CLOUD COMPUTING IN HEALTHCARE

Information Technology (IT) has modernizedhealthcare sector via the newest development. Cloud computing in healthcare is budding and charming as crucial methodologies by most of the stakeholders.  It has the special ability to offer infinite capacity and power of process in the e-healthcare sector.This leads computer to be used efficiently and exclusively by the sharing of resources in healthcare. This paper provide a review of some proposed cloud based e-healthcare architectures edge along with issues inbothtechnologies and the crucial reasons of enhancingforward to a cloud based e-healthcare especially in Malaysia

Managing healthcare workflows in a multi-agent system environment

Whilst Multi-Agent System (MAS) architectures appear to offer a more flexible model for designers and developers of complex, collaborative information systems, implementing real-world business processes that can be delegated to autonomous agents is still a relatively difficult task. Although a range of agent tools and toolkits exist, there still remains the need to move the creation of models nearer to code generation, in order that the development path be more rigorous and repeatable. In particular, it is essential that complex organisational process workflows are captured and expressed in a way that MAS can successfully interpret. Using a complex social care system as an exemplar, we describe a technique whereby a business process is captured, expressed, verified and specified in a suitable format for a healthcare MAS.</p

Investigating Cloud Access Security Broker In A Healthcare Service : Creating A Cloud Access Security Broker (CASB) Discussion Frame-work For Evaluating Security in Cloud Healthcare Services

Master's thesis in Cyber security (IKT523)Covid-19 accentuated the importance of accessible services, causing a major increase in the adoption of cloud services for enterprises. Cloud computing is a new paradigm that promises significant benefits for organizations in healthcare services. However, cloud computing also transforms enterprise architectures and introduces new problems of information security. Decision-makers in a large healthcare service provider need to justify decisions on cloud adoption, but such a task is convoluted given the different views on cloud computing and the potential impact of cyberthreats on critical infrastructures. As a consequence, cloud security controls need to be selected and implemented to complement cloud services. Our research focuses on the decision-making process for selecting a Cloud Access Security Broker (CASB) in a large public healthcare ICT provider in Norway. This thesis applies Action Design Research (ADR) to design a decision support tool for cloud security control selection in healthcare organizations. The result is a framework for evaluating cloud security controls that facilitates the decision-making process by considering multiple aspects of enterprise security architectures. Participants in the decision-making process can achieve a common understanding of cloud security control and a tailored assessment of how the cloud will impact information security in the organization. We present the design process and apply the framework to the CASB selection problem. As a practical implication, our findings suggest that selecting a cloud security control in a healthcare service provider is an ill-structured or “wicked” problem that requires a unique problem-solving approac

Integration of Information Technologies in Enterprise Application Development

Healthcare enterprises are disconnected. In the era of integrated information systems and Internet explosion, the necessity of information systems integration reside from business process evolution, on the one hand, and from information technology tendencies, on the other hand. In order to become more efficient and adaptive to change, healthcare organizations are tremendously preoccupied of business process automation, flexibility and complexity. The need of information systems integration arise from these goals, explaining, at the same time, the special interest in EAI. Extensible software integration architectures and business orientation of process modeling and information systems functionalities, the same as open-connectivity, accessibility and virtualization lead to most suitable integration solutions: SOA and BPM architectural styles in a cloud computing environment

SAFS: A Deep Feature Selection Approach for Precision Medicine

In this paper, we propose a new deep feature selection method based on deep architecture. Our method uses stacked auto-encoders for feature representation in higher-level abstraction. We developed and applied a novel feature learning approach to a specific precision medicine problem, which focuses on assessing and prioritizing risk factors for hypertension (HTN) in a vulnerable demographic subgroup (African-American). Our approach is to use deep learning to identify significant risk factors affecting left ventricular mass indexed to body surface area (LVMI) as an indicator of heart damage risk. The results show that our feature learning and representation approach leads to better results in comparison with others

Identifying common problems in the acquisition and deployment of large-scale software projects in the US and UK healthcare systems

Public and private organizations are investing increasing amounts into the development of healthcare information technology. These applications are perceived to offer numerous benefits. Software systems can improve the exchange of information between healthcare facilities. They support standardised procedures that can help to increase consistency between different service providers. Electronic patient records ensure minimum standards across the trajectory of care when patients move between different specializations. Healthcare information systems also offer economic benefits through efficiency savings; for example by providing the data that helps to identify potential bottlenecks in the provision and administration of care. However, a number of high-profile failures reveal the problems that arise when staff must cope with the loss of these applications. In particular, teams have to retrieve paper based records that often lack the detail on electronic systems. Individuals who have only used electronic information systems face particular problems in learning how to apply paper-based fallbacks. The following pages compare two different failures of Healthcare Information Systems in the UK and North America. The intention is to ensure that future initiatives to extend the integration of electronic patient records will build on the ‘lessons learned’ from previous systems

Abnormal Infant Movements Classification With Deep Learning on Pose-Based Features

The pursuit of early diagnosis of cerebral palsy has been an active research area with some very promising results using tools such as the General Movements Assessment (GMA). In our previous work, we explored the feasibility of extracting pose-based features from video sequences to automatically classify infant body movement into two categories, normal and abnormal. The classification was based upon the GMA, which was carried out on the video data by an independent expert reviewer. In this paper we extend our previous work by extracting the normalised pose-based feature sets, Histograms of Joint Orientation 2D (HOJO2D) and Histograms of Joint Displacement 2D (HOJD2D), for use in new deep learning architectures. We explore the viability of using these pose-based feature sets for automated classification within a deep learning framework by carrying out extensive experiments on five new deep learning architectures. Experimental results show that the proposed fully connected neural network FCNet performed robustly across different feature sets. Furthermore, the proposed convolutional neural network architectures demonstrated excellent performance in handling features in higher dimensionality. We make the code, extracted features and associated GMA labels publicly available