Legal framework for health cloud: A systematic review

Privacy, Security, and Legal Issues in the Health Cloud: Structured Review for Taxonomy Development

BACKGROUND

Privacy in our digital world is a very complicated topic, especially when meeting cloud computing technological achievements with its multidimensional context. Here, privacy is an extended concept that is sometimes referred to as legal, philosophical, or even technical. Consequently, there is a need to harmonize it with other aspects in health care in order to provide a new ecosystem. This new ecosystem can lead to a paradigm shift involving the reconstruction and redesign of some of the most important and essential requirements like privacy concepts, legal issues, and security services. Cloud computing in the health domain has markedly contributed to other technologies, such as mobile health, health Internet of Things, and wireless body area networks, with their increasing numbers of embedded applications. Other dependent applications, which are usually used in health businesses like social networks, or some newly introduced applications have issues regarding privacy transparency boundaries and privacy-preserving principles, which have made policy making difficult in the field.

OBJECTIVE

One way to overcome this challenge is to develop a taxonomy to identify all relevant factors. A taxonomy serves to bring conceptual clarity to the set of alternatives in in-person health care delivery. This study aimed to construct a comprehensive taxonomy for privacy in the health cloud, which also provides a prospective landscape for privacy in related technologies.

METHODS

A search was performed for relevant published English papers in databases, including Web of Science, IEEE Digital Library, Google Scholar, Scopus, and PubMed. A total of 2042 papers were related to the health cloud privacy concept according to predefined keywords and search strings. Taxonomy designing was performed using the deductive methodology.

RESULTS

This taxonomy has 3 layers. The first layer has 4 main dimensions, including cloud, data, device, and legal. The second layer has 15 components, and the final layer has related subcategories (n=57). This taxonomy covers some related concepts, such as privacy, security, confidentiality, and legal issues, which are categorized here and defined by their expansion and distinctive boundaries. The main merits of this taxonomy are its ability to clarify privacy terms for different scenarios and signalize the privacy multidisciplinary objectification in eHealth.

CONCLUSIONS

This taxonomy can cover health industry requirements with its specifications like health data and scenarios, which are considered as the most complicated among businesses and industries. Therefore, the use of this taxonomy could be generalized and customized to other domains and businesses that have less complications. Moreover, this taxonomy has different stockholders, including people, organizations, and systems. If the antecedent effort in the taxonomy is proven, subject matter experts could enhance the extent of privacy in the health cloud by verifying, evaluating, and revising this taxonomy.

Digital Transformation of Public Health for Noncommunicable Diseases: Narrative Viewpoint of Challenges and Opportunities

The recent SARS-CoV-2 pandemic underscored the effectiveness and rapid deployment of digital public health interventions, notably the digital proximity tracing apps, leveraging Bluetooth capabilities to trace and notify users about potential infection exposures. Backed by renowned organizations such as the World Health Organization and the European Union, digital proximity tracings showcased the promise of digital public health. As the world pivots from pandemic responses, it becomes imperative to address noncommunicable diseases (NCDs) that account for a vast majority of health care expenses and premature disability-adjusted life years lost. The narrative of digital transformation in the realm of NCD public health is distinct from infectious diseases. Public health, with its multifaceted approach from disciplines such as medicine, epidemiology, and psychology, focuses on promoting healthy living and choices through functions categorized as "Assessment," "Policy Development," "Resource Allocation," "Assurance," and "Access." The power of artificial intelligence (AI) in this digital transformation is noteworthy. AI can automate repetitive tasks, facilitating health care providers to prioritize personal interactions, especially those that cannot be digitalized like emotional support. Moreover, AI presents tools for individuals to be proactive in their health management. However, the human touch remains irreplaceable; AI serves as a companion guiding through the health care landscape. Digital evolution, while revolutionary, poses its own set of challenges. Issues of equity and access are at the forefront. Vulnerable populations, whether due to economic constraints, geographical barriers, or digital illiteracy, face the threat of being marginalized further. This transformation mandates an inclusive strategy, focusing on not amplifying existing health disparities but eliminating them. Population-level digital interventions in NCD prevention demand societal agreement. Policies, like smoking bans or sugar taxes, though effective, might affect those not directly benefiting. Hence, all involved parties, from policy makers to the public, should have a balanced perspective on the advantages, risks, and expenses of these digital shifts. For a successful digital shift in public health, especially concerning NCDs, AI's potential to enhance efficiency, effectiveness, user experience, and equity-the "quadruple aim"-is undeniable. However, it is vital that AI-driven initiatives in public health domains remain purposeful, offering improvements without compromising other objectives. The broader success of digital public health hinges on transparent benchmarks and criteria, ensuring maximum benefits without sidelining minorities or vulnerable groups. Especially in population-centric decisions, like resource allocation, AI's ability to avoid bias is paramount. Therefore, the continuous involvement of stakeholders, including patients and minority groups, remains pivotal in the progression of AI-integrated digital public health.

A comprehensive method for the quantification of medication error probability based on fuzzy SLIM

Medication errors can endanger the health and safety of patients and need to be managed appropriately. This study aimed at developing a new and comprehensive method for estimating the probability of medication errors in hospitals. An extensive literature review was conducted to identify factors affecting medication errors. Success Likelihood Index Methodology was employed for calculating the probability of medication errors. For weighting and rating of factors, the Fuzzy multiple attributive group decision making methodology and Fuzzy analytical hierarchical process were used, respectively. A case study in an emergency department was conducted using the framework. A total number of 17 factors affecting medication error were identified. Workload, patient safety climate, and fatigue were the most important ones. The case study showed that subtasks requiring nurses to read the handwritten of other nurses and physicians are more prone to human error. As there is no specific method for assessing the risk of medication errors, the framework developed in this study can be very useful in this regard. The developed technique was very easy to administer.

A Secure and Decentralized Trust Management Scheme for Smart Health Systems

The Internet of Things (IoT) growth is extremely fast because of the connection of smart devices to the Internet. It now has found its way to healthcare applications too. Many smart health gadgets and devices are helping practitioners in collecting medical information and monitoring patients. In this distributed system, information or service is sometimes shared and used by other devices. Considering the importance of health-related information and the decisions made based on it, there should be some sort of assurance on the security and quality of the services or information provided. Trust management is an efficient means of promoting application security and reliability in these cases. However, due to some properties that are specific to IoT, implementation of traditional trust evaluation algorithms is either infeasible or does not yield satisfactory results. In this paper, evidence theory is exploited to design a decentralized service-oriented trust management model for healthcare IoT. A measure of evidence distance is used to reward well-behaving healthcare service/information providers as well as referrers and punish malicious entities. In this context-aware model, trust is estimated by combining direct experiences and indirect feedbacks from recommenders. The process runs in two contexts; trust to healthcare service and trust to recommendation. The final trust value to a source or service is found by applying the combinatorial laws of evidence theory and integrating direct and indirect trust values. The proposed model is secure against bad-mouthing, good-mouthing, and on-off attacks due to its dynamic parameters and using the concept of evidence distance. Our results confirm the robustness and efficiency of this scheme.

A big data framework for E-Government in Industry 4.0

The next generation of E-Government and healthcare has the potential to increase the more intelligent governance with improvements in transparency, accountability, efficiency, and effectiveness. It enables organizations to use the benefits of information via big data analysis to settle the difficulties effectively. Big Data has emerged which plays a significant role in many sectors around the world. Global trends in taking advantage of the benefits from big data are considered with an overview of the US, European Union, and several developing countries. To deeply understand the utilization of big data in several domains, this study has presented a brief survey of key concepts (such as IoT-enabled data, blockchain-enabled data, and intelligent systems data) to deeply understand the utilization of big data in several domains. Our analysis sets out also the similarities and differences in these concepts. We have also surveyed state-of-the-art technologies including cloud computing, multi-cloud, webservice, and microservice which are used to exploit potential benefits of big data analytics. Furthermore, some typical big data frameworks are surveyed and a big data framework for E-Government is also proposed. Open research questions and challenges are highlighted (for researchers and developers) following our review. Our goal in presenting the novel concepts presented in this article is to promote creative ideas in the research endeavor to perform efficaciously next-generation E-Government in the context of Industry 4.0.

Do data security measures, privacy regulations, and communication standards impact the interoperability of patient health information? A cross-country investigation

BACKGROUND

The lack of interoperability is one of the biggest obstacles to the complete digitalization of patient health information in electronic medical records (EMR). The high volume of data breaches has put pressure on care providers to adopt data protection measures to remain compliant with legal requirements. Extreme data protection measures can impede information flow, but they also instill confidence in secure information sharing. This study investigates how the adoption of security measures, privacy regulations, and communication standards has impacted patient health information interoperability at technical (TI), semantic (SI), and organizational (OI) levels within the hospitals.

METHODS

The study utilizes a quasi-experimental research design to probe the relationships of interest. Secondary data from a survey of randomly selected 773 hospitals conducted by the European Commission in over 30 countries in Europe is used to understand the relationships. The study counters selection bias and accounts for systematic differences in adopting treatments of interest in the hospitals using the propensity score-based approaches for the observational data.

RESULTS

The empirical models that account for selection bias explain more observational data variations than those that did not. Access control measures on workstations are linked to 44 % lesser odds of experiencing TI problems. However, hospitals with regional and organizational level privacy regulations have 85 % and 76 % higher odds of experiencing SI and OI problems, respectively. On the other hand, hospitals with a single hospital-wide EMR are 53 % and 43 % less likely to experience TI and SI problems, respectively, in comparison to those with multiple EMR systems.

CONCLUSION

The study highlights the differential impacts of data protection measures on the hospitals' three key types of interoperability problems (i.e., TI, SI, and OI). Homogenous EMR systems type and substantial investment in technology are critical to supporting health information interoperability within the hospitals. The study findings inform policy considerations for improving specific aspects of health information's interoperability while preserving patient data privacy and security.

A cloud-based approach to library management solution for college libraries

Purpose

The study aims to focus on the present automation status of the college libraries with an objective to offer enhanced Web-based library service on an affordable virtualization on cloud computing model.

Design/methodology/approach

With Infrastructure as a Service (Infrastructure as a Service) delivery model, this study demonstrates how libraries of colleges/smaller institutes could be connected to cloud Library Management System infrastructure through internet or dedicated point-to-point WAN connectivity. The Software as a Service (SaaS) delivery model depicts how college libraries could form library consortium at its own private cloud environment with installation of the required LMS application, database, middleware and other prerequisites.

Findings

A cloud-based consortium approach for the college libraries will reduce the cost of purchasing hardware equipment and setting up of infrastructural facilities; relieve libraries of involving additional IT skilled manpower; foster collaborative approach with shared environment and minimise duplication in resource subscription.

Originality/value

To the best of the authors’ knowledge, the present study is the first of its kind in the light of shifting of infrastructure, software and hardware requirements of smaller libraries for cooperative sharing in both IaaS and SaaS cloud platform. The study delineates step by step how college libraries could effectively leverage the cooperative cloud architecture for enhanced library services to reach wider user community.

Flype: Software for enabling personalized medicine

The advent of next generation DNA sequencing (NGS) has revolutionized clinical medicine by enabling wide‐spread testing for genomic anomalies and polymorphisms. With that explosion in testing, however, come several informatics challenges including managing large amounts of data, interpreting the results and providing clinical decision support. We present Flype, a web‐based bioinformatics platform built by a small group of bioinformaticians working in a community hospital setting, to address these challenges by allowing us to: (a) securely accept data from a variety of sources, (b) send orders to a variety of destinations, (c) perform secondary analysis and annotation of NGS data, (d) provide a central repository for all genomic variants, (e) assist with tertiary analysis and clinical interpretation, (f) send signed out data to our EHR as both PDF and discrete data elements, (g) allow population frequency analysis and (h) update variant annotation when literature knowledge evolves. We discuss the multiple use cases Flype supports such as (a) in‐house NGS tests, (b) in‐house pharmacogenomics (PGX) tests, (c) dramatic scale‐up of genomic testing using an external lab, (d) consumer genomics using two external partners, and (e) a variety of reporting tools. The source code for Flype is available upon request to the authors.