Opportunities and challenges of cloud computing to improve health care services

Enhancing Energy Efficiency in Telehealth Internet of Things Systems Through Fog and Cloud Computing Integration: Simulation Study

BACKGROUND

The increasing adoption of telehealth Internet of Things (IoT) devices in health care informatics has led to concerns about energy use and data processing efficiency.

OBJECTIVE

This paper introduces an innovative model that integrates telehealth IoT devices with a fog and cloud computing-based platform, aiming to enhance energy efficiency in telehealth IoT systems.

METHODS

The proposed model incorporates adaptive energy-saving strategies, localized fog nodes, and a hybrid cloud infrastructure. Simulation analyses were conducted to assess the model's effectiveness in reducing energy consumption and enhancing data processing efficiency.

RESULTS

Simulation results demonstrated significant energy savings, with a 2% reduction in energy consumption achieved through adaptive energy-saving strategies. The sample size for the simulation was 10-40, providing statistical robustness to the findings.

CONCLUSIONS

The proposed model successfully addresses energy and data processing challenges in telehealth IoT scenarios. By integrating fog computing for local processing and a hybrid cloud infrastructure, substantial energy savings are achieved. Ongoing research will focus on refining the energy conservation model and exploring additional functional enhancements for broader applicability in health care and industrial contexts.

Predictive Modeling Using Artificial Intelligence and Machine Learning Algorithms on Electronic Health Record Data: Advantages and Challenges

The rapid adoption of electronic health record (EHR) systems in US hospitals from 2008 to 2014 produced novel data elements for analysis. Concurrent innovations in computing architecture and machine learning (ML) algorithms have made rapid consumption of health data feasible and a powerful engine for clinical innovation. In critical care research, the net convergence of these trends has resulted in an exponential increase in outcome prediction research. In the following article, we explore the history of outcome prediction in the intensive care unit (ICU), the growing use of EHR data, and the rise of artificial intelligence and ML (AI) in critical care.

Health Care Equity Through Intelligent Edge Computing and Augmented Reality/Virtual Reality: A Systematic Review

Intellectual capital is a scarce resource in the healthcare industry. Making the most of this resource is the first step toward achieving a completely intelligent healthcare system. However, most existing centralized and deep learning-based systems are unable to adapt to the growing volume of global health records and face application issues. To balance the scarcity of healthcare resources, the emerging trend of IoMT (Internet of Medical Things) and edge computing will be very practical and cost-effective. A full examination of the transformational role of intelligent edge computing in the IoMT era to attain health care equity is offered in this research. Intelligent edge computing-aided distribution and collaborative information management is a possible approach for a long-term digital healthcare system. Furthermore, IEC (Intelligent Edge Computing) encourages digital health data to be processed only at the edge, minimizing the amount of information exchanged with central servers/the internet. This significantly increases the privacy of digital health data. Another critical component of a sustainable healthcare system is affordability in digital healthcare. Affordability in digital healthcare is another key component of a sustainable healthcare system. Despite its importance, it has received little attention due to its complexity. In isolated and rural areas where expensive equipment is unavailable, IEC with AR / VR, also known as edge device shadow, can play a significant role in the inexpensive data collection process. Healthcare equity becomes a reality by combining intelligent edge device shadows and edge computing.

Integration of Healthcare 4.0 and blockchain into secure cloud-based electronic health records systems

Since the last decade, cloud-based electronic health records (EHRs) have gained significant attention to enable remote patient monitoring. The recent development of Healthcare 4.0 using the Internet of Things (IoT) components and cloud computing to access medical operations remotely has gained the researcher's attention from a smart city perspective. Healthcare 4.0 mainly consisted of periodic medical data sensing, aggregation, data transmission, data sharing, and data storage. The sensitive and personal data of patients lead to several challenges while protecting it from hackers. Therefore storing, accessing, and sharing the patient medical information on the cloud needs security attention that data should not be compromised by the authorized user's components of E-healthcare systems. To achieve secure medical data storage, sharing, and accessing in cloud service provider, several cryptography algorithms are designed so far. However, such conventional solutions failed to achieve the trade-off between the requirements of EHR security solutions such as computational efficiency, service side verification, user side verifications, without the trusted third party, and strong security. Blockchain-based security solutions gained significant attention in the recent past due to the ability to provide strong security for data storage and sharing with the minimum computation efforts. The blockchain made focused on bitcoin technology among the researchers. Utilizing the blockchain which secure healthcare records management has been of recent interest. This paper presents the systematic study of modern blockchain-based solutions for securing medical data with or without cloud computing. We implement and evaluate the different methods using blockchain in this paper. According to the research studies, the research gaps, challenges, and future roadmap are the outcomes of this paper that boost emerging Healthcare 4.0 technology.

Explaining the effect of artificial intelligence on the technology acceptance model in media: a cloud computing approach

Purpose

The purpose of this paper is to explain the effect of the technology acceptance model in the media environment by using the mediating role of artificial intelligence and the cloud computing approach.

Design/methodology/approach

After reviewing the theoretical foundations, a conceptual model framework and research hypotheses were formed. The statistical population of the study included managers, deputies and experts from the National Iranian Oil Company, and a statistical sample of 368 people was selected by simple random sampling.

Findings

The results of structural equation modelling with PLS 2.0 software show a positive and significant effect on the artificial intelligence variable in the technology acceptance model with the cloud approach. Artificial intelligence has opened a new space in the digital world, especially in the media, so that its profound impact is quite evident and has affected people’s lives.

Originality/value

The acceleration of various technologies has severely challenged the approach of organizations, especially the media. The media environment with word of the technologies of the Industry 4.0, especially cloud computing technology, has changed the ways of accessing and using products and services.

Securing future healthcare environments in a post-COVID-19 world: moving from frameworks to prototypes

The deployment of Internet of Things platforms as well as the use of mobile and wireless technologies to support healthcare environments have enormous potential to transform healthcare. This has also led to a desire to make eHealth and mHealth part of national healthcare systems. The COVID-19 pandemic has accelerated the requirement to do this to reduce the number of patients needing to attend hospitals and General Practitioner surgeries. This direction, however, has resulted in a renewed need to look at security of future healthcare platforms including information and data security as well as network and cyber-physical security. There have been security frameworks that were developed to address such issues. However, it is necessary to develop a security framework with a combination of security mechanisms that can be used to provide all the essential security requirements for healthcare systems. In addition, there is now a need to move from frameworks to prototypes which is the focus of this paper. Several security frameworks for eHealth and mHealth are first examined. This leads to a new reference model from which an implementation framework is developed using new mechanisms such as Capabilities, Secure Remote Procedure Calls, and a Service Management Framework. The prototype is then evaluated against practical security requirements.

Broadband Mobile Applications’ Adoption by SMEs in Taiwan—A Multi-Perspective Study of Determinants

The purpose of this study is to explore what determinants affect small and medium enterprises regarding the adoption of broadband mobile applications. Today, broadband mobile applications are changing the business landscape rapidly, which presents a vital issue for enterprises to understand and tackle. Both qualitative and quantitative methods were applied in this study in order to analyze the issues enterprises may face with broadband mobile applications. Qualitatively, a preliminary study was conducted based on a review of literature to explore the factors that impact small and medium enterprises. Quantitatively, Structural Equation Modeling and AMOS were deployed to further examine the potential factors. As broadband mobile applications are mostly installed in the personal mobile device and operated by individuals, this study integrated the perspective of internal users along with the Technology-Organization-Environment framework to develop an ITOE research model to provide a more comprehensive view on the determinants and factors. The practicality and feasibility of the ITOE research model were then verified by the study results through the fifteen determinants identified. Based on the findings, implications and future research directions are proposed.

A Hybrid Service Selection and Composition for Cloud Computing Using the Adaptive Penalty Function in Genetic and Artificial Bee Colony Algorithm

The rapid development of Cloud Computing (CC) has led to the release of many services in the cloud environment. Service composition awareness of Quality of Service (QoS) is a significant challenge in CC. A single service in the cloud environment cannot respond to the complex requests and diverse requirements of the real world. In some cases, one service cannot fulfill the user’s needs, so it is necessary to combine different services to meet these requirements. Many available services provide an enormous QoS and selecting or composing those combined services is called an Np-hard optimization problem. One of the significant challenges in CC is integrating existing services to meet the intricate necessities of different types of users. Due to NP-hard complexity of service composition, many metaheuristic algorithms have been used so far. This article presents the Artificial Bee Colony and Genetic Algorithm (ABCGA) as a metaheuristic algorithm to achieve the desired goals. If the fitness function of the services selected by the Genetic Algorithm (GA) is suitable, a set of services is further introduced for the Artificial Bee Colony (ABC) algorithm to choose the appropriate service from, according to each user’s needs. The proposed solution is evaluated through experiments using Cloud SIM simulation, and the numerical results prove the efficiency of the proposed method with respect to reliability, availability, and cost.

Considerations for an integrated population health databank in Africa: lessons from global best practices

Background: The rising digitisation and proliferation of data sources and repositories cannot be ignored. This trend expands opportunities to integrate and share population health data. Such platforms have many benefits, including the potential to efficiently translate information arising from such data to evidence needed to address complex global health challenges. There are pockets of quality data on the continent that may benefit from greater integration. Integration of data sources is however under-explored in Africa. The aim of this article is to identify the requirements and provide practical recommendations for developing a multi-consortia public and population health data-sharing framework for Africa.

Methods: We conducted a narrative review of global best practices and policies on data sharing and its optimisation. We searched eight databases for publications and undertook an iterative snowballing search of articles cited in the identified publications. The Leximancer software © enabled content analysis and selection of a sample of the most relevant articles for detailed review. Themes were developed through immersion in the extracts of selected articles using inductive thematic analysis. We also performed interviews with public and population health stakeholders in Africa to gather their experiences, perceptions, and expectations of data sharing.

Results: Our findings described global stakeholder experiences on research data sharing. We identified some challenges and measures to harness available resources and incentivise data sharing.  We further highlight progress made by the different groups in Africa and identified the infrastructural requirements and considerations when implementing data sharing platforms. Furthermore, the review suggests key reforms required, particularly in the areas of consenting, privacy protection, data ownership, governance, and data access.

Conclusions: The findings underscore the critical role of inclusion, social justice, public good, data security, accountability, legislation, reciprocity, and mutual respect in developing a responsive, ethical, durable, and integrated research data sharing ecosystem.

Chatbot-Delivered Psychotherapy for Adults With Depressive and Anxiety Symptoms: A Systematic Review and Meta-Regression

Although psychotherapy is a well-established treatment for depression and anxiety, chatbot-delivered psychotherapy is an emerging field that has yet to be explored in depth. This review aims to (a) examine the effectiveness of chatbot-delivered psychotherapy in improving depressive symptoms among adults with depression or anxiety, and (b) evaluate the preferred features for the design of chatbot-delivered psychotherapy. Eight electronic databases were searched for relevant randomized controlled trials. Meta-analysis and random effects meta-regression was conducted using Comprehensive Meta-Analysis 3.0 software. Overall effect was measured using Hedges's g and determined using z statistics at significance level p < .05. Assessment of heterogeneity was done using χ² and I² tests. A meta-analysis of 11 trials revealed that chatbot-delivered psychotherapy significantly improved depressive symptoms (g = 0.54, 95% confidence interval [-0.66, -0.42], p < .001). Although no significant subgroup differences were detected, results revealed larger effect sizes for samples of clinically diagnosed anxiety or depression, chatbots with an embodiment, a combination of types of input and output formats, less than 10 sessions, problem-solving therapy, off-line platforms, and in different regions of the United States than their counterparts. Meta-regression did not identify significant covariates that had an impact on depressive symptoms. Chatbot-delivered psychotherapy can be adopted in health care institutions as an alternative treatment for depression and anxiety. More high-quality trials are warranted to confirm the effectiveness of chatbot-delivered psychotherapy on depressive symptoms. PROSPERO registration number: CRD42020153332.

Key Challenges and Opportunities for Cloud Technology in Health Care: Semistructured Interview Study

Background

The use of cloud computing (involving storage and processing of data on the internet) in health care has increasingly been highlighted as having great potential in facilitating data-driven innovations. Although some provider organizations are reaping the benefits of using cloud providers to store and process their data, others are lagging behind.

Objective

We aim to explore the existing challenges and barriers to the use of cloud computing in health care settings and investigate how perceived risks can be addressed.

Methods

We conducted a qualitative case study of cloud computing in health care settings, interviewing a range of individuals with perspectives on supply, implementation, adoption, and integration of cloud technology. Data were collected through a series of in-depth semistructured interviews exploring current applications, implementation approaches, challenges encountered, and visions for the future. The interviews were transcribed and thematically analyzed using NVivo 12 (QSR International). We coded the data based on a sociotechnical coding framework developed in related work.

Results

We interviewed 23 individuals between September 2020 and November 2020, including professionals working across major cloud providers, health care provider organizations, innovators, small and medium-sized software vendors, and academic institutions. The participants were united by a common vision of a cloud-enabled ecosystem of applications and by drivers surrounding data-driven innovation. The identified barriers to progress included the cost of data migration and skill gaps to implement cloud technologies within provider organizations, the cultural shift required to move to externally hosted services, a lack of user pull as many benefits were not visible to those providing frontline care, and a lack of interoperability standards and central regulations.

Conclusions

Implementations need to be viewed as a digitally enabled transformation of services, driven by skill development, organizational change management, and user engagement, to facilitate the implementation and exploitation of cloud-based infrastructures and to maximize returns on investment.

Enablers and Challenges for E-health Services- A Systematic Literature Review

The purpose of the paper is to systematically review the literature of enablers and challenges for implementation and adoption e-health service. The review aims to identify the most critical success factors and barriers related with the development and growth of the e-health services under different circumstances. A total 68 relevant publications related to enablers and challenges of e-health services were selected from a total 694 research papers. These publications were thoroughly reviewed to find out the critical factors influencing the e-health services. The findings indicate that there are five broad factors viz. technological, environmental, organizational, social and economical along with four major stakeholders’ viz. citizens, patients, caregivers and service providers which influence the e-health services. These factors act as enablers as well as challenges for proper implementation and adoption of e-health services in different situations. On the basis of the findings of the review, a conceptual diamond model of e-health services adoption has been proposed.

Leveraging cloud computing for improved health service delivery: Findings from public health facilities in Kisumu County, Western Kenya-2019

INTRODUCTION

Healthcare delivery systems across the world have been shown to fall short of the ideals of being cost-effective and meeting pre-established standards of quality but the problem is more pronounced in Africa. Cloud computing emerges as a platform healthcare institutions could leverage to address these shortfalls. The aim of this study was to establish the extent of cloud computing adoption and its influence on health service delivery by public health facilities in Kisumu County.

METHODS

The study employed a cross-sectional study design in one-time data collection among facility in-charges and health records officers from 57 public health facilities. The target population was 114 healthcare personnel and the sample size (n = 88) was computed using Yamane formula and drawn using stratified random sampling. Poisson regression was used to determine the influence of cloud computing adoption on the number of realized benefits to health service delivery.

RESULTS

Among 80 respondents, Cloud computing had been adopted by 42 (53%) while Software-as-a-Service, Platform-as-a-Service and Infrastructure-as-a-Service implementations were at 100%, 0% and 5% among adopters, respectively. Overall, those who had adopted cloud computing realized a significantly higher number of benefits to health service delivery compared to those who had not (Incident-rate ratio (IRR) =1.93, 95% confidence interval (95% CI) [1.36-2.72]). A significantly higher number of benefits was realized by those who had implemented Infrastructure-as-a-Service alongside Software-as-a-Service (IRR = 2.22, 95% CI [1.15-4.29]) and those who had implemented Software-as-a-Service only (IRR = 1.89, 95% CI [1.33-2.70]) compared to non-adopters. We observed similar results in the stratified analysis looking at economic, operational, and functional benefits to health service delivery.

CONCLUSION

Cloud computing resulted in improved health service delivery with these benefits still being realized irrespective of the service implementation model deployed. The findings buttress the need for healthcare institutions to adopt cloud computing and integrate it in their operations in order to improve health service delivery.

Information Technology Ambidexterity, Digital Dynamic Capability, and Knowledge Processes as Enablers of Patient Agility: Empirical Study

BACKGROUND

There is a limited understanding of information technology's (IT) role as an enabler of patient agility and the department's ability to respond to patients' needs and wishes adequately.

OBJECTIVE

This study aims to contribute to the insights of the validity of the hypothesized relationship among IT resources, practices and capabilities, and hospital departments' knowledge processes, and the department's ability to adequately sense and respond to patient needs and wishes (ie, patient agility).

METHODS

This study conveniently sampled data from 107 clinical hospital departments in the Netherlands and used structural equation modeling for model assessment.

RESULTS

IT ambidexterity positively enhanced the development of a digital dynamic capability (β=.69; t4999=13.43; P<.001). Likewise, IT ambidexterity also positively impacted the hospital department's knowledge processes (β=.32; t4999=2.85; P=.005). Both digital dynamic capability (β=.36; t4999=3.95; P<.001) and knowledge processes positively influenced patient agility (β=.33; t4999=3.23; P=.001).

CONCLUSIONS

IT ambidexterity promotes taking advantage of IT resources and experiments to reshape patient services and enhance patient agility.

Cloud-Based Business Process Security Risk Management: A Systematic Review, Taxonomy, and Future Directions

Despite the attractive benefits of cloud-based business processes, security issues, cloud attacks, and privacy are some of the challenges that prevent many organizations from using this technology. This review seeks to know the level of integration of security risk management process at each phase of the Business Process Life Cycle (BPLC) for securing cloud-based business processes; usage of an existing risk analysis technique as the basis of risk assessment model, usage of security risk standard, and the classification of cloud security risks in a cloud-based business process. In light of these objectives, this study presented an exhaustive review of the current state-of-the-art methodology for managing cloud-based business process security risk. Eleven electronic databases (ACM, IEEE, Science Direct, Google Scholar, Springer, Wiley, Taylor and Francis, IEEE cloud computing Conference, ICSE conference, COMPSAC conference, ICCSA conference, Computer Standards and Interfaces Journal) were used for the selected publications. A total of 1243 articles were found. After using the selection criteria, 93 articles were selected, while 17 articles were found eligible for in-depth evaluation. For the results of the business process lifecycle evaluation, 17% of the approaches integrated security risk management into one of the phases of the business process, while others did not. For the influence of the results of the domain assessment of risk management, three key indicators (domain applicability, use of existing risk management techniques, and integration of risk standards) were used to substantiate our findings. The evaluation result of domain applicability showed that 53% of the approaches had been testing run in real-time, thereby making these works reusable. The result of the usage of existing risk analysis showed that 52.9% of the authors implemented their work using existing risk analysis techniques while 29.4% of the authors partially integrated security risk standards into their work. Based on these findings and results, security risk management, the usage of existing security risk management techniques, and security risk standards should be integrated with business process phases to protect against security issues in cloud services.

Affecting factors of cloud computing adoption in public hospitals affiliated with Zahedan University of Medical Sciences: A cross-sectional study in the Southeast of Iran

Objective

Health care organizations require cloud computing to remain efficient and cost-effective, and provide high-quality health care services. Adoption of this technology by users plays a critical role in the success of its application. This study aimed to determine factors affecting cloud computing adoption in public hospitals affiliated with Zahedan University of Medical Sciences.

Methods

A cross-sectional descriptive and analytic study was performed in 2017. The study population comprised information technology and hospital information system authorities and hospital information system users. The sample consisted of 573 participants. The data were collected using a questionnaire and analyzed with the Statistical Package for Social Sciences software using descriptive and analytical statistics.

Results

The mean score of environmental, human, organizational, technological, and intention dimensions of cloud computing adoption was 3.39 ± 0.81, 3.27 ± 0.63, 3.19 ± 0.71, 3 ± 0.43, and 3.55 ± 1.10, respectively. Furthermore, a significant positive relationship was found between intention of cloud computing adoption and environmental (R = 0.521, p = 0.000), organizational (R = 0.426, p = 0.000), human (R = 0.492, p = 0.000), and technological dimensions (R = 0.157, p = 0.000).

Conclusions

Benefits of cloud computing adoption, relative advantage, and competitive pressure were identified as the most influential factors in accepting cloud computing. Simplifying the users’ understanding of this technology and its application, improving the staff's technical capabilities, promoting executive managers’ understanding of the nature and functions of cloud computing, and fully supporting and increasing governmental mandates for adoption of new technologies are necessary for facilitating the adoption of cloud computing in given hospitals.

Investigation of Cloud ERP Adoption in the Healthcare Industry Through Technology-Organization-Environment (TOE) Framework

There is an accelerated migration from on-premise ERP to Cloud ERP systems in many industries, but this transition is relatively slow in the healthcare industry. To address this concern, we developed a research model based on Technology-Organization-Environment (TOE) framework, and explored it in the healthcare industry through semi-structured interviews with IT managers and finance managers. We found noticeable differences between small-sized and large-sized healthcare organizations, as well as the perceptions of IT managers and finance managers in Cloud ERP adoption decisions. We discussed these findings, and proposed future research questions on Cloud ERP adoption in the healthcare industry.

Cloud architecture for electronic health record systems interoperability

BACKGROUND

Current Electronic Health Record (EHR) systems are built using different data representation and information models, which makes difficult achieving information exchange.

OBJECTIVE

Our aim was to propose a scalable architecture that allows the integration of information from different EHR systems.

METHODS

A cloud-based EHR interoperable architecture is proposed through the standardization and integration of patient electronic health records. The data is stored in a cloud repository with high availability features. Stakeholders can retrieve the patient EHR by requesting only to the integrated data repository. The OpenEHR two-level approach is applied according to the HL7-FHIR standards. We validated our architecture by comparing it with 5 different works (CHISTAR, ARIEN, DIRAYA, LLPHR and INEHRIS) using a set of selected axes and a scoring method.

RESULTS

The problem was reduced to a single point of communication between each EHR system and the integrated data repository. By combining cloud computing paradigm with selected health informatics standards, we obtained a generic and scalable architecture that complies 100% with interoperability requisites according to the evaluation framework applied.

CONCLUSIONS

The architecture allowed the integration of several EHR systems, adapting them with the use of standards and ensuring the availability thanks to cloud computing features.

Big Data-Enabled Solutions Framework to Overcoming the Barriers to Circular Economy Initiatives in Healthcare Sector

Ever-changing conditions and emerging new challenges affect the ability of the healthcare sector to survive with the current system, and to maintain its processes effectively. In the healthcare sector, the conservation of the natural resources is being obstructed by insufficient infrastructure for managing residual waste resulting from single-use medical materials, increased energy use, and its environmental burden. In this context, circularity and sustainability concepts have become essential in healthcare to meliorate the sector’s negative impacts on the environment. The main aim of this study is to identify the barriers related to circular economy (CE) in the healthcare sector, apply big data analytics in healthcare, and provide solutions to these barriers. The contribution of this research is the detailed examination of the current healthcare literature about CE adaptation, and a proposal for a big data-enabled solutions framework to barriers to circularity, using fuzzy best-worst Method (BWM) and fuzzy VIKOR. Based on the findings, managerial, policy, and theoretical implementations are recommended to support sustainable development initiatives in the healthcare sector.

Security challenges and solutions using healthcare cloud computing

Cloud computing is among the most beneficial solutions to digital problems. Security is one of the focal issues in cloud computing technology, and this study aims at investigating security issues of cloud computing and their probable solutions. A systematic review was performed using Scopus, Pubmed, Science Direct, and Web of Science databases. Once the title and abstract were evaluated, the quality of studies was assessed in order to choose the most relevant according to exclusion and inclusion criteria. Then, the full texts of studies selected were read thoroughly to extract the necessary results. According to the review, data security, availability, and integrity, as well as information confidentiality and network security, were the major challenges in cloud security. Further, data encryption, authentication, and classification, besides application programming interfaces (API), were security solutions to cloud infrastructure. Data encryption could be applied to store and retrieve data from the cloud in order to provide secure communication. Besides, several central challenges, which make the cloud security engineering process problematic, have been considered in this study.

Industry 4.0 Applications for Medical/Healthcare Services

At present, the whole world is transitioning to the fourth industrial revolution, or Industry 4.0, representing the transition to digital, fully automated environments, and cyber-physical systems. Industry 4.0 comprises many different technologies and innovations, which are being implemented in many different sectors. In this review, we focus on the healthcare or medical domain, where healthcare is being revolutionized. The whole ecosystem is moving towards Healthcare 4.0, through the application of Industry 4.0 methodologies. Many technical and innovative approaches have had an impact on moving the sector towards the 4.0 paradigm. We focus on such technologies, including Internet of Things, Big Data Analytics, blockchain, Cloud Computing, and Artificial Intelligence, implemented in Healthcare 4.0. In this review, we analyze and identify how their applications function, the currently available state-of-the-art technologies, solutions to current challenges, and innovative start-ups that have impacted healthcare, with regards to the Industry 4.0 paradigm.

Do personal health concerns and trust in healthcare providers mitigate privacy concerns? Effects on patients' intention to share personal health data on electronic health records

RATIONALE

Health digitalization raises important challenges for personal health-data management. Particularly, the advantages underlying the implementation of Electronic Health Record (EHR) remain limited in many countries due to patients' privacy concerns.

OBJECTIVE

Drawing on the privacy calculus theory, the objective of this research is to introduce personal health concerns and trust in healthcare providers as new predictors, beyond the constituent variables of the privacy calculus model - the perceived benefits and risk. We propose and test a conceptual model that investigates simultaneously the effects of these four variables on patients' privacy concerns and intention to share personal health-data on EHR.

METHOD

A cross-sectional study using an on online survey was administered from December 2019 to February 2020 in France to both users and non-users of EHR. A structural equation modelling was used to assess the reliability and validity of the measurement as well as to test the research hypotheses.

RESULTS

The results confirm the positive effects of personal health concerns and trust in healthcare providers on (a) the intention to create an EHR and (b) to share personal health-data. In the same vein, we do not find any significant effect of patients' privacy concerns on the intention to create an EHR and intention to share personal health-data. Furthermore, the patients' perceived benefits outweigh the perceived risks for EHR using.

CONCLUSIONS

This research provides a more holistic understanding of patients' privacy concerns. Particularly, we highlight the key role of personal health concerns and trust in healthcare providers with the intention to create an EHR and to share personal health data. Empirical evidence underlines the importance to involve all the stakeholders in the implementation process. Findings are discussed according to existing literature and practical guidelines are suggested to the health policymakers and healthcare providers.

Integrating Digital Technologies and Public Health to Fight Covid-19 Pandemic: Key Technologies, Applications, Challenges and Outlook of Digital Healthcare

Integration of digital technologies and public health (or digital healthcare) helps us to fight the Coronavirus Disease 2019 (COVID-19) pandemic, which is the biggest public health crisis humanity has faced since the 1918 Influenza Pandemic. In order to better understand the digital healthcare, this work conducted a systematic and comprehensive review of digital healthcare, with the purpose of helping us combat the COVID-19 pandemic. This paper covers the background information and research overview of digital healthcare, summarizes its applications and challenges in the COVID-19 pandemic, and finally puts forward the prospects of digital healthcare. First, main concepts, key development processes, and common application scenarios of integrating digital technologies and digital healthcare were offered in the part of background information. Second, the bibliometric techniques were used to analyze the research output, geographic distribution, discipline distribution, collaboration network, and hot topics of digital healthcare before and after COVID-19 pandemic. We found that the COVID-19 pandemic has greatly accelerated research on the integration of digital technologies and healthcare. Third, application cases of China, EU and U.S using digital technologies to fight the COVID-19 pandemic were collected and analyzed. Among these digital technologies, big data, artificial intelligence, cloud computing, 5G are most effective weapons to combat the COVID-19 pandemic. Applications cases show that these technologies play an irreplaceable role in controlling the spread of the COVID-19. By comparing the application cases in these three regions, we contend that the key to China's success in avoiding the second wave of COVID-19 pandemic is to integrate digital technologies and public health on a large scale without hesitation. Fourth, the application challenges of digital technologies in the public health field are summarized. These challenges mainly come from four aspects: data delays, data fragmentation, privacy security, and data security vulnerabilities. Finally, this study provides the future application prospects of digital healthcare. In addition, we also provide policy recommendations for other countries that use digital technology to combat COVID-19.

Cardiovascular Informatics: building a bridge to data harmony

The search for new strategies for better understanding cardiovascular disease is a constant one, spanning multitudinous types of observations and studies. A comprehensive characterization of each disease state and its biomolecular underpinnings relies upon insights gleaned from extensive information collection of various types of data. Researchers and clinicians in cardiovascular biomedicine repeatedly face questions regarding which types of data may best answer their questions, how to integrate information from multiple datasets of various types, and how to adapt emerging advances in machine learning and/or artificial intelligence to their needs in data processing. Frequently lauded as a field with great practical and translational potential, the interface between biomedical informatics and cardiovascular medicine is challenged with staggeringly massive datasets. Successful application of computational approaches to decode these complex and gigantic amounts of information becomes an essential step toward realizing the desired benefits. In this review, we examine recent efforts to adapt informatics strategies to cardiovascular biomedical research: automated information extraction and unification of multifaceted -omics data. We discuss how and why this interdisciplinary space of Cardiovascular Informatics is particularly relevant to and supportive of current experimental and clinical research. We describe in detail how open data sources and methods can drive discovery while demanding few initial resources, an advantage afforded by widespread availability of cloud computing-driven platforms. Subsequently, we provide examples of how interoperable computational systems facilitate exploration of data from multiple sources, including both consistently-formatted structured data and unstructured data. Taken together, these approaches for achieving data harmony enable molecular phenotyping of cardiovascular (CV) diseases and unification of cardiovascular knowledge.

Willingness to share personal health information: impact of attitudes, trust and control

Purpose

This study aims to explicate the behavioral factors that determine willingness to share personal health data for secondary uses.

Design/methodology/approach

A theoretical model is developed and tested with structural equation modeling using survey data from Finland.

Findings

It is shown that attitude toward information sharing is the strongest factor contributing to the willingness to share personal health information (PHI). Trust and control serve as mediating factors between the attitude and willingness to share PHI.

Research limitations/implications

The measures of the model need further refinement to cover the various aspects of the behavioral concepts.

Practical implications

The model provides useful insights into the factors that affect the willingness for information sharing in health care and in other areas where personal information is distributed.

Social implications

Sharing of PHI for secondary purposes can offer social benefits through improvements in health-care performance.

Originality/value

A broad-scale empirical data gives a unique view of attitudes toward sharing of PHI in one national setting.

Public Health Innovation through Cloud Adoption: A Comparative Analysis of Drivers and Barriers in Japan, South Korea, and Singapore

Governments are increasingly using cloud computing to reduce cost, increase access, improve quality, and create innovations in healthcare. Existing literature is primarily based on successful examples from developed western countries, and there is a lack of similar evidence from Asia. With a population close to 4.5 billion people, Asia faces healthcare challenges that pose an immense burden on economic growth and policymaking. Cloud computing in healthcare can potentially help increase the quality of healthcare delivery and reduce the economic burden, enabling governments to address healthcare challenges effectively and within a short timeframe. Advanced Asian countries such as Japan, South Korea, and Singapore provide successful examples of how cloud computing can be used to develop nationwide databases of electronic health records; real-time health monitoring for the elderly population; genetic database to support advanced research and cancer treatment; telemedicine; and health cities that drive the economy through medical industry, tourism, and research. This article examines these countries and identifies the drivers and barriers of cloud adoption in healthcare and makes policy recommendations to enable successful public health innovations through cloud adoption.

Security and Privacy Requirements for Cloud Computing in Healthcare

Cloud computing promises essential improvements in healthcare delivery performance. However, its wide adoption in healthcare is yet to be seen, one main reason being patients’ concerns for security and privacy of their sensitive medical records. These concerns can be addressed through corresponding security and privacy requirements within the system engineering process. Despite a plethora of related research, security and privacy requirements for cloud systems and services have seldomly been investigated methodically so far, whereas their individual priorities to increase the system success probability have been neglected. Against this background, this study applies a systematic requirements engineering process: First, based on a systematic literature review, an extensive initial set of security and privacy requirements is elicited. Second, an online survey based on the best-worst scaling method is designed, conducted, and evaluated to determine priorities of security and privacy requirements.

Our results show that confidentiality and integrity of medical data are ranked at the top of the hierarchy of prioritized requirements, followed by control of data use and modification, patients’ anonymity, and patients’ control of access rights. Availability, fine-grained access control, revocation of access rights, flexible access, clinicians’ anonymity, as well as usability, scalability, and efficiency of the system complete the ranking. The level of agreement among patients is rather small, but statistically significant at the 0.01 level.

The main contribution of the present research comprises the study method and results highlighting the role of strong security and privacy and excluding any trade-offs with system usability. Enabling a richer understanding of patients’ security and privacy requirements for adopting cloud computing in healthcare, these are of particular importance to researchers and practitioners interested in supporting the process of security and privacy engineering for health-cloud solutions. It further represents a supplement that can support time-intensive negotiation meetings between the requirements engineers and patients.

Development of a computerized adaptive substance use disorder scale for screening and measurement: the CAT-SUD

BACKGROUND AND AIMS

The focus of this paper is on the improvement of substance use disorder (SUD) screening and measurement. Using a multi-dimensional item response theory model, the bifactor model, we provide a psychometric harmonization between SUD, depression, anxiety, trauma, social isolation, functional impairment and risk-taking behavior symptom domains, providing a more balanced view of SUD. The aims are to (1) develop the item-bank, (2) calibrate the item-bank using a bifactor model that includes a primary dimension and symptom-specific subdomains, (3) administer using computerized adaptive testing (CAT) and (4) validate the CAT-SUD in Spanish and English in the United States and Spain.

DESIGN

Item bank construction, item calibration phase, CAT-SUD validation phase.

SETTING

Primary care, community clinics, emergency departments and patient-to-patient referrals in Spain (Barcelona and Madrid) and the United States (Boston and Los Angeles).

PARTICIPANTS/CASES

Calibration phase: the CAT-SUD was developed via simulation from complete item responses in 513 participants. Validation phase: 297 participants received the Composite International Diagnostic Interview (CIDI) and the CAT-SUD.

MEASUREMENTS

A total of 252 items from five subdomains: (1) SUD, (2) psychological disorders, (3) risky behavior, (4) functional impairment and (5) social support. CAT-SUD scale scores and CIDI SUD diagnosis.

FINDINGS

Calibration: the bifactor model provided excellent fit to the multi-dimensional item bank; 168 items had high loadings (> 0.4 with the majority > 0.6) on the primary SUD dimension. Using an average of 11 items (four to 26), which represents a 94% reduction in respondent burden (average administration time of approximately 2 minutes), we found a correlation of 0.91 with the 168-item scale (precision of 5 points on a 100-point scale).

VALIDATION

strong agreement was found between the primary CAT-SUD dimension estimate and the results of a structured clinical interview. There was a 20-fold increase in the likelihood of a CIDI SUD diagnosis across the range of the CAT-SUD (AUC = 0.85).

CONCLUSIONS

We have developed a new approach for the screening and measurement of SUD and related severity based on multi-dimensional item response theory. The bifactor model harmonized information from mental health, trauma, social support and traditional SUD items to provide a more complete characterization of SUD. The CAT-SUD is highly predictive of a current SUD diagnosis based on a structured clinical interview, and may be predictive of the development of SUD in the future.

A Mobile Cloud based IoMT Framework for Automated Health Assessment and Management

In recent years, there has been growing interest in the use of mobile cloud and Internet of Medical Things (IoMT) in automated diagnosis and health monitoring. These applications play a significant role in providing smart medical services in modern healthcare systems. In this paper, we deploy a mobile cloud-based IoMT scheme to monitor the progression of a neurological disorder using a test of motor coordination. The computing and storage capabilities of cloud server is employed to facilitate the estimation of the severity levels given by an established quantitative assessment. An Android application is used for data acquisition and communication with the cloud. Further, we integrate the proposed system with a data sharing framework in a blockchain network as an innovative solution that allows reliable data exchange among healthcare users. The experimental results show the feasibility of implementing the proposed system in a wide range of healthcare applications.

Health Information Systems in the Digital Health Ecosystem-Problems and Solutions for Ethics, Trust and Privacy

Digital health information systems (DHIS) are increasingly members of ecosystems, collecting, using and sharing a huge amount of personal health information (PHI), frequently without control and authorization through the data subject. From the data subject's perspective, there is frequently no guarantee and therefore no trust that PHI is processed ethically in Digital Health Ecosystems. This results in new ethical, privacy and trust challenges to be solved. The authors' objective is to find a combination of ethical principles, privacy and trust models, together enabling design, implementation of DHIS acting ethically, being trustworthy, and supporting the user's privacy needs. Research published in journals, conference proceedings, and standards documents is analyzed from the viewpoint of ethics, privacy and trust. In that context, systems theory and systems engineering approaches together with heuristic analysis are deployed. The ethical model proposed is a combination of consequentialism, professional medical ethics and utilitarianism. Privacy enforcement can be facilitated by defining it as health information specific contextual intellectual property right, where a service user can express their own privacy needs using computer-understandable policies. Thereby, privacy as a dynamic, indeterminate concept, and computational trust, deploys linguistic values and fuzzy mathematics. The proposed solution, combining ethical principles, privacy as intellectual property and computational trust models, shows a new way to achieve ethically acceptable, trustworthy and privacy-enabling DHIS and Digital Health Ecosystems.

Towards a European health research and innovation cloud (HRIC)

The European Union (EU) initiative on the Digital Transformation of Health and Care (Digicare) aims to provide the conditions necessary for building a secure, flexible, and decentralized digital health infrastructure. Creating a European Health Research and Innovation Cloud (HRIC) within this environment should enable data sharing and analysis for health research across the EU, in compliance with data protection legislation while preserving the full trust of the participants. Such a HRIC should learn from and build on existing data infrastructures, integrate best practices, and focus on the concrete needs of the community in terms of technologies, governance, management, regulation, and ethics requirements. Here, we describe the vision and expected benefits of digital data sharing in health research activities and present a roadmap that fosters the opportunities while answering the challenges of implementing a HRIC. For this, we put forward five specific recommendations and action points to ensure that a European HRIC: i) is built on established standards and guidelines, providing cloud technologies through an open and decentralized infrastructure; ii) is developed and certified to the highest standards of interoperability and data security that can be trusted by all stakeholders; iii) is supported by a robust ethical and legal framework that is compliant with the EU General Data Protection Regulation (GDPR); iv) establishes a proper environment for the training of new generations of data and medical scientists; and v) stimulates research and innovation in transnational collaborations through public and private initiatives and partnerships funded by the EU through Horizon 2020 and Horizon Europe.

Towards a maturity model for health-care cloud security (M2HCS)

Purpose

The purpose of this paper is to propose a novel maturity model for health-care cloud security (M2HCS), which focuses on assessing cyber security in cloud-based health-care environments by incorporating the sub-domains of health-care cyber security practices and introducing health-care-specific cyber security metrics. This study aims to expand the domain of health-care cyber security maturity model by including cloud-specific aspects than is usually seen in the literature.

Design/methodology/approach

The intended use of the proposed model was demonstrated using the evaluation method – “construct validity test” as the paper’s aim was to assess the final model and the output of the valuation. The study involved a literature-based case study of a national health-care foundation trust with an overall view because the model is assessed for the entire organisation. The data were complemented by examination of hospitals’ cyber security internal processes through web-accessible documents, and identified relevant literature.

Findings

The paper provides awareness about how organisational-related challenges have been identified as a main inhibiting factor for the adoption of cloud computing in health care. Regardless of the remunerations of cloud computing, its security maturity and levels of adoption varies, especially in health care. Maturity models provide a structure towards improving an organisation’s capabilities. It suggests that although several cyber security maturity models and standards resolving specific threats exist, there is a lack of maturity models for cloud-based health-care security.

Research limitations/implications

Due to the selected research method, the research results may lack generalizability. Therefore, future research studies can investigate the propositions further. Another is that the current thresholds were determined empirically, although it worked for the case study assessment. However, to establish more realistic threshold levels, there is a need for more validation of the model using more case studies.

Practical implications

The paper includes maturity model for the assessment management and improvement of the security posture of a health-care organisation actively using cloud. For executives, it provides a detailed security assessment of the eHealth cloud to aid in decision making. For security experts, its quantitative metrics support proactive and reactive processes.

Originality/value

The paper fulfils a recognised requirement for security maturity model focussed on health-care cloud. It could be extended to resolve evolving cyber settings.

Evaluating the factors that influence cloud technology adoption-comparative case analysis of health and non-health sectors: A systematic review

Cloud technology has brought great benefits to the health industry, including enabling improvement in the quality of services. The objective of this review study is to investigate the reported factors affecting the adoption of cloud in the health sector by comparing studies in the health and non-health sectors. This article is a systematized review of studies conducted in 2018. From 541 articles, 47 final articles were selected and classified into two categories: health and non-health studies; conclusions were drawn from the two sectors by comparing their effective factors. Based on the results of this review, the factors were categorized as technological, organizational, environmental, and individual. The results of this review study could be a beneficial guide to the health empirical research on cloud adoption. Individual domains have not been examined in health sector studies. Since the process of adoption of new technologies in organizations is time-consuming, due to the lack of managerial knowledge about the efficient factors, recognition of these factors by decision-makers while planning for cloud adoption becomes of great importance. The findings of this review study aim to help health decision-makers by increasing their awareness of the cloud and of the factors that impact decisions at both the organizational and individual levels.

eHealth Cloud Security Challenges: A Survey

Cloud computing is a promising technology that is expected to transform the healthcare industry. Cloud computing has many benefits like flexibility, cost and energy savings, resource sharing, and fast deployment. In this paper, we study the use of cloud computing in the healthcare industry and different cloud security and privacy challenges. The centralization of data on the cloud raises many security and privacy concerns for individuals and healthcare providers. This centralization of data (1) provides attackers with one-stop honey-pot to steal data and intercept data in-motion and (2) moves data ownership to the cloud service providers; therefore, the individuals and healthcare providers lose control over sensitive data. As a result, security, privacy, efficiency, and scalability concerns are hindering the wide adoption of the cloud technology. In this work, we found that the state-of-the art solutions address only a subset of those concerns. Thus, there is an immediate need for a holistic solution that balances all the contradicting requirements.

Concept to gain trust for a German personal health record system using public cloud and FHIR

OBJECTIVE

Although a national Personal Health Record (PHR) infrastructure was supposed to have been introduced in Germany by law in 2006 and different providers are entering the market, no system has yet been widely adopted in Germany. There is also little information available on how current technical advancements affect German patients' and physicians' trust in PHR systems.

METHODS

Supporting scenarios obtained from clinicians, this study proposes a concept for a German PHR system using a public cloud infrastructure, smartphone access and focusing on trust, privacy, and interoperability. In advance to an eventual implementation, a multi-center questionnaire study has been conducted to predict patients' and physicians' intention to use that system and evaluate their trust in different providers of such a system.

RESULTS

Our results show that both patients and physicians are highly likely to use the PHR based on the present concept. Trust in healthcare providers exceeds trust in other institutions like private companies, health insurance companies, or even governmental institutions when offering such a PHR system.

CONCLUSIONS

We recommend the implementation of this PHR system. To maximize patients' and physicians' trust in the system, it should be offered to patients by their healthcare provider. Further evaluation regarding its actual adoption and expected improvement in patient outcome based on the scenarios is advisable.

Towards Development of an Integrated Cloud-Computing Adoption Framework — A Case of Indian School Education System

Many theoretical models viz., technology acceptance model (TAM), technology–organization–environment (TOE), diffusion of innovation (DOI), and human–organization–technology-fit (HOT-fit), etc., have been developed, validated, and tested to explain the acceptance of innovative technologies by the intended end users. However, given the limitations associated with these theoretical models as well as different cloud computing adoption scenario, they may not point out to the major constructs and the variables under so-called “selective contexts” in an explicit manner. Therefore, several research studies have been undertaken to integrate more than one model to provide a holistic evaluation of the determinants of cloud computing adoption for different domains. Such studies have also been conducted for education sector as well. But, the target of these studies is mostly specific to higher education using TOE or TAM models. To solve this limitation, we propose integrated approach of TAM, TOE framework, DOI, and HOT-fit frameworks in an effort to improve predictive power of proposed resulting model and, stretching the constructs to enrich the literature and implementing the same for Indian school education system as a case study.

Construction of medical equipment-based doctor health monitoring system

The health status of doctors has been overlooked by the society and even the doctors themselves, especially those doctors who work long hours. Their attention is always on patients, so they are more likely to ignore their own health problems. Therefore, in this paper, we propose a medical equipment-based doctor health monitoring system (hereinafter referred to as Doc-care). Doc-care can be used as a private health manager for doctors, and doctors can monitor their health indicators in real time while using medical equipment to aid diagnosis and treatment. When the doctor's health status is neglected, Doc-care can protect the doctor's health; combining with the convolutional neural network method to detect and grade the doctor's health indicators, to assess the doctor's real-time health status. After referring to the doctor's past health data in the cloud server, giving appropriate advice and predictions about the doctor's health status.

The dual roles of the government in cloud computing assimilation: an empirical study in China

Purpose

The purpose of this paper is to investigate the government roles in cloud computing assimilation along two dimensions: government regulation and government support.

Design/methodology/approach

A research model was developed to depict the dual roles of government regulation and government support in cloud computing assimilation as well as the mediating effect of top management support (TMS). Using survey data collected from 376 Chinese firms that have already adopted cloud services, the authors tested the research model.

Findings

The impacts of both government regulation and government support on cloud computing assimilation are partially mediated by TMS. Government support exerts stronger impacts on TMS than government regulation.

Research limitations/implications

This study extends the current information systems literature by highlighting the specific mechanisms through which governments influence firms’ assimilation of cloud computing.

Practical implications

Governments in developing countries could actively allocate funds or enact policies to effectively encourage cloud computing assimilation.

Originality/value

This study would complement previous findings about government regulation, and develop a more holistic understanding about the dual roles of governments in information technology innovation assimilation.

An Edge Computing Based Smart Healthcare Framework for Resource Management

The revolution in information technologies, and the spread of the Internet of Things (IoT) and smart city industrial systems, have fostered widespread use of smart systems. As a complex, 24/7 service, healthcare requires efficient and reliable follow-up on daily operations, service and resources. Cloud and edge computing are essential for smart and efficient healthcare systems in smart cities. Emergency departments (ED) are real-time systems with complex dynamic behavior, and they require tailored techniques to model, simulate and optimize system resources and service flow. ED issues are mainly due to resource shortage and resource assignment efficiency. In this paper, we propose a resource preservation net (RPN) framework using Petri net, integrated with custom cloud and edge computing suitable for ED systems. The proposed framework is designed to model non-consumable resources and is theoretically described and validated. RPN is applicable to a real-life scenario where key performance indicators such as patient length of stay (LoS), resource utilization rate and average patient waiting time are modeled and optimized. As the system must be reliable, efficient and secure, the use of cloud and edge computing is critical. The proposed framework is simulated, which highlights significant improvements in LoS, resource utilization and patient waiting time.

An Investigation of Pharmacists' Acceptance of NHI-PharmaCloud in Taiwan

Taiwan's National Health Insurance (NHI) is one of the most successful insurance programs in the world. The National Health Insurance Administration (NHIA) established the NHI-PharmaCloud as a platform to reduce medication duplication and other medication errors among the NHI-contracted facilities. The NHI-PharmaCloud can help pharmacists access patient medication information from the preceding 3 months to improve drug safety. The use of NHI-PharmaCloud can improve the quality of healthcare, but improvements cannot occur if pharmacists are unwilling to use the platform. Therefore, the main objective of our study is to investigate the factors affecting pharmacists' adoption of the NHI-PharmaCloud. This study develops a research model using theories of technology adoption, self-efficacy, and perceived risk and uses randomly distributed survey questionnaires to collect data from local pharmacists. The results show that self-efficacy, perceived usefulness, and perceived psychological risk are 3 critical factors that could affect pharmacists' willingness to use the NHI-PharmaCloud. The research results may also help NHIA to effectively promote the usage of the NHI-PharmaCloud in Taiwan. In addition, governments in other countries may refer to the results of this study when implementing their own PharmaCloud-type systems to improve drug safety.

Data Privacy Protection Based on Micro Aggregation with Dynamic Sensitive Attribute Updating

With the rapid development of information technology, large-scale personal data, including those collected by sensors or IoT devices, is stored in the cloud or data centers. In some cases, the owners of the cloud or data centers need to publish the data. Therefore, how to make the best use of the data in the risk of personal information leakage has become a popular research topic. The most common method of data privacy protection is the data anonymization, which has two main problems: (1) The availability of information after clustering will be reduced, and it cannot be flexibly adjusted. (2) Most methods are static. When the data is released multiple times, it will cause personal privacy leakage. To solve the problems, this article has two contributions. The first one is to propose a new method based on micro-aggregation to complete the process of clustering. In this way, the data availability and the privacy protection can be adjusted flexibly by considering the concepts of distance and information entropy. The second contribution of this article is to propose a dynamic update mechanism that guarantees that the individual privacy is not compromised after the data has been subjected to multiple releases, and minimizes the loss of information. At the end of the article, the algorithm is simulated with real data sets. The availability and advantages of the method are demonstrated by calculating the time, the average information loss and the number of forged data.

Rethinking the Meaning of Cloud Computing for Health Care: A Taxonomic Perspective and Future Research Directions

BACKGROUND

Cloud computing is an innovative paradigm that provides users with on-demand access to a shared pool of configurable computing resources such as servers, storage, and applications. Researchers claim that information technology (IT) services delivered via the cloud computing paradigm (ie, cloud computing services) provide major benefits for health care. However, due to a mismatch between our conceptual understanding of cloud computing for health care and the actual phenomenon in practice, the meaningful use of it for the health care industry cannot always be ensured. Although some studies have tried to conceptualize cloud computing or interpret this phenomenon for health care settings, they have mainly relied on its interpretation in a common context or have been heavily based on a general understanding of traditional health IT artifacts, leading to an insufficient or unspecific conceptual understanding of cloud computing for health care.

OBJECTIVE

We aim to generate insights into the concept of cloud computing for health IT research. We propose a taxonomy that can serve as a fundamental mechanism for organizing knowledge about cloud computing services in health care organizations to gain a deepened, specific understanding of cloud computing in health care. With the taxonomy, we focus on conceptualizing the relevant properties of cloud computing for service delivery to health care organizations and highlighting their specific meanings for health care.

METHODS

We employed a 2-stage approach in developing a taxonomy of cloud computing services for health care organizations. We conducted a structured literature review and 24 semistructured expert interviews in stage 1, drawing on data from theory and practice. In stage 2, we applied a systematic approach and relied on data from stage 1 to develop and evaluate the taxonomy using 14 iterations.

RESULTS

Our taxonomy is composed of 8 dimensions and 28 characteristics that are relevant for cloud computing services in health care organizations. By applying the taxonomy to classify existing cloud computing services identified from the literature and expert interviews, which also serves as a part of the taxonomy, we identified 7 specificities of cloud computing in health care. These specificities challenge what we have learned about cloud computing in general contexts or in traditional health IT from the previous literature. The summarized specificities suggest research opportunities and exemplary research questions for future health IT research on cloud computing.

CONCLUSIONS

By relying on perspectives from a taxonomy for cloud computing services for health care organizations, this study provides a solid conceptual cornerstone for cloud computing in health care. Moreover, the identified specificities of cloud computing and the related future research opportunities will serve as a valuable roadmap to facilitate more research into cloud computing in health care.

Potentials and Challenges of the Health Data Cooperative Model

INTRODUCTION

Currently, abundances of highly relevant health data are locked up in data silos due to decentralized storage and data protection laws. The health data cooperative (HDC) model is established to make this valuable data available for societal purposes. The aim of this study is to analyse the HDC model and its potentials and challenges.

RESULTS

An HDC is a health data bank. The HDC model has as core principles a cooperative approach, citizen-centredness, not-for-profit structure, data enquiry procedure, worldwide accessibility, cloud computing data storage, open source, and transparency about governance policy. HDC members have access to the HDC platform, which consists of the "core," the "app store," and the "big data." This, respectively, enables the users to collect, store, manage, and share health information, to analyse personal health data, and to conduct big data analytics. Identified potentials of the HDC model are digitization of healthcare information, citizen empowerment, knowledge benefit, patient empowerment, cloud computing data storage, and reduction in healthcare expenses. Nevertheless, there are also challenges linked with this approach, including privacy and data security, citizens' restraint, disclosure of clinical results, big data, and commercial interest. Limitations and Outlook: The results of this article are not generalizable because multiple studies with a limited number of study participants are included. Therefore, it is recommended to undertake further elaborate research on these topics among larger and various groups of individuals. Additionally, more pilots on the HDC model are required before it can be fully implemented. Moreover, when the HDC model becomes operational, further research on its performances should be undertaken.