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This paper aims to design a secure and seamless system that ensures quick sharing of health-care data to improve the privacy of sensitive health-care data, the efficiency of health-care infrastructure, effective treatment given to patients and encourage the development of new health-care technologies by researchers. These objectives are achieved through the proposed system, a “privacy-aware data tagging system using role-based access control for health-care data.”

Health-care data must be stored and shared in such a manner that the privacy of the patient is maintained. The method proposed, uses data tags to classify health-care data into various color codes which signify the sensitivity of data. It makes use of the ARX tool to anonymize raw health-care data and uses role-based access control as a means of ensuring only authenticated persons can access the data.

The system integrates the tagging and anonymizing of health-care data coupled with robust access control policies into one architecture. The paper discusses the proposed architecture, describes the algorithm used to tag health-care data, analyzes the metrics of the anonymized data against various attacks and devises a mathematical model for role-based access control.

The paper integrates three disparate topics – data tagging, anonymization and role-based access policies into one seamless architecture. Codifying health-care data into different tags based on International Classification of Diseases 10th Revision (ICD-10) codes and applying varying levels of anonymization for each data tag along with role-based access policies is unique to the system and also ensures the usability of data for research.

Health information exchange (HIE) initiatives utilize sharing mechanisms through which health information is mostly transmitted without a patient's close supervision; thus, patient trust in the HIE is the core in this setting. Existing technology acceptance theories mainly consider cognitive beliefs resulting in adoption behavior. The study argues that existing theories should be expanded to cover not only cognitive beliefs but also the emotion provoked by the sharing nature of the technology. Based on the theory of reasoned action, the technology adoption literature, and the trust literature, we theoretically explain and empirically test the impact of perceived transparency of privacy policy on cognitive trust and emotional trust in HIEs. Moreover, the study analyzes the effects of cognitive trust and emotional trust on the intention to opt in to HIEs and willingness to disclose health information.

An online survey was conducted using data from individuals who were aware of HIEs through experience with at least one provider participating in an HIE network. Data were collected from a wide range of adult population groups in the United States.

The structural equation modeling analysis results provide empirical support for the proposed model. The model highlights the strategic role of the perceived transparency of the privacy policy in building trust in HIEs. When patients know more about HIE security measures, sharing procedures, and privacy terms, they feel more in control, more assured, and less at risk. The results also show that patient trust in HIEs may take the forms of intention to opt in to an HIE and willingness to disclose health information exchanged through HIE networks.

The findings of this study should be of interest to both academics and practitioners. The research highlights the importance of developing and using a transparent privacy policy in the diffusion of HIEs. The findings provide a deep understanding of dimensions of HIE privacy policy that should be addressed by health-care organizations to exchange personal health information in a secure and private manner.

The purpose of this paper is to forward specific policy proposals permitting greater sharing of health data across multi-level government agencies with the purpose of improving rapid identification of bioterrorist attack or disease epidemics while protecting patient privacy.

A systematic literature review searched the following keyword phrases: knowledge sharing in the public sector, raw data sharing, interagency information systems, federal data sharing technology network and network theory on five primary databases.

The volunteer nature of data sharing must evolve through public health policy to permit interagency data access agreements while minimizing privacy infringement. A multi-level information infrastructure network linking agencies tasked to develop medical countermeasures is recommended.

This study optimizes the health data collection process to create a medical countermeasure network, demonstrates the utility of operationalizing data metrics for a US federal agency and advances meaningful use of electronic medical records.

This study aims to explore the institutionalization of value-based healthcare (VBHC) in the public healthcare system in the state of Victoria, Australia.

The empirical part of this paper is based on a content analysis of 34 policy and industry-commissioned reports that have guided the development of health-care strategy in Victoria from 1988 to 2020.

This study sheds light on how VBHC in Victoria has been institutionalized over time, through three key phases (centralization, transitioning and digitalization), how the conceptualization of best value has changed in each phase and the implications each phase has presented for other actors in the health-care system.

This study highlights the key opportunities and challenges for organizational actors that emerge when a health-care system transitions toward VBHC, and derives implications for vendors, health-care procurement, policymakers and governmental agencies.

This study develops a longitudinal analysis that describes the evolution and institutionalization of a VBHC approach in a complex societal system over three decades and highlights the key implications for other organizational stakeholders.

The purpose of this paper is to secure health data collection and transmission (SHDCT). In this system, a native network consists of portable smart devices that interact with multiple gateways. It entails IoMT devices and wearables connecting to exchange sensitive data with a sensor node which performs the aggeration process and then communicates the data using a Fog server. If the aggregator sensor loses the connection from the Fog server, it will be unable to submit data directly to the Fog server. The node transmits encrypted information with a neighboring sensor and sends it to the Fog server integrated with federated learning, which encrypts data to the existing data. The fog server performs the operations on the measured data, and the values are stored in the local storage area and later it is updated to the cloud server.

SHDCT uses an Internet-of-things (IoT)-based monitoring network, making it possible for smart devices to connect and interact with each other. The main purpose of the monitoring network has been in the collection of biological data and additional information from mobile devices to the patients. The monitoring network is composed of three different types of smart devices that is at the heart of the IoT.

It has been addressed in this work how to design an architecture for safe data aggregation in heterogeneous IoT-federated learning-enabled wireless sensor networks (WSNs), which makes use of basic encoding and data aggregation methods to achieve this. The authors suggest that the small gateway node (SGN) captures all of the sensed data from the SD and uses a simple, lightweight encoding scheme and cryptographic techniques to convey the data to the gateway node (GWN). The GWN gets all of the medical data from SGN and ensures that the data is accurate and up to date. If the data obtained is trustworthy, then the medical data should be aggregated and sent to the Fog server for further processing. The Java programming language simulates and analyzes the proposed SHDCT model for deployment and message initiation. When comparing the SHDCT scheme to the SPPDA and electrohydrodynamic atomisation (EHDA) schemes, the results show that the SHDCT method performs significantly better. When compared with the SPPDA and EHDA schemes, the suggested SHDCT plan necessitates a lower communication cost. In comparison to EHDA and SPPDA, SHDCT achieves 4.72% and 13.59% less, respectively. When compared to other transmission techniques, SHDCT has a higher transmission ratio. When compared with EHDA and SPPDA, SHDCT achieves 8.47% and 24.41% higher transmission ratios, respectively. When compared with other ways it uses less electricity. When compared with EHDA and SPPDA, SHDCT achieves 5.85% and 18.86% greater residual energy, respectively.

In the health care sector, a series of interconnected medical devices collect data using IoT networks in the health care domain. Preventive, predictive, personalized and participatory care is becoming increasingly popular in the health care sector. Safe data collection and transfer to a centralized server is a challenging scenario. This study presents a mechanism for SHDCT. The mechanism consists of Smart healthcare IoT devices working on federated learning that link up with one another to exchange health data. Health data is sensitive and needs to be exchanged securely and efficiently. In the mechanism, the sensing devices send data to a SGN. This SGN uses a lightweight encoding scheme and performs cryptography techniques to communicate the data with the GWN. The GWN gets all the health data from the SGN and makes it possible to confirm that the data is validated. If the received data is reliable, then aggregate the medical data and transmit it to the Fog server for further process. The performance parameters are compared with the other systems in terms of communication costs, transmission ratio and energy use.

Drawing from the extant literature on sensemaking theory, the purpose of this paper is to understand how physicians view health information exchange (HIE) implementation and how their stories frame the situation.

This paper utilizes content analysis with sensemaking theory as a theoretical lens to analyze physicians’ interviews.

The stories within this study draw attention to how sensemaking might impact the HIE implementation process. The findings demonstrated four well-defined manifest themes specific to sensemaking: bracketing, enactment, social and identity construction. There were sub-themes that cut across major themes: financial implications, practice changes and impact on professional reputation. The data demonstrated that each participant singled out items or events specific to the HIE change process in order to make sense of the change as an entirety.

No other study has applied sensemaking in an effort to gain insight into the ways physicians view the HIE process. Therefore, this study offers a unique perspective which might provide a framework through which to understand the possible barriers to successful implementation of HIE from a physician.

The current trend in Big Data analytics and in particular health information technology is toward building sophisticated models, methods and tools for business, operational and clinical intelligence. However, the critical issue of data quality required for these models is not getting the attention it deserves. The purpose of this paper is to highlight the issues of data quality in the context of Big Data health care analytics.

The insights presented in this paper are the results of analytics work that was done in different organizations on a variety of health data sets. The data sets include Medicare and Medicaid claims, provider enrollment data sets from both public and private sources, electronic health records from regional health centers accessed through partnerships with health care claims processing entities under health privacy protected guidelines.

Assessment of data quality in health care has to consider: first, the entire lifecycle of health data; second, problems arising from errors and inaccuracies in the data itself; third, the source(s) and the pedigree of the data; and fourth, how the underlying purpose of data collection impact the analytic processing and knowledge expected to be derived. Automation in the form of data handling, storage, entry and processing technologies is to be viewed as a double-edged sword. At one level, automation can be a good solution, while at another level it can create a different set of data quality issues. Implementation of health care analytics with Big Data is enabled by a road map that addresses the organizational and technological aspects of data quality assurance.

The value derived from the use of analytics should be the primary determinant of data quality. Based on this premise, health care enterprises embracing Big Data should have a road map for a systematic approach to data quality. Health care data quality problems can be so very specific that organizations might have to build their own custom software or data quality rule engines.

Today, data quality issues are diagnosed and addressed in a piece-meal fashion. The authors recommend a data lifecycle approach and provide a road map, that is more appropriate with the dimensions of Big Data and fits different stages in the analytical workflow.

Using the resource-based theoretical view of the firm, this paper aims to explore how firms’ efforts to integrate blockchain technology (BCT) into their supply chain systems and activities enable certain supply chain capabilities and, consequently, improve their supply chain performance.

Using an abductive research approach, a qualitative content analysis was conducted on 126 cases of firms attempting to implement a blockchain technology-enabled supply chain system (BCTeSCS). These firms spanning across multiple industries were identified using the Nexis Uni database.

Findings reveal that present BCTeSCS efforts are more-oriented toward improving operational-level capabilities (information sharing and coordination capabilities) than strategic-level capabilities (integration and collaboration capabilities). These operational and strategic-level capabilities alongside BCTeSCS deliver several supply chains performance outcomes such as quality compliance and improvement, process improvement, flexibility, reduced cost and reduced process time. However, outcomes may vary by industry type based on their uncertainties.

Given the nascent state of BCT, accessibility to primary data about ongoing BCTeSCS efforts is limited. The presented framework is based on 126 cases of secondary information. Within this constraint, the paper finds scope to future empirical research by proposing a resource-based framework of BCTeSCS and related propositions.

The results and discussion of this study serve as useful guidance for practitioners involved in BCTeSCS integrations.

The paper creates a BCTeSCS scenario for stakeholders to assume its potential socio-economic and socio-environmental pressures.

This paper is one of the initial attempts to examine BCTeSCS efforts across multiple industries, and thus, promises a broad future research scope.

An emerging technology in the primary stages of its life cycle is the blockchain. This research paper aims to evaluate the preparedness of hospitals in using blockchain technology in their electronic health record (EHR) systems.

In the initial stage, 15 criteria relating to preparedness in using blockchain in EHR systems were identified from the literature and divided into five criteria, namely, technological, legal, financial, environmental and organizational. Then, 17 experts from various specialized fields were invited to form expert panels. After validating the criteria identified by the expert panels, the weights of the criteria were determined through the fuzzy best-worst multicriteria decision-making method. Following that, the preparedness of ten selected hospitals in Tehran to use blockchain in their EHR systems was assessed via the weighted aggregated sum product assessment method. Finally, using sensitivity analysis and examining different scenarios, the robustness of the results of the proposed approach was validated.

According to expert judgments, the legal criterion (32%) was deemed the most important factor in the preparedness to use blockchain in EHR systems followed by technological (28%), financial (17%), organizational (13%) and environmental (9%) criteria. A sensitivity analysis showed that the proposed approach offers good strength and robustness in evaluating the selected hospitals.

This study can be useful in developing knowledge in the field of technology management for evaluating blockchain implementation in the health-care industry using a novel, coherent and robust approach. In addition, the proposed approach provides comprehensive insight for decision-makers on assessing preparedness in deploying blockchain technology in EHR systems.