Smart home technologies are increasingly used to support aging in place, yet safety, autonomy, and privacy are often examined separately. This scoping review mapped smart-home functions and analyzed how these values interact in later-life domestic settings. Guided by the Preferred Reporting Items for Systematic Reviews and Meta Analyses for Scoping Reviews and the Sample, Phenomenon of Interest, Design, Evaluation, and Research type framework, seven databases were searched for English-language, full-length, peer-reviewed articles published from January 2000 to 23 May 2026. The final value-oriented corpus included 218 sources. Eleven functional categories were identified, with caregiver support and remote care, fall detection and prevention, social connection, cognitive support, and mobility assistance being the most prominent. The synthesis identified three recurrent mechanisms: surveillance intensification, control displacement, and redistribution of authority and responsibility. The findings indicated that smart-home adaptation requires proportionate monitoring, negotiable user control, and periodic recalibration of permissions, care roles, and accountability.
This article examines the barriers and challenges affecting the integration of fintech into the Saudi Arabian healthcare system. Using a cross-sectional survey design, a study identified 10 barriers from the literature and incorporated them into a questionnaire completed by 395 healthcare workers from Saudi public hospitals, including physicians, specialist physicians, nurses, nurse managers, administrators, and managers. The findings showed that data security, privacy concerns, and the regulatory environment were the most significant barriers, with mean values of 4.26 and 4.21, respectively. Perceptions of the regulatory environment differed significantly across professional roles (p < .0001), with specialist physicians reporting the highest concern. The results emphasize the need for tailored strategies that address role-specific concerns and support effective fintech adoption in Saudi healthcare.
The editorial urges setting aside narrow, efficiency-obsessed models of healthcare quality. It proposes a human-centered alternative that sees patients as co-producers of health and processes as a support web, which is measured successfully by the depth of healing experience. The ultimate goal is nothing less than a revolution—to create health systems not only functioning, but resilient, adaptive, and empowering.
This study measured the quality of discharge teaching and discharge readiness levels in ischemic stroke patients to understand the factors affecting the discharge. A total of 120 patients with mild-to-moderate ischemic stroke were recruited using simple random sampling. Three instruments, namely, a demographic data questionnaire, the readiness for hospital discharge scale, and the quality of discharge teaching scale, were used for data collection. Descriptive statistical analysis and parametric tests with a correlation matrix, one-way analysis of variance, and independent t-tests were used for data analysis, providing robust evidence that the quality of discharge teaching was strongly correlated with patient readiness (r = 0.625, p<0.001) and underscoring that effective education was a key, modifiable factor for improving outcomes. The results advocated for preemptive screening and individualized discharge plans to enhance care quality and reduce readmission risks.
Cybersecurity vulnerabilities in medical devices can have immediate and potentially life-threatening consequences. A successful cyberattack on an implantable device may alter its functioning, disrupt its performance, or disable it entirely, directly jeopardizing the patient’s health and safety. Beyond physical harm, these vulnerabilities also endanger informational privacy, as such devices collect, store, and transmit highly sensitive health data. Unauthorized access to such data can lead to exploitation, discrimination, and loss of patient trust in digital health-care systems. Thus, cybersecurity weaknesses simultaneously undermine two deeply interconnected rights—the right to health and the right to privacy—both of which are recognized as integral components of the right to life and human dignity. This paper examines the intersection between medical device cybersecurity and the protection of fundamental rights through a legal and ethical lens. The study emphasizes that digital safety is not merely a technical or regulatory requirement but a moral, ethical, and legal imperative.
The purpose of the study is to understand the relationship between stress, burnout, and interpersonal conflicts among hospital nurses in Karnataka, India. The authors propose, in an Affective Events Theory (AET) perspective, that high stress and burnout are antecedents of interpersonal conflicts. A quantitative research method was used, and 636 nurses participated in responding to the stress, burnout, and interpersonal conflict questionnaire. Factorial and regression analyses were used to analyze data, which indicated a significant positive association among stress, burnout, and interpersonal conflict. The investigation suggests stress and burnout are powerful predictors of interpersonal conflict. To prevent the impact of stress and burnout on nursing staff, as well as to ensure a healthy work environment, the study highlights the need for effective interventions, including stress management strategies and policy changes.
The study investigates the impact of task characteristics and technology characteristics on task-technology fit (TTF) and the subsequent effects of TTF on behavioral intention to use and user satisfaction within the context of the mobile application for Indonesia’s national health insurance, Jaminan Kesehatan Nasional. The study employs a quantitative research design and a survey-based approach to examine the relationships among key constructs. A structural model was tested using data from 456 active application users. The results indicate that technology characteristics significantly influence TTF, which itself positively affects satisfaction and behavioral intention to use. In contrast, task characteristics showed no significant effect on TTF, suggesting that technological functionality may mitigate task complexity. These findings highlight the importance of designing user-centric, context-sensitive m-Health systems. The study provides practical insights for policymakers in emerging economies to align system features with user needs, enhancing engagement and the overall impact of digital health services.
A toddler’s growth can be assessed in two aspects: nutritional status and physical status. On the physical side, stunting is a condition in which height is not in accordance with age. In this study, a hybrid bi-response model combining logistic regression (LR) and a support vector machine (SVM) was developed to assess its performance in classifying toddler status in Wajak Village, Malang Regency. The data used in this study were primary data collected from questionnaires completed by mothers of toddlers. The response variables in this study consist of two: nutritional status and physical stunting status in toddlers. The method used was a hybrid bi-response model combining LR and an SVM, both of which are supervised learning methods. This study found that hybrid LR and the SVM bi-response performed better at classifying data with two response variables than LR or an SVM alone with an accuracy of 94.43%, sensitivity of 92%, and specificity of 94.38%.
Health information systems are vital for healthcare modernization; however, their implementation across Saudi Arabia remains inconsistent due to technical, organizational, and governance challenges. Current evaluation methods lack lifecycle coverage and fail to align with international standards. This study proposes a lifecycle-based evaluation framework tailored to the Saudi context, integrating findable, accessible, interoperable, reusable principles along with Health Level Seven (HL7)/Fast Healthcare Interoperability Resources (FHIR) standards and the CApable Reuse of EHR Data (CARED) architecture across three phases: pre-implementation, implementation, and post-deployment. Developed using a design science research methodology, the framework addresses key gaps in planning, interoperability, usability, and the use of secondary data. A multi-phase evaluation strategy comprising Delphi consensus, literature benchmarking, scenario-based simulations, and pilot trials is recommended. The framework incorporates measurable indicators and scalable tools to support Saudi Vision 2030, enhance the effectiveness of the health information system, and guide policymakers and informatics leaders in developing sustainable, standards-aligned digital health infrastructure.
The phrase social exclusion came to Australia via Britain. Discussion on ‘inclusion and exclusion' mainly focus on those individuals and groups living in poverty. Socially excluded occurs as a result of many factors, based on sex, race, ethnic origin, religion, sexual orientation, and disability. Until 2012 people with a disability were excluded from Australian' resettlement programs representing a significant discrimination concern. As a result of strong opposition to this issue, the policy changed in 2012 and the number of refugees with disabilities increased. This in turn led to more ethnic diversity of people with disabilities within NDIS. However, since NDIS has not been able to provide adequate services to these populations, refugees with a disability continue to be excluded. This paper advocates inclusion continuation of refugees with a disability based on their rights to live a life of inclusion and dignity. These people have been facing double discriminations that requires Government attention to remove existing barriers.
Food nutrition is the source of heat energy in human body. The human body obtains nutrients from food, such as proteins, fats, carbohydrates and so on. Nutrition is very important for human health. The technology is used in every sectors. Indeed, AI is emerging as an important tool in clinical nutrition using telematic means to self-monitor various health metrics, including blood glucose levels, body weight, heart rate, fat percentage, blood pressure, activity tracking and calorie intake trackers. In particular, the most common application of digital technology is in the area of nutrition. Data Analytics and similar technologies play a leading role, and here in this paper different aspect related to the Big-data and Artificial Intelligence with references to the Food and Nutritional Systems have been described with current and future scenario. The existing works related to the fields are well described and reported with major highlights.
This study aims to identify the nature and prevalence of patient safety incidents and the design, development, and implementation of a patient-reported incident management system. The system's effectiveness in capturing patient-reported incidents and promoting patient empowerment was also evaluated. Findings reveal that nearly 33.8% of patients reported experiencing a patient safety incident, with 81.6% of these incidents identified by the patients themselves. After the implementation of the system, 47% of reported incidents were addressed by staff, indicating a significant improvement in patient empowerment. Furthermore, there was a 71% increase in overall incident reporting. Following the introduction of the patient-reported incident management system, 67% of patients reported feeling more empowered, and 53% provided positive feedback regarding the system's user-friendliness.
The landscape of healthcare delivery and management is undergoing a transformative evolution, driven by the emergence of self-organizing processes, fractal scaling, and attractors within the industry. These concepts are redefining the way healthcare entities operate, collaborate, and adapt to changing patient needs and technological advancements. The landscape of healthcare delivery and management is undergoing a transformative evolution, driven by the emergence of self-organizing processes, fractal scaling, and attractors within the industry. These concepts are redefining the way healthcare entities operate, collaborate, and adapt to changing patient needs and technological advancements.
The landscape of healthcare delivery and management is undergoing a transformative evolution, driven by the emergence of self-organizing processes, fractal scaling, and attractors within the industry. These concepts are redefining the way healthcare entities operate, collaborate, and adapt to changing patient needs and technological advancements. The landscape of healthcare delivery and management is undergoing a transformative evolution, driven by the emergence of self-organizing processes, fractal scaling, and attractors within the industry. These concepts are redefining the way healthcare entities operate, collaborate, and adapt to changing patient needs and technological advancements.
The research emphasizes the effectiveness of Bayesian classification algorithms in predicting patient visits in healthcare settings. Bayesian algorithms examine past patient data to detect intricate patterns in admission dynamics, including demographic, clinical, and temporal factors. Through the use of Bayesian principles, prediction models are able to estimate the probability of certain patient demographics occurring at certain intervals, therefore assisting in the allocation of resources and the management of operations. Probabilities that have been estimated are used to make choices on staffing, resource allocation, and operational strategy. The variation in probability estimates across different observations improves the predictive usefulness, hence strengthening the effectiveness in healthcare management and planning.
This research examines the significance of organizational culture (OC) among the staff of medical centers which are at the forefront of healthcare deliveries. It illustrates the extent to which there is a prevalence of understandable culture among the staff and demonstrates the strength of whether the subculture mirrors the dominant culture among medical and non-medical staff. To perform this analysis the sample categorized into two groups to know the strengths of association and difference between the responses of each group. It found that there is a hierarchical culture that mirrors the dominant culture the of center whereas there is no subculture which makes the employees less innovative due to their work nature. The study suggests that medical centers need to approach employee engagement in making decisions about the cultures or subcultures in an organization which will affect the employee's productivity positively.
COVID-19 prediction models are highly welcome and necessary for authorities to make informed decisions. Traditional models, which were used in the past, were unable to reliably estimate death rates due to procedural flaws. The genetic algorithm in association with an artificial neural network (GA-ANN) is one of the suitable blended AI strategies that can foretell more correctly by resolving this difficult COVID-19 phenomena. The genetic algorithm is used to simultaneously optimise all of the ANN parameters. In this work, GA-ANN and ANN models were performed by applying historical daily data from sick, recovered, and dead people in India. The performance of the designed hybrid GA-ANN model is validated by comparing it to the standard ANN and MLR approach. It was determined that the GA-ANN model outperformed the ANN model. When compared to previous examined models for predicting mortality rates in India, the hypothesized hybrid GA-ANN model is the most competent. This hybrid AI (GA-ANN) model is suggested for the prediction due to reasonably better performance and ease of implementation.
Medical education is experimenting with different tools to make teaching-learning more compatible with the medical curriculum. One such addition is blended learning, which combines traditional teaching with e-learning. The study aims to assess the effectiveness of combining e-learning and traditional face-to-face gross anatomy teaching in undergraduate medical students. This collaborative study was done in the Department of Anatomy, A.C.S Medical College and Hospital, Dr. M.G.R. Educational and Research Institute (Deemed to be University). One hundred fourteen students volunteered to participate in the study. Six topics from the gross anatomy of the abdomen were chosen for the study. An overall pre-test questionnaire was delivered with the didactic lectures. Another pre-test questionnaire was given about the selected topic before sharing the online learning materials. A post-test questionnaire in Google form was collected at the end of the day. Feedback was collected from all study participants.
Coronavirus has spread worldwide, with over 688 million confirmed cases and 6.8 million deaths. The results could be important as containment restrictions begin to be relaxed and we are not immune to new strains. They underscore the need to introduce increasingly effective techniques to deal with such a spread and help identify new infections more quickly, at a reasonable cost and with a minimum error rate. Machine learning models constitute a new approach, used increasingly in this field. In this proposed work, the authors built a classification model named CovStacknet based on StackNet metamodeling methodology combined with the deep convolutional neural network as the basis for feature extraction from x-ray images. Firstly, the proposed model used VGG16 as a transfer learning of deep convolutional neural networks and achieved an accuracy score of 98%. Secondly, the proposed model is extended to evaluate four other deep convolutional neural networks, ResNet-50, Inception-V3, MobileNet-V2 and DenseNet, and ResNet-50, has achieved the best performance.
In recent years, there have been many attempts to introduce blockchain-based identity management solutions, which allow the user to take over control of his/her own identity. In this paper, the authors have reviewed in-depth existing blockchain-based identity management papers and patents published online. Based on that analysis of the literature, a system will be implemented which will come up with the current issues and try to minimize them. Being transparent, immutable, and decentralized in nature, blockchain mechanism is found to be a better technology which can reduce the corruption in the experimental scenario. The objective is to develop a decentralized system which can be used for the verification of the employees in an organization. This is done to stop or reduce the cases of identity theft and data leakage in recent time. This system will be using Ethereum blockchain platform for monitoring the information and smart contract for authentication.
