finance, information science, and computer science (Basseville & Nikiforov, 1993; Chen, 2004; Del Negro, 2001). Trend detection studies can be divided into two broad categories. At technical levels, the focus is on detecting and tracking emerging trends based on dedicated algorithms; at decision making and management levels, the focus is on the process in which algorithmically identified temporal patterns can be translated into elements of a decision making process. Much of the work is concentrated in the first category, primarily focusing on the efficiency and effectiveness from an algorithmic perspective. In contrast, relatively fewer studies in the literature have addressed the role of human perceptual and cognitive system in interpreting and utilizing algorithmically detected trends and changes in their own working environments. In particular, human factors have not been adequately taken into account; trend detection and tracking, especially in text document processing and more recent emerging application areas, has not been studied as integral part of decision-making and related activities. However, rapidly growing technology, and research in the field of human-computer interaction has opened vast and, certainly, thought-provoking possibilities for incorporating usability and heuristic design into the areas of trend detection and tracking.

Human Factors in the Development of Trend Detection and Tracking Techniques

2009 ◽  
pp. 1678-1686
Author(s):  
Chaomei Chen ◽  
Kaushal Toprani ◽  
Natasha Lobo
Keyword(s):  
Decision Making ◽  
Human Factors ◽  
Information Science ◽  
Trend Detection ◽  
Text Document ◽  
Working Environments ◽  
Detection And Tracking ◽  
Emerging Trends ◽  
Efficiency And Effectiveness

Trend detection has been studied by researchers in many fields, such as statistics, economy, finance, information science, and computer science (Basseville & Nikiforov, 1993; Chen, 2004; Del Negro, 2001). Trend detection studies can be divided into two broad categories. At technical levels, the focus is on detecting and tracking emerging trends based on dedicated algorithms; at decision making and management levels, the focus is on the process in which algorithmically identified temporal patterns can be translated into elements of a decision making process. Much of the work is concentrated in the first category, primarily focusing on the efficiency and effectiveness from an algorithmic perspective. In contrast, relatively fewer studies in the literature have addressed the role of human perceptual and cognitive system in interpreting and utilizing algorithmically detected trends and changes in their own working environments. In particular, human factors have not been adequately taken into account; trend detection and tracking, especially in text document processing and more recent emerging application areas, has not been studied as integral part of decision-making and related activities. However, rapidly growing technology, and research in the field of human-computer interaction has opened vast and, certainly, thought-provoking possibilities for incorporating usability and heuristic design into the areas of trend detection and tracking.

Role of Learning Analytics in Enhancing Teaching and Learning

2016 ◽  
pp. 259-281
Author(s):  
Amir Manzoor
Keyword(s):  
Decision Making ◽  
Learning Analytics ◽  
Data Analytics ◽  
Teaching And Learning ◽  
Learning Systems ◽  
Practical Applications ◽  
Research Organizations ◽  
Student Grades ◽  
Tools And Techniques

Data analytics, tools and techniques are no more confined to research organizations. These tools are being adopted by many organizations to generate business intelligence for improving decision making. Higher education institutions (HEIs) are beginning to use data analytics for improving their services and for increasing student grades and retention. Educational learning analytics are used to research and build models in several areas that can influence online learning systems. While use of analytics and data mining in education is increasing, sorting out fact from fiction and identifying research possibilities and practical applications are not easy. This chapter intends to help policymakers and administrators of HEIs understand how learning analytics have been used and can be applied for educational improvements.

Skills, Influence, and Effectiveness of Management Accountants

2020 ◽  
pp. 0000-0000
Author(s):  
Hans ten Rouwelaar ◽  
Frans Schaepkens ◽  
Sally K. Widener
Keyword(s):  
Decision Making ◽  
Interpersonal Skills ◽  
Technical Skills ◽  
Business Environment ◽  
Path Model ◽  
Business Partner ◽  
Healthcare Organizations ◽  
Management Accountants ◽  
Conceptual Skills

The role of the management accountant (MA) has broadened to include acting as a strategic business partner. Our study examines whether MAs believe they need interpersonal skills (i.e., ability to constructively challenge and question assumptions, numbers, and their meanings), conceptual skills (i.e., making and leading decisions consistent with the organization's business environment and strategy), and/or technical skills (i.e., computer, accounting, and data modeling) to be influential and effective in this expanded role. To examine our hypotheses, we use survey data from 215 controllers in Dutch healthcare organizations and develop a partial least squares path model. We conclude that interpersonal and conceptual skills are associated with MAs' perceptions that they influence management's decision making, while all three skills are associated with their effectiveness. We also find that technical and conceptual skills are jointly associated with the influence of MAs while conceptual and interpersonal skills are jointly associated with their effectiveness.

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