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This paper tactics to implement the attack detection in medical Internet of things (IoT) devices using improved deep learning architecture for accomplishing the concept bring your own device (BYOD). Here, a simulation-based hospital environment is modeled where many IoT devices or medical equipment are communicated with each other. The node or the device, which is creating the attack are recognized with the support of attribute collection. The dataset pertaining to the attack detection in medical IoT is gathered from each node that is considered as features. These features are subjected to a deep belief network (DBN), which is a part of deep learning algorithm. Despite the existing DBN, the number of hidden neurons of DBN is tuned or optimized correctly with the help of a hybrid meta-heuristic algorithm by merging grasshopper optimization algorithm (GOA) and spider monkey optimization (SMO) in order to enhance the accuracy of detection. The hybrid algorithm is termed as local leader phase-based GOA (LLP-GOA). The DBN is used to train the nodes by creating the data library with attack details, thus maintaining accurate detection during testing.

This paper has presented novel attack detection in medical IoT devices using improved deep learning architecture as BYOD. With this, this paper aims to show the high convergence and better performance in detecting attacks in the hospital network.

From the analysis, the overall performance analysis of the proposed LLP-GOA-based DBN in terms of accuracy was 0.25% better than particle swarm optimization (PSO)-DBN, 0.15% enhanced than grey wolf algorithm (GWO)-DBN, 0.26% enhanced than SMO-DBN and 0.43% enhanced than GOA-DBN. Similarly, the accuracy of the proposed LLP-GOA-DBN model was 13% better than support vector machine (SVM), 5.4% enhanced than k-nearest neighbor (KNN), 8.7% finer than neural network (NN) and 3.5% enhanced than DBN.

This paper adopts a hybrid algorithm termed as LLP-GOA for the accurate detection of attacks in medical IoT for improving the enhanced security in healthcare sector using the optimized deep learning. This is the first work which utilizes LLP-GOA algorithm for improving the performance of DBN for enhancing the security in the healthcare sector.

Approaches for the systematic evaluation of nursing practice are outlined. Practical methods and steps in auditing care at ward and individual patient level are discussed. Standard‐setting at ward level is systematically described and examples given. Monitoring and measuring outcomes and individual patient unit (IPA) are also described.

The medical advances and historical fluctuations in the demographics are contributing to the rise of the average age. These changes are increasing the pressure to organize adequate care to a growing number of individuals. As a way to provide efficient and cost-effective care, eHealth systems are gaining importance. However, this trend is creating new ethical concerns. Major issues are privacy and patients’ control over their data. To deploy these systems on a large scale, they need to offer strict privacy protection. Even though many research proposals focus on eHealth systems and related ethical requirements, there is an evident lack of practical solutions for protecting users’ personal information. The purpose of this study is to explore the ethical considerations related to these systems and extract the privacy requirements. This paper also aims to put forth a system design which ensures appropriate privacy protection.

This paper investigates the existing work in the area of eHealth systems and the related ethical considerations, which establish privacy as one of the main requirements. It lists the ethical requirements and data protection standards that a system needs to fulfil and uses them as a guideline for creating the proposed design.

Even though privacy is considered to be a paramount aspect of the eHealth systems, the existing proposals do not tackle this issue from the outset of the design. Consequently, introducing privacy at the final stages of the system deployment imposes significant limitations and the provided data protection is not always to the standards expected by the users.

This paper motivates the need for addressing ethical concerns in the eHealth domain with special focus on establishing strict privacy protection. It lists the privacy requirements and offers practical solutions for developing a privacy-friendly system and takes the approach of privacy-by-design. Additionally, the proposed design is evaluated against ethical principles as proposed in the existing literature. The aim is to show that technological advances can be used to improve quality and efficiency of care, while the usually raised concerns can be avoided.

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 mental healthcare is experiencing an ever-growing surge in understanding the consumer (e.g., patient) engagement paradox, aiming to vouch for the quality of care. Despite this surge, scant attention has been given in academia to conceptualize and empirically investigate this particular aspect. Thus, drawing on the Stimulus-Organism-Response (S-O-R) paradigm, the study explores how patients engage with healthcare service providers and how they perceive the quality of the healthcare services.

Data were collected from 279 respondents, and the derived conceptual model was tested by using Smart PLS 3.2.7 and PROCESS. To complement the findings of partial least squares (PLS)-based structural equation modeling (SEM), the present study also applied fuzzy set qualitative comparative analysis (fsQCA) to identify the necessary and sufficient conditions to explore substitute conjunctive paths that emerge.

Findings show that patients’ perceived intimacy (PI), cohesion and privacy enhance the quality of mental healthcare service providers. The results also suggest that patients’ PI, cohesion and privacy have indirect effects on the perceived quality of care (PQC) by the service providers through consumer engagement. The fsQCA results derive that the relationship among conditions leading to patients’ perception of the quality of care in regard to mental healthcare service providers is complex and is best reflected as multiple and conjectural causation configurations.

The findings from this research contribute to the advancement of studies on patients’ experiences by empirically examining the unique dynamics of interaction between consumers (patients) and mental healthcare service providers, thereby enriching both the literature on social interactions and the understanding of the consumer–provider relationship.

The results of this study provide practical implications for mental healthcare service providers on how to combine the study variables to enhance the quality of care and satisfy more patients.

A significant research gap has ascertained the inter-relationship between PI, cohesion, privacy, engagement and PQC from the perspective of mental healthcare service providers. This research is one of the primary studies from a managerial and methodological standpoint. The study contributes by combining symmetric and asymmetric statistical tools in service marketing and healthcare research. Furthermore, the application of fsQCA helps to understand the interactions that might not be immediately obvious through traditional symmetric methods.

Patient-centric exchanges, a major type of Health Information Exchange (HIE), empower patients to aggregate and manage their health information. This exchange model helps patients access, modify and share their medical information with multiple healthcare organizations. Although existing studies examine patient engagement, more research is required to investigate patients' attitudes and willingness to play an active role in patient-centered information exchange. The study's main objective is to develop a model based on the belief-attitude-intention paradigm to empirically examine the effects of patients' attitudes toward engagement in care on their willingness to participate in patient-centric HIE.

The authors conducted an online survey study to identify the antecedents and consequences of patients' attitudes toward engagement in care. To empirically test the research model, the authors collected data from a national sample (n = 357) of individuals in the United States. The data were analyzed using structural equation modeling (SEM).

The proposed model categorizes the antecedents to patients' attitudes toward engagement in patient-related and healthcare system factors. The results show that patient-related factors (perceived health literacy and perceived coping ability) and health system factors (perceived experience with the healthcare organization and perceived patient-provider interaction) significantly shape patient attitude toward care management engagement. The results indicate that patients' attitudes toward engaging in their healthcare significantly contribute to their willingness to participate in medical information sharing through patient-centric HIE initiatives. Moreover, the authors’ findings also demonstrate that the link between patient engagement and willingness to participate in HIE is stronger for individuals who perceive lower levels of privacy and security concerns.

The authors validate the proposed model explaining patients' perceptions about their characteristics and the healthcare system significantly influence their attitude toward engaging in their care. This study also suggests that patients' favorable attitude toward engagement can bring patient-centric HIE efforts onto a path to success. The authors’ research attempts to shed light on the importance of patients' roles in adopting patient-centric HIE initiatives. Theoretical and practical contributions of this study are noticeable since they could result in a deeper understanding of the concept of patient engagement and how it may affect healthcare services in an evolving digital world. The authors’ findings can help healthcare organizations provide public citizen-centric services by introducing user-oriented approaches in healthcare delivery systems.

Patient right is the most important ethical right in the hospital, which equally, belongs to every human kind. Observance of patient right is responsibility of all treatment staff when they offer treatment and care for patient. This study aims to investigate observance of patients’ rights in emergency department of educational hospitals in south-east Iran.

This study used a cross-sectional design and was conducted in four educational hospitals affiliated with the Kerman University of Medical Sciences in 2018. Using a two-section standard questionnaire of Patients’ Rights Charter, this study assessed patients’ rights observance using a census method, N = 382. The data from the questionnaire were analyzed using descriptive statistics including mean and standard deviations and analytic statistics such as Kolmogorov – Smirnov, ANOVA, t-test and Pearson test using SPSS 21.

Means of total score for observing all essentials of patients’ rights in emergency department of educational hospitals were at a moderate level (43.10 ±15.05) from the viewpoint of patients. The area of “providing health services based on respecting patient’s privacy and observing the essentials of secrecy and confidentiality” enjoying the highest mean score (86.89 ± 24.39), was at a good level compared to other areas. The area of “having access to effective complaint management system” showed the lowest mean score (23/85 ± 23/07) from the participants’ perspective proving a poor level. Between the patient rights observance and gender, education level, resident status and duration of hospitalization, a significant relationship was observed.

As regarded in this study, the degree of patients’ rights observance was moderate so, culture, paying attention to the rights of all stakeholders, identifying barriers and various factors, including the professional and environmental differences in the assessment of the need, should be considered by policymakers to design promotional and regulatory programs for improving the rights of the patient.

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.

This paper aims to analyze visitors’ waiting behavior in corridors of an internal medicine ward in relation to concepts of territoriality and privacy.

Waiting activities, visitors’ room numbers and duration of activities were recorded on a floor plan. Results were tallied according to behavioral and architectural zones in which the activity took place.

Locations that are near patient rooms that provide visual and auditory access are largely used as territorial areas for non-privacy-required activities. Ends of corridors, secondary corridors and staircases were mainly used for activities that required some level of privacy such as grieving.

As is true with post-occupancy evaluations in other single buildings, this research may not be generalizable to all internal medicine wards. Future research could measure whether responding to visitors’ spatial needs could result in lower density and sense of crowding in the corridors, as well as reduce stress and task interruptions and increase efficiency of patient-check rounds.

The findings indicate that internal medicine wards should include waiting areas near the patient rooms for visitors to be able to keep visual and auditory connection with their patients, as well as areas that provide privacy. This may help lower density, sense of crowding, distraction of medical staff, stress and burn-out and errors, as well as increase the efficiency of patient checks.

The ways in which architectural design of internal medicine corridors can support visitors’ activities and environmental needs such as territoriality and privacy is an issue that is rarely examined. This paper also adds an example from a non-Western culture, to a literature that is dominated by examples from Western cultures.