E-infrastructures fostering multi-centre collaborative research into the intensive care management of patients with brain injury

Clinical research is becoming ever more collaborative with multi-centre trials now a common practice. With this in mind, never has it been more important to have secure access to data and, in so doing, tackle the challenges of inter-organisational data access and usage. This is especially the case for research conducted within the brain injury domain due to the complicated multi-trauma nature of the disease with its associated complex collation of time-series data of varying resolution and quality. It is now widely accepted that advances in treatment within this group of patients will only be delivered if the technical infrastructures underpinning the collection and validation of multi-centre research data for clinical trials is improved. In recognition of this need, IT-based multi-centre e-Infrastructures such as the Brain Monitoring with Information Technology group (BrainIT - www.brainit.org) and Cooperative Study on Brain Injury Depolarisations (COSBID - www.cosbid.de) have been formed. A serious impediment to the effective implementation of these networks is access to the know-how and experience needed to install, deploy and manage security-oriented middleware systems that provide secure access to distributed hospital based datasets and especially the linkage of these data sets across sites. The recently funded EU framework VII ICT project Advanced Arterial Hypotension Adverse Event prediction through a Novel Bayesian Neural Network (AVERT-IT) is focused upon tackling these challenges. This chapter describes the problems inherent to data collection within the brain injury medical domain, the current IT-based solutions designed to address these problems and how they perform in practice. We outline how the authors have collaborated towards developing Grid solutions to address the major technical issues. Towards this end we describe a prototype solution which ultimately formed the basis for the AVERT-IT project. We describe the design of the underlying Grid infrastructure for AVERT-IT and how it will be used to produce novel approaches to data collection, data validation and clinical trial design is also presented

Cystic Fibrosis

Cystic Fibrosis - Heterogeneity and Personalized Treatment provides the latest research and clinical evidence for clinicians, scientists and researchers involved in the care of patients with cystic fibrosis (CF). This book outlines the burden of the CF microbiome, utilisation of CF registries to impact future care, the sequelae of hepatobiliary complication, the use of upcoming technologies to provide patient-centred care, and provides an overview of cystic fibrosis transmembrane regulator (CFTR) modulators. Looking after patients with CF is highly rewarding, allowing those of us to combine our dedication and problem-solving skills to create a personalized approach. This book is invaluable for those involved in the care of CF patients

An enhanced healthcare system in mobile cloud computing environment

Abstract Mobile cloud computing (MCC) is a new technology for mobile web services. Accordingly, we assume that MCC is likely to be of the heart of healthcare transformation. MCC offers new kinds of services and facilities for patients and caregivers. In this regard, we have tried to propose a new mobile medical web service system. To this end, we implement a medical cloud multi-agent system (MCMAS) solution for polyclinic ESSALEMA Sfax—TUNISIA, using Google's Android operating system. The developed system has been assessing using the CloudSim Simulator. This paper presents initial results of the system in practice. In fact the proposed solution shows that the MCMAS has a commanding capability to cope with the problem of traditional application. The performance of the MCMAS is compared with the traditional system in polyclinic ESSALEMA which showed that this prototype yields better recital than using usual application

Organizing the U.S. Health Care Delivery System for High Performance

Analyzes the fragmentation of the healthcare delivery system and makes policy recommendations -- including payment reform, regulatory changes, and infrastructure -- for creating mechanisms to coordinate care across providers and settings

Decision Support Systems

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference