Proposed Framework for Smart Healthcare Services

Smart healthcare is of great interest to researchers and governments due to the increasing development of new smart cities. However, there is no current standard practice to format the cloud computing infrastructure and to assist the healthcare system architect in designing a comprehensive solution for the basic services that are required by the healthcare users while taking into consideration a balanced approach towards their specific functional and non-functional needs such as openness, scalability, concurrency, interoperability and security factors. The integration of smart healthcare services with cloud computing needs a concrete framework. The main objective of this paper is to analyze the different frameworks that discuss smart healthcare services and reach to a conclusion of the common factors to arrive at a unified and smart framework

A CROSS-COUNTRY STUDY OF CLOUD COMPUTING POLICY AND REGULATION IN HEALTHCARE

International health IT policy currently supports the move towards cloud computing. Governments, industry leaders and advocacy groups are keen to build confidence among health professionals to adopt cloud-based solutions in healthcare. However, the potential benefits from cloud computing need to be evaluated against the risks. This research is a comparative study on U.S and EU health professionals´ views on the potential benefits and risks from cloud computing. The results from surveying healthcare organizations in the U.S and five EU countries (France, Germany, the Netherlands, Sweden and the UK) identify differences across countries in health IT policy, incentives for adoption, privacy and security, and trust in third party suppliers. Our findings show that privacy and security are important issus for healthcare organizations, yet differences exist between the U.S and across EU Member States in how these concepts are viewed. U.S laws and EU Directives on data protection are more advanced than other international regulatory systems. Our study provides insights on cross-jurisdictional approaches to personal data and privacy, regulations and rules on health data export, how countries interpret and implement different data protection regulations and rules, and the practical implementation of regulatory rules using a comparative research method. \

Healthcare Data Analytics on the Cloud

Meaningful analysis of voluminous health information has always been a challenge in most healthcare organizations. Accurate and timely information required by the management to lead a healthcare organization through the challenges found in the industry can be obtained using business intelligence (BI) or business analytics tools. However, these require large capital investments to implement and support the large volumes of data that needs to be analyzed to identify trends. They also require enormous processing power which places pressure on the business resources in addition to the dynamic changes in the digital technology. This paper evaluates the various nuances of business analytics of healthcare hosted on the cloud computing environment. The paper explores BI being offered as Software as a Service (SaaS) solution towards offering meaningful use of information for improving functions in healthcare enterprise. It also attempts to identify the challenges that healthcare enterprises face when making use of a BI SaaS solution

On the Deployment of Healthcare Applications over Fog Computing Infrastructure

Fog computing is considered as the most promising enhancement of the traditional cloud computing paradigm in order to handle potential issues introduced by the emerging Interned of Things (IoT) framework at the network edge. The heterogeneous nature, the extensive distribution and the hefty number of deployed IoT nodes will disrupt existing functional models, creating confusion. However, IoT will facilitate the rise of new applications, with automated healthcare monitoring platforms being amongst them. This paper presents the pillars of design for such applications, along with the evaluation of a working prototype that collects ECG traces from a tailor-made device and utilizes the patient's smartphone as a Fog gateway for securely sharing them to other authorized entities. This prototype will allow patients to share information to their physicians, monitor their health status independently and notify the authorities rapidly in emergency situations. Historical data will also be available for further analysis, towards identifying patterns that may improve medical diagnoses in the foreseeable future

Towards Smart and Green Features of Cloud Computing in Healthcare Services: A Systematic Literature Review

Background: The healthcare sector has been facing multilateral challenges regarding the quality of services and access to healthcare innovations. As the population grows, the sector requires faster and more reliable services, but the opposite is true in developing countries. As a robust technology, cloud computing has numerous features and benefits that are still to be explored. The intervention of the latest technologies in healthcare is crucial to shifting toward next-generation healthcare systems. In developing countries like Ethiopia, cloud features are still far from being systematically explored to design smart and green healthcare services. Objective: To excavate contextualized research gaps in the existing studies towards smart and green features of cloud computing in healthcare information services. Methods: We conducted a systematic review of research publications indexed in Scopus, Web of Science, IEEE Xplore, PubMed, and ProQuest. 52 research articles were screened based on significant selection criteria and systematically reviewed. Extensive efforts have been made to rigorously review recent, contemporary, and relevant research articles. Results: This study presented a summary of parameters, proposed solutions from the reviewed articles, and identified research gaps. These identified research gaps are related to security and privacy concerns, data repository standardization, data shareability, self-health data access control, service collaboration, energy efficiency/greenness, consolidation of health data repositories, carbon footprint, and performance evaluation. Conclusion: The paper consolidated research gaps from multiple research investigations into a single paper, allowing researchers to develop innovative solutions for improving healthcare services. Based on a rigorous analysis of the literature, the existing systems overlooked green computing features and were highly vulnerable to security violations. Several studies reveal that security and privacy threats have been seriously hampering the exponential growth of cloud computing. 54 percent of the reviewed articles focused on security and privacy concerns. Keywords: Cloud computing, Consolidation, Green computing, Green features, Healthcare services, Systematic literature review

A Secure Cloud-based Platform to Host Healthcare Applications

Digital technologies, such as Big Data analytics, artificial intelligence, cloud and high-performance computing are presenting new opportunities to transform healthcare systems, increase connectivity of hospitals and other providers, and therefore potentially and significantly improve patient care. However, such networked computing infrastructures also raise significant cybersecurity risks, especially in the healthcare domain, where protecting sensitive personal information is of paramount importance. Project ASCLEPIOS aims at strengthening the trust of users in cloud-based healthcare services by utilizing trusted execution environment and several modern cryptographic approaches such as attribute based encryption, searchable encryption, functional encryption to build a cloud-based e-health framework that protects users’ privacy, prevents both internal and external attacks, verifies the integrity of medical devices before application, and runs privacy-preserving data analytics on encrypted data. The project investigates modern encryption techniques and their combination in order to provide increased security of e-health applications that are then presented towards end-users utilizing a cloud-based platform. Although some topics such as security and privacy are already investigated through block-chain related technologies, it has been decided that the selected approaches would be more suitable for these particular challenges. In order to prototype its security services, ASCLEPIOS develops and deploys three large-scale healthcare demonstrators, provided by three leading hospitals from Europe. These demonstrators are rooted in the practice-based problems and applications provided by the project’s healthcare partners. The Amsterdam University Centers, University of Amsterdam, plans to improve stroke hyper-acute care through secure information sharing on a cloud computing platform to improve patient management. Additionally, they are also building prediction models to enable earlier discharge of patients from hospitals with lower risk factors. Charité Berlin plans to improve inpatient and outpatient sleep medication by remotely controlling the quality of the collected data and transferring it on-line for further analysis. Finally, the Norwegian Centre for e-health Research, University Hospital of North Norway is developing a system for privacy-preserving monitoring and benchmarking of antibiotics prescription of general practitioners. The common characteristics of these three scenarios are the increased demand for high levels of security in data transfer, storage and privacy preserving analytics on cloud infrastructures. In order deploy, operate and further develop these applications to increase their security with the ASCLEPIOS framework, a cloud computing testbed is being setup. The testbed uses state-of-the-art technologies for cloud application deployment and run-time orchestration in order to ensure the optimized deployment and execution of the demonstrator applications. As the data sources do not require the local execution (albeit in one case data may remain on the data source) of processing, there is no need for fog or edge computing, but the testbed is based on private OpenStack cloud computing infrastructures and utilizes the MiCADO framework which is compatible with different containers such as Docker and Kubernetes. The project started only recently, and currently it is in the early stages of systems design and specification. This presentation will provide a short introduction to the ASCLEPIOS project and its demonstrators and will present early results of the currently ongoing requirements specification and platform design processes

SPACE4AI-R: a Runtime Management Tool for AI Applications Component Placement and Resource Scaling in Computing Continua

The recent migration towards Internet of Things determined the rise of a Computing Continuum paradigm where Edge and Cloud resources coordinate to support the execution of Artificial Intelligence (AI) applications, becoming the foundation of use-cases spanning from predictive maintenance to machine vision and healthcare. This generates a fragmented scenario where computing and storage power are distributed among multiple devices with highly heterogeneous capacities. The runtime management of AI applications executed in the Computing Continuum is challenging, and requires ad-hoc solutions. We propose SPACE4AI-R, which combines Random Search and Stochastic Local Search algorithms to cope with workload fluctuations by identifying the minimum-cost reconfiguration of the initial production deployment, while providing performance guarantees across heterogeneous resources including Edge devices and servers, Cloud GPU-based Virtual Machines and Function as a Service solutions. Experimental results prove the efficacy of our tool, yielding up to 60% cost reductions against a static design-time placement, with a maximum execution time under 1.5s in the most complex scenarios

Towards A Well-Secured Electronic Health Record in the Health Cloud

The major concerns for most cloud implementers particularly in the health care industry have remained data security and privacy. A prominent and major threat that constitutes a hurdle for practitioners within the health industry from exploiting and benefiting from the gains of cloud computing is the fear of theft of patients health data in the cloud. Investigations and surveys have revealed that most practitioners in the health care industry are concerned about the risk of health data mix-up amongst the various cloud providers, hacking to comprise the cloud platform and theft of vital patients’ health data.An overview of the diverse issues relating to health data privacy and overall security in the cloud are presented in this technical report. Based on identifed secure access requirements, an encryption-based eHR security model for securing and enforcing authorised access to electronic health data (records), eHR is also presented. It highlights three core functionalities for managing issues relating to health data privacy and security of eHR in health care cloud

Cloud Storage and Bioinformatics in a private cloud deployment: Lessons for Data Intensive research

This paper describes service portability for a private cloud deployment, including a detailed case study about Cloud Storage and bioinformatics services developed as part of the Cloud Computing Adoption Framework (CCAF). Our Cloud Storage design and deployment is based on Storage Area Network (SAN) technologies, details of which include functionalities, technical implementation, architecture and user support. Experiments for data services (backup automation, data recovery and data migration) are performed and results confirm backup automation is completed swiftly and is reliable for data-intensive research. The data recovery result confirms that execution time is in proportion to quantity of recovered data, but the failure rate increases in an exponential manner. The data migration result confirms execution time is in proportion to disk volume of migrated data, but again the failure rate increases in an exponential manner. In addition, benefits of CCAF are illustrated using several bioinformatics examples such as tumour modelling, brain imaging, insulin molecules and simulations for medical training. Our Cloud Storage solution described here offers cost reduction, time-saving and user friendliness