Distributed Computing and Monitoring Technologies for Older Patients

This book summarizes various approaches for the automatic detection of health threats to older patients at home living alone. The text begins by briefly describing those who would most benefit from healthcare supervision. The book then summarizes possible scenarios for monitoring an older patient at home, deriving the common functional requirements for monitoring technology. Next, the work identifies the state of the art of technological monitoring approaches that are practically applicable to geriatric patients. A survey is presented on a range of such interdisciplinary fields as smart homes, telemonitoring, ambient intelligence, ambient assisted living, gerontechnology, and aging-in-place technology. The book discusses relevant experimental studies, highlighting the application of sensor fusion, signal processing and machine learning techniques. Finally, the text discusses future challenges, offering a number of suggestions for further research directions

Disease diagnosis in smart healthcare: Innovation, technologies and applications

To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things (IoT). The ageing issue is an aspect that researchers, companies and government should devote efforts in developing smart healthcare innovative technology and applications. In this paper, the topic of disease diagnosis in smart healthcare is reviewed. Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered. Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular diseases, diabetes mellitus, Alzheimer’s disease and other forms of dementia, and tuberculosis, are considered. In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed

Big data analytics for preventive medicine

© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations

Developing a neural network model to monitor and predict waiting times in the emergency department

In parallel with manufacturing context, quality control toward provided services in service organisations have been growing as well including healthcare industry, but often models of healthcare service quality face challenges in measuring quality. The developed meta-algorithm and ANN models in this thesis can facilitate measuring service quality in Healthcare industry

A risk management system for healthcare facilities service operators

The 24-hour post-modern society in which the NHS delivers healthcare today in the UK as a business has resulted in purchasers and providers of non clinical/FM services continuing to face more and more service delivery and operational risks (Payne and Rees, 1999). These business risks are mainly caused by uncertainties in customer supply and demand service chain, limited support resources (human, capital, modern healthcare facilities and information technology) and the dynamic NHS service scape (environment). This has resulted in non clinical service decisions being reached in an ad-hoc manner and often with no effective business strategy. Furthermore, this approach has led to disastrous business planning and caring consequences, particularly in a highly politicised and consumer-sensitive environment like healthcare service provision (Wagstaff, 1997). These risks are also mainly attributed to the apparent lack of best practice guidelines that are available to assist FM service operators in identifying and managing non-clinical service operations effectively. In addition, there is evidence from NHS literature that clearly indicates the lack of best practice models for managing business risks associated with hotel, estates and site (non-clinical/FM) services delivery (Okoroh et al., 2000; DoH, 1999; CFM, 1993; Smith, 1997; Featherstone, 1999; HFN 17,1998). To date, no research has been carried out in the NHS using FM service operators' (domain experts) knowledge to develop an integrated risk management system for managing non-clinical services using modern business approaches. This thesis presents research findings from healthcare executives and FM experts on business risks faced by service operators (purchasers and providers) when managing non- clinical services effectively in the UK NHS. The research methodology used were, a detailed analysis of a best practice hospital case study, structured interviews with domain healthcare FM experts, pilot and major questionnaire surveys and Repertory Grid interviews. The research has established that in managing non clinical/FM services in the NHS, there are seven major common management-related risk classes identified as critical; customer care; financial and economic; commercial; legal; facility-transmitted; business transfer and corporate. Further research using second factor analysis established that these classical non-clinical risk factors could further be subdivided into forty-eight (48) constructs/sub-attributes highly rated by healthcare facilities executives. Using these risks factors and sub-attributes the research has developed a decision support system for risk management that can be used by FM operators to manage business risks in NHS trust hospitals

Bibliometric Studies and Worldwide Research Trends on Global Health

Global health, conceived as a discipline, aims to train, research and respond to problems of a transboundary nature, in order to improve health and health equity at the global level. The current worldwide situation is ruled by globalization, and therefore the concept of global health involves not only health-related issues, but also those related to the environment and climate change. Therefore, in this Special Issue, the problems related to global health have been addressed from a bibliometric approach in four main areas: environmental issues, diseases, health, education and society

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe