⚕️ What Does Health Management Mean? Building a Framework for Efficiency, Sustainability, and Equity in Healthcare An emerging conceptual framework for health management redefines the role by recogni...

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Ankit Kumar's answer: Clinic software improves workflow in healthcare facilities by automating appointment scheduling, patient records, billing, prescriptions, and reporting in one centralized system....

Background: With the rising prevalence of aging worldwide, there is a necessity for reliable and frequent assessments of older individuals’ health status to manage and potentially prevent age-related complications. Digital health technologies (DHTs), such as wearable devices, provide an opportunity to gather objective, continuous, and unobtrusive measurements, enabling effective health management in everyday life. Objective: We aimed to evaluate the relationship between digital endpoints and frailty, nutritional status, and patient-reported outcomes (PROs) in older individuals, as well as quantified their compliance and comfort with DHTs. Methods: In two cross-sectional studies, namely the Geriatric Anorexia Studies 1 and 2, including three in-clinic visits and 2-3 weeks of at-home monitoring, 94 participants (mean age 72.98 years, SD 6.28 years) were stratified based on their frailty status (n=39, 41%, nonfrail; n=45, 48%, prefrail; and n=10, 11%, frail) and nutritional status (n=70, 74%, with normal nutrition and n=24, 26%, at risk for malnutrition), as assessed in a clinical setting using the Fried Frailty Score and the Simplified Nutritional Appetite Questionnaire, respectively. We remotely monitored older adults using different DHTs. At home, participants were monitored with a wrist accelerometer for physical activity, continuous glucose monitoring (CGM) for glucose concentration, a digital body scale for weight and body composition, and a digital nutritional scale for meal tracking. Compliance with devices was assessed via wear time and correct at-home usage, while comfort was evaluated using questionnaires. The association between digital endpoints and frailty/nutritional status was investigated via linear regression, followed by ANOVA, and the relationship between digital endpoints collected at home and PROs was evaluated via Spearman’s ρ. Weight and body composition were also assessed during in-clinic visits with a research-grade scale, used to validate the at-home digital body scale measurements, via the intraclass correlation coefficient, Pearson’s R, and Bland-Altman plots with mean bias. Results: Physical activity digital endpoints collected at home were significantly different across frailty and nutritional groups and significantly correlated with self-reported appetite, fatigue, and physical function (eg, for mean daily activity in the maximum 60 minutes of activity, ρ=0.28, –0.23, and 0.47, respectively). More than 80% of participants reported that all devices were mostly to very acceptable to wear/use: 95% (88/93) for the body scale, 86% (43/50) for CGM, 80% (40/50) for the nutrition scale, and 91% (84/93) for the wrist accelerometer. Compliance ranged from 60% of monitoring days for the digital nutrition scale to 90% for the wrist accelerometer. Conclusions: Physical activity digital endpoints show clear prognostic significance for frailty and malnutrition in older individuals and reflect self-reported measures. DHTs can be reliably deployed at home for older individuals to measure their physical activity, weight and body composition, glucose concentration, and meal intakes, thus enabling patient-centric, data-centric clinical trials. Clinical Trial: ClinicalTrials.gov NCT04858932; https://clinicaltrials.gov/study/NCT04858932; ClinicalTrials.gov NCT05211973; https://clinicaltrials.gov/study/NCT05211973

Purpose. This study examines the role of psychological capital (PsyCap) and motivation in the adjustment and performance of self-initiated expatriate (SIE) nurses in Kuwait's health system, where 95% ...

EY ShinNihon has been recognized as a Health Management Corporation for 2026, highlighting its dedication to employee health and well-being.

Background: Population aging has emerged as a global concern, older adults’ ability to access health knowledge and manage their well-being impacts their health outcomes. In the artificial intelligence era, generative artificial intelligence (GenAI) videos hold promise for enhancing geriatric health management. However, their potential and the needs of older adults in using GenAI videos for health-related purposes deserve a more in-depth investigation. Objective: This study aims to explore the application potential and multifaceted needs of older adults in using GenAI videos for health information acquisition and management, while providing actionable recommendations for future aging-friendly GenAI video tools. Methods: A qualitative approach was adopted. Twenty older adults (aged ≥60 y) with basic digital literacy were recruited from communities for participation in iterative GenAI video workshops. Semistructured interviews were conducted. Thematic analysis, following Braun and Clarke’s 6-phase reflexive framework, was used to identify key themes from interview transcripts. Results: Our results have identified a 3-layer hierarchical structure of needs when older adults interact with GenAI videos. The first layer is technical accessibility, which suggests the direct barriers preventing them from operating GenAI tools independently. The second layer is age-friendly design, which depicts the needs of age-friendly design adaptations, which can help older adults to better use GenAI features. The final layer is integration with professional resources, highlighting the need for integrating professional medical information resources with GenAI tools, so that the GenAI videos can be more appropriately used for health management purposes. By triangulating the core needs of older adults, the capabilities of GenAI, and their remaining gaps, we have found that the existing gaps limit the extent to which the health needs of older adults are met, and also inversely restrain the acceptance and use of GenAI videos by older adults. Conclusions: This study explored the potential and gaps of using GenAI videos in older adults’ health management. GenAI videos are prominent for self-management of health, but our triangular model reveals coexisting opportunities and challenges. While the core needs drive the development directions of GenAI video technologies, our study suggests that GenAI health videos can benefit from the improvements in technical capabilities, service innovation, localization, and integration of health resources.

Background: Aging is a pressing global issue, and older adults need to build up their knowledge to manage their health. Insufficient self-efficacy and low acceptance of technology hinder their ability to use emerging technologies for self-management. Objective: This study explored the potential of generative artificial intelligence (GenAI) videos in the health management of older adults. Methods: We developed a video-based GenAI prototype, AIHealthV. This study used a mixed methods approach, enrolling 20 older adult participants (aged 60 to 80 years) in 3 rounds of iterative workshops. Data collection included pre- and postquestionnaires, in-depth interviews, prompt text records, and the generated video content. Qualitative data were analyzed using the 6-stage reflexivity thematic analysis method by Braun and Clarke, and quantitative data were analyzed using the Wilcoxon signed-rank test. Results: The findings revealed that GenAI videos can enhance older adults’ self-efficacy and technology acceptance, reduce cognitive load, and simultaneously meet their health management needs. The multimodal content generated by GenAI makes health information more comprehensible and thus improves the accessibility of health knowledge. Following the workshop, the interaction between older adults and AIHealthV exhibits a trend of exploration, adaptation, and verification. Conclusions: Despite ethical, privacy, and usability concerns, AIHealthV has been proven to be beneficial in improving the health management capabilities of older adults. This paper also provides practical insights for developing artificial intelligence health tools tailored to older adults as GenAI tools continue to prevail.

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An illicit trade gets Death reassigned to a new District