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The move toward e-health care in various countries is envisaged to reduce the cost of provision of health care, improve the quality of care and reduce medical errors. The most significant problem is the protection of patients’ data privacy. If the patients are reluctant or refuse to participate in health care system due to lack of privacy laws and regulations, the benefit of the full-fledged e-health care system cannot be materialized. The purpose of this paper is to investigate the available e-health data privacy protection laws and the perception of the people using the e-health care facilities.

The researchers used content analysis to analyze the availability and comprehensive nature of the laws and regulations. The researchers also used survey method. Participants in the study comprised of health care professionals (n=46) and health care users (n=187) who are based in the Dubai, United Arab Emirates. The researchers applied descriptive statistics mechanisms and correlational analysis to analyze the data in the survey.

The content analysis revealed that the available health data protection laws are limited in scope. The survey results, however, showed that the respondents felt that they could trust the e-health services systems offered in the UAE as the data collected is protected, the rights are not violated. The research also revealed that there was no significance difference between the nationality and the privacy data statements. All the nationality agreed that there is protection in place for the protection of e-health data. There was no significance difference between the demographic data sets and the many data protection principles.

The findings on the users’ perception could help to evaluate the success in realizing current strategies and an action plan of benchmarking could be introduced.

The purpose of this article is to describe the design and development of an e‐health strategy for the Spanish health care system. Using quality function deployment and benchmarking analysis as an analytical model, a strategy in e‐health care is proposed.

This article uses the case of a Spanish community to build a general framework for e‐health system development. Based on a multi‐disciplinary literature, and the specific needs of a community, the process of e‐health system development is analyzed and reduced into a series of phases that form an integrated method. Best practice managerial techniques are adapted to the healthcare industry and the inter‐relationships between them are mapped in a theoretical model that results in the desired outcomes.

This analysis produces a road‐map to e‐health system development consisting of several phases: analysis of the current situation of the system and determination of objectives; collection and analysis of customer expectations; development of an action plan through cross‐evaluation of customer and system needs; cost and strategic analyses; and evaluation and control systems. Managerial implications are provided.

The strategy proposed in this article is a prototype and an ongoing study in the Castilla‐La Mancha community.

The purpose of this paper is to delineate alternative e‐health futures for public health policymaking in Bangladesh.

The methodology used in the paper is the Six Pillars approach to explore alternative and preferred futures for the establishment of a health information system and e‐health provision in Bangladesh.

There is a great deal of potential for encouraging participatory engagement between various stakeholders in Bangladesh's public health policymaking. The Six Pillars approach is used not only to enhance this participatory approach but also to examine various discourses and different stakeholder voices to create four alternative futures for e‐health delivery in Bangladesh: first, leap‐frog 2025; second, e‐health car 2025; third, health cloud 2025; and fourth, e‐health political party aimed at optimizing the use of information and communication technologies (ICTs) on one hand and ensuring widened access and participation on the other.

The alternative scenarios identify four possible ways of bringing together society, government and business to achieve positive public health outcomes by creating an open, trust‐based and efficient e‐health system, not only in Bangladesh but also in other developing countries. The scenarios challenge health business as usual and used futures.

The paper aims to communicate a humane public approach to the issue of health which envisages using modern ICTs to maximize democratic access to the public health system and reduce health marginalization in Bangladesh.

The paper is a pioneering attempt to use Six Pillars for public health policymaking in Bangladesh. It is based on the first e‐health workshop in Bangladesh.

There has been a move towards the implementation of digital/e-health interventions for some time. Digital/e-health interventions have demonstrable efficacy in increasing individual empowerment, providing timely access to psychological interventions for those experiencing mental ill-health and improving outcomes for those using them. This study aims to determine the efficacy of digital/e-health interventions for individuals detained in prison who experience mental ill-health.

A systematic search of five academic databases – CINAHL, ASSIA, PsycINFO, Embase and Medline – was completed in December 2020 and updated in February 2022. The review was guided by the Whittemore and Knafl (2005) framework for integrative reviews. A total of 6,255 studies were returned and screened by title and abstract. A full-text screening of nine (n = 9) studies was conducted.

No study met the inclusion criteria for the clinical efficacy of digital/e-health interventions in a prison setting. Subsequently, a review of the literature that made it to the full-text review stage was conducted, and gaps in the literature were identified to inform policy, practice and future research.

To the best of the authors’ knowledge, this is the first integrative review conducted on the efficacy of digital/e-health interventions for mental ill-health in prison settings.

The implementation of digital twin in e-government services will become the future of public service delivery. It has a great promise for significantly optimizing e-government service delivery in public services because digital twin can be leveraged to achieve value co-creation, which can be turned for innovation and new knowledge creation. The purpose of this study is to fill a knowledge gap in the domain of e-government with digital twin enabled.

This study examined the concept of digital twins in the context of e-government for innovation management. This research applied exploratory research discussing a dynamic and interpretive model that examines the main factors to consider when developing digital twins for the Fourth Industrial Revolution’s integration of e-government services. This study begins with a thorough assessment and then evaluates the results to propose a model that would be used as a benchmark for future research. Secondary data was gathered from a variety of previously published primary research sources, including peer-reviewed journals, case studies, periodicals, newspapers and books.

E-government with digital twin platform will become increasingly integral to business or public value creation and can be managed individually as people and organizations expect much greater value for their well-being that is linked to a number of better outcomes. E-government with digital twin will no longer to be seen as a static web service but the next enabling platform to offer a comprehensive digital advisory for each and every user. The digital twin’s goal is to extract all of a user’s digital activity processes and thoroughly analyze them across all of e-services. When there are crucial issues or problems that need to be alerted to the (physical) user, the digital twin will present options, solutions and recommendations based on the entire gathered data continuum.

This study is conducted to provide a better understanding of the digital twin’s impact on public service delivery in the future. When it comes to e-government, a digital twin is a digital representation of an individual with the ability to integrate e-government services (such as e-citizenship, e-employment, e-participation, e-business, e-commerce, e-health, e-learning, e-regulation, e-entertainment and so on) with nearly real-time data and advanced analytics. Individuals will be able to improve, discover, foresee and make better and faster decisions as a result of the digital twin. The proposed model shows a future scenario for e-government services, in which the key principle of Industrial Revolution 4.0, Cyber Physical Systems, is accommodated by digital twins.

This study provides academics, policymakers and practitioners in the fields of technology, public and/or private service delivery and public policy, with the opportunity to define priorities, processes and outcomes of e-government services and thereby benefit more directly from the findings of the study. This study presents some novel insights into e-government services the use of digital twins to optimize public service delivery.

Offline reinforcement learning (RL) acquires effective policies by using prior collected large-scale data, while, in some scenarios, collecting data may be hard because it is time-consuming, expensive and dangerous, i.e. health care, autonomous driving, seeking a more efficient offline RL method. The purpose of the study is to introduce an algorithm, which attempts to sample the high-value transitions in the prioritized buffer, and uniformly sample from the normal experience buffer, improving sample efficiency of offline reinforcement learning, as well as alleviating the “extrapolation error” commonly arising in offline RL.

The authors propose a new structure of experience replay architecture, which consists of double experience replies, a prioritized experience replay and a normal experience replay, supplying samples for policy updates in different training phases. At the first training stage, the authors sample from prioritized experience replay according to the calculated priority of each transitions. At the second training stage, the authors sample from the normal experience replay uniformly. The combination of the two experience replies is initialized by the same offline data set.

The proposed method eliminates out-of-distribution problem in an offline RL regime, and promotes training by leveraging a new efficient experience replay. The authors evaluate their method on D4RL benchmark, and the results reveal that the algorithm can achieve superior performance over the state-of-the-art offline RL algorithm. The ablation study proves that the authors’ experience replay architecture plays an important role in terms of improving final performance, data-efficiency and training stability.

Because of the extra addition of prioritized experience replay, the proposed method increases the computational burden and has the risk of changing data distribution due to the combined sample strategy. Therefore, researchers are encouraged to use the experience replay block effectively and efficiently further.

Offline RL is susceptible to the quality and coverage of pre-collected data, which may be not easy to be collected from specific environment, demanding practitioners to handcraft behavior policy to interact with environment for gathering data.

The proposed approach focuses on the experience replay architecture for offline RL, and empirically demonstrates the superiority of the algorithm on data efficiency and final performance over conservative Q-learning across diverse D4RL tasks. In particular, the authors compare different variants of their experience replay block, and the experiments show that the stages, when to sample from the priority buffer, play an important role in the algorithm. The algorithm is easy to implement and can be combined with any Q-value approximation-based offline RL methods by minor adjustment.

This paper aims to discuss the strategic role of telehealth technologies in managing the COVID-19 pandemic.

This is a viewpoint paper, based on opportune information published and discussed by scholars and managers from different sources; the authors gathered this information to discuss the implications of telehealth during the outbreak.

Based on examples and benchmarking, the authors found that it is possible to lean on telehealth technologies as a frontline ally to avoid the spread of the virus by tracking, testing and treating (3T’s model).

Together with information published on COVID-19, the authors present their critical observations on the use of telehealth. However, the authors acknowledge that there are restrictions on the use of new technologies in health-care practices that were not addressed by this paper, and they suggest further research to address this limitation.

Governments, health-care organizations and managers are encouraged to take advantage of the information published in this paper. One of the benefits of telehealth is the possibility of bringing patients and physicians together virtually, without the need for physical contact. Henceforth, the authors suggest a more comprehensive implementation of best practices from telehealth to relieve congested health-care facilities and to avoid the risk of further infection.

The economic and social impacts of the virus are considered unprecedented by governments worldwide. Therefore, the authors advocate that telehealth practices embedded in health-care practices relieve the pressure that naturally arise during this type of critical event.

In this timely paper, the authors provide invaluable information related to the impact of telehealth technologies on flattening the infection curve of COVID-19.

The purpose of this study is to alleviate the specified issues to a great extent. To promote patients’ health via early prediction of diseases, knowledge extraction using data mining approaches shows an integral part of e-health system. However, medical databases are highly imbalanced, voluminous, conflicting and complex in nature, and these can lead to erroneous diagnosis of diseases (i.e. detecting class-values of diseases). In literature, numerous standard disease decision support system (DDSS) have been proposed, but most of them are disease specific. Also, they usually suffer from several drawbacks like lack of understandability, incapability of operating rare cases, inefficiency in making quick and correct decision, etc.

Addressing the limitations of the existing systems, the present research introduces a two-step framework for designing a DDSS, in which the first step (data-level optimization) deals in identifying an optimal data-partition (Popt) for each disease data set and then the best training set for Popt in parallel manner. On the other hand, the second step explores a generic predictive model (integrating C4.5 and PRISM learners) over the discovered information for effective diagnosis of disease. The designed model is a generic one (i.e. not disease specific).

The empirical results (in terms of top three measures, namely, accuracy, true positive rate and false positive rate) obtained over 14 benchmark medical data sets (collected from https://archive.ics.uci.edu/ml) demonstrate that the hybrid model outperforms the base learners in almost all cases for initial diagnosis of the diseases. After all, the proposed DDSS may work as an e-doctor to detect diseases.

The model designed in this study is original, and the necessary parallelized methods are implemented in C on Cluster HPC machine (FUJITSU) with total 256 cores (under one Master node).

The adoption of benchmarking techniques has increased over recent years, yet service organisations have been relatively reluctant to adopt the practice. It is widely acknowledged that elements of “service quality” play a key role in the performance and competitiveness of service organisations and thus provide potential benchmarking criteria. Yet perceived service quality must be defined from the consumer’s perspective and, unlike manufacturing organisations, the consumer is involved in the production process. This study examines the potential for the generation and evaluation of consumer focused benchmarking criteria. Consumers of three service sectors – health (family planning); education/professional (accountancy training) and retail (supermarkets) completed measurement scales relating to potentially deterministic attributes and assessed these for current and previous suppliers. The findings indicate that, although management must be aware of a number of potential problems and issues, information derived from consumers can provide a valuable input into a comprehensive external benchmarking programme involving both competitive and generic measures.