Analysis of Research in Healthcare Data Analytics

The main aim of this paper is to provide a deep analysis on the research field of healthcare data analytics., as well as highlighting some of guidelines and gaps in previous studies. This study has focused on searching relevant papers about healthcare analytics by searching in seven popular databases such as google scholar and springer using specific keywords, in order to understand the healthcare topic and conduct our literature review. The paper has listed some data analytics tools and techniques that have been used to improve healthcare performance in many areas such as: medical operations, reports, decision making, and prediction and prevention system. Moreover, the systematic review has showed an interesting demographic of fields of publication, research approaches, as well as outlined some of the possible reasons and issues associated with healthcare data analytics, based on geographical distribution theme

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

Bioinformatics, Healthcare Informatics and Analytics: An Imperative for Improved Healthcare System

Healthcare Informatics focuses on health data, information and knowledge, including their collection, processing, analysis and use. Bioinformatics employ computational tools and techniques to study and analyse large biological databases and to absolutely understand disease and grasp the genetics and proteomics by relating them with healthcare data. The focus is on processing genomic and proteomics data for basic research in biology, but also medicine, drug discovery, and related areas. Analytics in healthcare came as a result of large healthcare data that are being gathered electronically. Data analytics is proficient in terms of healthcare improvement, reduction in cost and safety of lives. Applications of data analytics in healthcare is as a result of the eruption in data to mine understandings so as to make informed decisions. This paper reviews bioinformatics, Healthcare Informatics and Analytics as an imperative for an improved Healthcare System. It looks at the benefits, the contribution of each of them to improving healthcare system, the overlap among bioinformatics, healthcare Informatics and analytics and finally the future prospects of healthcare informatics and analytic

Bioinformatics, Healthcare Informatics and Analytics: An Imperative for Improved Healthcare System

Healthcare Informatics focuses on health data, information and knowledge, including their collection, processing, analysis and use. Bioinformatics employ computational tools and techniques to study and analyse large biological databases and to absolutely understand disease and grasp the genetics and proteomics by relating them with healthcare data. The focus is on processing genomic and proteomics data for basic research in biology, but also medicine, drug discovery, and related areas. Analytics in healthcare came as a result of large healthcare data that are being gathered electronically. Data analytics is proficient in terms of healthcare improvement, reduction in cost and safety of lives. Applications of data analytics in healthcare is as a result of the eruption in data to mine understandings so as to make informed decisions. This paper reviews bioinformatics, Healthcare Informatics and Analytics as an imperative for an improved Healthcare System. It looks at the benefits, the contribution of each of them to improving healthcare system, the overlap among bioinformatics, healthcare Informatics and analytics and finally the future prospects of healthcare informatics and analytics

The Use of Business Analytics Systems: An Empirical Investigation in Taiwan’s Hospitals

This paper aims to develop a research model to examine the mechanisms by which business analytics capabilities in healthcare units are shown to indirectly influence decision-making effectiveness through a mediating role of absorptive capacity. We employed a survey method to collect primary data from Taiwan\u27s hospitals. Structural equation modeling (SEM) was used for path analysis. This study conceptualizes, operationalizes, and measures the business analytics (BA) capability as a multi-dimensional construct formed by capturing the functionalities of BA systems in healthcare. The results found that healthcare units are likely to obtain valuable knowledge as they utilize the data interpretation tools effectively. Also, the effective use of data analysis and interpretation tools in healthcare units indirectly influence decision-making effectiveness, an impact that is mediated by absorptive capacity

Aligning Business Analytics Programs with Industry Required Knowledge, Skills and Abilities

This paper describes results from a topic modeling analysis of online data analytics-related job advertisements. Five distinct clusters emerged, each with a focus on different knowledge, skills and abilities (KSAs) profiles. We labelled these clusters big data, systems analysis, business, healthcare, and technical research. Identification of these clusters provides a framework that can be used by information systems and business analytics faculty to offer customized and specialized information systems and business analytics programs that prepare graduates to fill specific roles in the data ecosystem of the workplace

Review of innovative immersive technologies for healthcare applications

Immersive technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), can connect people using enhanced data visualizations to better involve stakeholders as integral members of the process. Immersive technologies have started to change the research on multidimensional genomic data analysis for disease diagnostics and treatments. Immersive technologies are highlighted in some research for health and clinical needs, especially for precision medicine innovation. The use of immersive technology for genomic data analysis has recently received attention from the research community. Genomic data analytics research seeks to integrate immersive technologies to build more natural human-computer interactions that allow better perception engagements. Immersive technologies, especially VR, help humans perceive the digital world as real and give learning output with lower performance errors and higher accuracy. However, there are limited reviews about immersive technologies used in healthcare and genomic data analysis with specific digital health applications. This paper contributes a comprehensive review of using immersive technologies for digital health applications, including patient-centric applications, medical domain education, and data analysis, especially genomic data visual analytics. We highlight the evolution of a visual analysis using VR as a case study for how immersive technologies step, can by step, move into the genomic data analysis domain. The discussion and conclusion summarize the current immersive technology applications’ usability, innovation, and future work in the healthcare domain, and digital health data visual analytics

Value co-creation and potential benefits through big data analytics: Health Benefit Analysis

Big data analytics in healthcare context is often studied from a technical point of view. In the field of strategic management, researchers have indicated a research gap in how big data analytics create business value. This study examines how big data and advanced analytics generate potential benefits and business value for the healthcare service provider, and value for the individual patients and population health. In addition, the effects of advanced analytics to the value co-creation practices and actors in healthcare ecosystem are studied. The theoretical framework used for the purpose is the big data analytics-enabled transformation model which is adapted to answer the research questions. The study is conducted as a single case study. The studied case is the Health Benefit Analysis (HBA) tool. The empirical data is collected in eight semi-structured interviews with participants of the tool development project. Using the HBA tool reveals several paths-to-value chains. The most evident path shows how using advanced analytics affects the personalized care practice by enabling a more interactive service process between the health professionals and patients. It denotes a business scope redefinition as patients are now being interpreted as essential actors in the value co-creation of their own health outcomes. The benefits that arise from the advanced analytics are of several dimensions; operational, managerial, strategic, and organizational. Using the HBA tool generates strategic business value for the healthcare service provider as a differentiator that contributes to gaining competitive advantage compared to other service providers not using this innovation. Value emerges for the individual patient as improved patient experience and better health outcomes. Population health gains most value from the reduced health inequalities. The evolving value co-creation practices set requirements for the healthcare ecosystem actors as they need to conform to new practices with patients and other professionals from other sectors and levels of the ecosystem. The healthcare work and service culture need to develop and adapt to new tools, related processes, and a more diversified professional base, including health analysts and other new professionals. To conclude, it can be claimed that advanced analytics of healthcare big data contributes to the shift to value-based healthcare.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Transformation through Big Data Analytics: a Qualitative Enquiry in Healthcare

With an aim to understand transformation around big data analytics, this paper first investigates the literature to explore elements of change around big data. The research comprises a qualitative enquiry in the New Zealand healthcare context to understand how professionals across the sector view this transformation. Healthcare sectors are increasingly adopting big data technologies to improve healthcare delivery and management. However, for sectors like healthcare, big data brings significant changes in terms of technology architecture, infrastructure, skills, and organisational structure changes. Security measures and policy changes are also apparent. Using a deductive approach to data analysis, it confirms the important elements identified in the literature around big data transformation and highlights the relationships between these elements of change. The paper uses Sociotechnical Systems Theory as the underlying theoretical foundation for this study. The findings of this research contribute to policy and practice in healthcare

An Adaptive Neuro-Fuzzy System with Semi-Supervised Learning as an Approach to Improving Data Classification: An Illustration of Bad Debt Recovery in Healthcare

Business analytics has become an increasingly important priority for organizations today as they strive to achieve greater competitiveness. As organizations adopt business practices that rely on complex, large-scale data, new challenges also emerge. A common situation in business analytics is concerned with appropriate and adequate methods for dealing with unlabeled data in classification. This study examines the effectiveness of a semi-supervised learning approach to classify unlabeled data to improve classification accuracy rates. The context for our study is healthcare. The healthcare costs in the U.S. have risen at an alarming rate over the last two decades. One of the causes for the rising costs could be attributed to medical bad debt, i.e., debt that is not recovered by healthcare institutions. A major obstacle to debt classification, hence better debt recovery, is the presence of unlabeled cases, a situation not uncommon in many other business contexts. There is surprisingly very little research that explores the performance of computational intelligence and soft computing methods in improving bad debt recovery in the healthcare industry. Using a real data set from a healthcare organization, we address this important research gap by examining the performance of an adaptive neuro-fuzzy inference system (ANFIS) with semi-supervised learning (SSL) in improving debt recovery rate. In particular, this study explores the role of ANFIS in conjunction with SSL in classifying unknown cases (those that were not pursued for debt collection) as either a good case (recoverable) or a bad case (unrecoverable). Healthcare institutions can then pursue these potentially good cases and improve their debt recovery rates. Test results show that ANFIS with SSL is a viable method. Our models generated better classification accuracy rates than those in prior studies. These results and their analysis show the potential of ANFIS with SSL models in classifying unknown cases, which are a potential source of revenue recovery for health care organizations. The significance of this research extends to all types of organizations that face an increasingly urgent need to adopt reliable practices for business analytics