Informatics for health & social care最新文献

The research aims to explore the theoretical aspects of using a virtual digital person as an emotional companion and to reveal the possibilities offered by the synthesis of design, AI, computer science, and modern technologies. Analytical methods, machine learning, and neural networks were used. The results of the study showed that the use of AI has great potential for creating a virtual person adapted to aging. The virtual digital person developed with the help of AI was specially adapted to the unique needs and requirements of the elderly. It provided not only emotional support but also served as an information resource, helping to monitor medical conditions, remind them to take medications and provide advice on activity and health care. It has been found that the creation of a virtual digital person as an emotional companion is an important step in expanding the possibilities of interaction between people and technology, in particular in the context of the growing age of the population and the need for support. The possibility of using the results obtained in practical activities will allow the use of AI to develop new methods for creating a virtual digital person.

Health-related quality of life (HrQoL) has emerged as a key metric in the management of non-communicable diseases (NCDs), reflecting the global shift from acute infectious conditions to chronic illnesses. This shift indicates a broader movement toward patient-centered care. Amidst global digital health transformations, a synthesized understanding of the HrQoL research landscape is essential. To address this gap, the study adopts the PRISMA framework to present a combined bibliometric and machine learning-based Latent Dirichlet Allocation (LDA) topic modeling analysis of 353 articles, retrieved from Scopus and Web of Science between 2015 and 2024. Using R Studio and VOSviewer, the study maps publication trends, influential authors, sources, and countries, revealing an expanded research interest across related disciplines. Five key research topics were identified: interventions and disease management, psychosocial wellbeing, digital health technologies, epidemiological research, and preventive healthcare. These represent a thematic evolution from clinical protocols to more integrative and digitally responsive models. The findings highlight the need for culturally sensitive, interdisciplinary approaches that bridge clinical care with social determinants of health. Through its computational approach, the study offers actionable insights for researchers, clinicians, and policymakers seeking to enhance the quality of life of individuals living with chronic conditions.

This study aims to develop a customized Competency Diagnosis Program for Personal Information Protection that enables medical institution personnel to independently assess their competency levels in personal information protection and receive tailored educational materials. The ultimate goal is to enhance their competency and awareness regarding personal information protection. The program was developed by incorporating institutional aspects reflecting amendments to relevant laws, technical aspects considering the digitalization of medical information, and administrative aspects aligned with the practical realities of medical institutions. Additionally, to ensure precise competency diagnosis, a newly designed questionnaire was implemented, allowing diagnosis at each stage of personal information processing. With the rapid digitalization of medical information driven by advances in information and communication technology, enhancing the competency and awareness of medical institution personnel in personal information protection has become increasingly critical. By utilizing the newly designed competency diagnosis questionnaire and program developed in this study, any South Korean medical institution personnel can independently assess their competency levels and receive targeted educational materials for areas requiring improvement. This is expected to have a positive impact on enhancing competency and raising awareness of personal information protection.

Digital health technologies are revolutionizing the treatment of diabetes by providing creative ways to enhance patient outcomes. In this study, the role of digital medicines in the treatment of diabetes is examined with special attention paid to wearable technology, mobile applications, and clinical data that backs up their usage. Software-driven interventions designed to prevent, manage, or treat diabetes using specialized data-driven methods are referred to as digital therapies. The article examines many smartphone applications that support insulin management, nutritional tracking, blood glucose monitoring, and lifestyle changes. It examines important clinical trials that demonstrate how successfully these tools reduce HbA1c levels, enhance glycemic control, and promote long-term treatment regimen adherence. New advancements in AI such as personalized AI algorithms, integration of continuous glucose monitors with mobile apps, remote patient monitoring, telemedicine, behavioral nudges, machine learning, and data analytics that enhance the personalization of diabetic care are also the subject of the evaluation. Digital treatments have the potential to revolutionize diabetes care by providing more accessible, patient-centered, and effective care.

This pilot study evaluated the effectiveness and usability of the Mobithera application, a mobile health intervention designed to reduce musculoskeletal pain and improve physical function in adult caregivers of oncology patients. Thirty-five caregivers with self-reported neck or lower back pain participated in this study. The intervention involved 10 sessions over two weeks, during which participants performed therapeutic exercises using the Mobithera. Pain levels, lower extremity reaction time, body awareness, and balance were assessed before and after the intervention using to the Visual Analog Scale (VAS), Rapid Foot-Tap Test, Fremantle Awareness Scales, and Tinetti Gait and Balance Test. Usability of the application was evaluated using the System Usability Scale (SUS). The Mobithera significantly reduced both neck and lower back pain among participants. Improvements were also observed in lower extremity reaction time, body awareness, and balance. The findings suggest that the Mobithera is effective in enhancing the physical health and well-being of caregivers. The Mobithera demonstrates significant potential as a supportive tool for caregivers, reducing pain and improving physical function. Further research with larger sample sizes and longer follow-up periods is recommended to confirm these findings and explore the long-term benefits of such interventions in the caregiver population.

Background: YouTube has become a major source of health information, including content on dental cleaning. However, reliability and quality of videos remain unclear. This study evaluated quality and reliability of YouTube videos related to dental cleaning by comparing two time periods: before and after the COVID-19 pandemic.

Methods: Google Trends identified "dental cleaning" as the most searched term related to non-surgical periodontal therapy. Two periodontists evaluated the 100 YouTube™ videos from 2019 and 2024 using VIQI, DISCERN, Modified DISCERN, GQS, and a customized content analysis. Statistical analyses were conducted using IBM SPSS 26.0.

Results: A total of 28 videos from 2019 and 34 videos from 2024 were included. GQS scores were significantly higher in 2019 compared to 2024 (p < .001). In both years, "useful" videos had longer durations, higher DISCERN (p < .05), Modified DISCERN (p < .05), GQS (p = .001), and VIQI scores (p < .001). Content Analysis scores were positively correlated with all quality indices (p < .001).

Conclusion: Despite an increase in the number of useful videos according to Customized Content Index in 2024, the overall quality and reliability of YouTube™ videos on dental cleaning remain limited, highlighting the need for more accurate and professional content.

The monitoring, diagnosis, and treatment of patients has been completely transformed by the Internet of Things (IoT) in healthcare, especially in underprivileged and distant locations. IoT-enabled remote patient monitoring (RPM) makes it possible to gather and transmit health data like heart rate, glucose levels, and blood pressure in real-time, facilitating proactive and individualized care. IoT-enabled smart wearable devices like smartwatches, ECG patches, and fitness bands track physical activity and health parameters provide real-time data to clinicians via mobile apps or cloud servers and empower patients with self-monitoring tools. IoT-enabled RFID tags to locate critical medical equipment (e.g. ventilators, infusion pumps) enhances operational efficiency and reduces loss or theft and assists in hospital inventory and supply chain management. IoT devices facilitate real-time consultation with physicians using health data from patients and bridges the healthcare gap in rural and underserved areas. With a focus on RPM systems, Smart Wearables and RFID tags this chapter examines the architecture, technology, uses, and advantages of IoT in healthcare. The difficulties with interoperability, system integration, security, and data privacy are also covered. IoT has the revolutionary potential to improve healthcare outcomes, lower readmission rates to hospitals, and improve the quality of life for patients with chronic illnesses, as demonstrated by case studies and recent advances.

The prevalence of chronic diseases necessitates continuous health monitoring and personalized management strategies to optimize patient outcomes. Traditional healthcare approaches, reliant on periodic assessments, often fail to provide the real-time insights needed for effective chronic disease management. This research introduces SmartWear, an innovative system integrating Edge AI with wearable health monitors to address this critical gap. SmartWear employs advanced, lightweight AI algorithms deployed directly on wearable devices, enabling real-time data analysis and personalized health recommendations. By processing data locally, SmartWear minimizes latency and enhances data privacy, thereby addressing the challenges of bandwidth consumption and sensitive information transmission. The primary objectives of this research are to develop efficient Edge AI models tailored for wearable devices, create adaptive health intervention systems, and ensure robust data security through on-device processing and federated learning. The focus is specifically on chronic conditions such as hypertension, diabetes, and chronic obstructive pulmonary disease (COPD), where continuous monitoring can significantly impact patient care. This study promises to advance the field of IoMT by offering a practical, scalable solution for real-time health monitoring and personalized care. The anticipated contributions include the introduction of novel AI techniques optimized for edge computing, the implementation of a user-friendly system that supports proactive health management, and the enhancement of patient outcomes through timely, personalized interventions. SmartWear represents a significant step forward in leveraging Edge AI to revolutionize chronic disease management and personalized healthcare.

Caregivers' occupational balance (OB) can be influenced by multiple factors that may vary depending on the caregiver's age. This study aimed to identify key predictors of occupational balance among informal caregivers from different age groups, with particular emphasis on stress, perceived social support, and caregiver burden. The cross-sectional survey study was conducted with 233 participants who completed the Multidimensional Scale of Perceived Social Support, the Perceived Stress Scale, the Zarit Caregiver Burden Scale, and the Occupational Balance Questionnaire. Between-group differences were analyzed using one-way ANOVA, while multiple linear regression was performed to identify significant predictors of occupational balance. Caregiver burden emerged as the strongest predictor of occupational balance across all age groups (β = 0.61, p < .001 for caregivers of children; β = 0.33, p = .007 for caregivers of adults; β = 0.54, p < .001 for caregivers of older adults). Additionally, stress (β = 0.32, p = .008) was a significant predictor among caregivers of adults, and disability percentage (β = 0.21, p = .021) predicted occupational balance among caregivers of older adults. All analyses used a significance threshold of p < .05. These findings highlight the necessity of assessing caregiver burden, stress, and social support in efforts to promote occupational balance and well-being among informal caregivers.

This study aims to analyze research papers related to Hospital Information Systems (HIS), including Electronic Health Record and Electronic Medical Record (EMR), in South Korea over three decades. The research explores frequently used terms and concepts in HIS research, examines how these reflect key focus areas, and investigates the evolution of research trends. This study employed text preprocessing, text network analysis, and a large language model (LLM)-based topic modeling approach. Network analysis identified the most connected keywords in the research corpus, highlighting their central importance across diverse studies. Through LLM-based topic modeling, seven distinct research topics were identified. Intertopic distance mapping revealed significant conceptual overlap between system performance and digital transformation research, while Security and Privacy Management and Health Information Exchange emerged as more distinct areas. Temporal analysis demonstrated evolution in research focus, beginning with system performance in the 1990s, shifting toward EMR adoption and nursing applications, then emphasizing digitalization and security concerns. Recent trends continue to emphasize security alongside digital integration of hospital information infrastructure. These findings reveal that future research should prioritize security in complex healthcare ecosystems, address the underdeveloped area of documentation standardization, and investigate user experience across diverse clinical settings to understand HIS implementation barriers.