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Journal of World Future Medicine, Health and Nursing
Published by Yayasan Adra Karima Hubbi
ISSN : 29880459     EISSN : 29887550     DOI : 10.70177/health
Core Subject :
Journal of World Future Medicine, Health and Nursing is a leading international journal focused on the global exchange of knowledge in medicine, health, and nursing, as well as advancing research and practice across health disciplines. The journal provides a forum for articles reporting on original research, systematic and scholarly reviews focused on health science, clinical practice and education from around the world. Journal of World Future Medicine, Health and Nursing publishes national and international research in an attempt to present a reliable and respectable information source for the researchers. Journal of World Future Medicine, Health and Nursing has been published since 2023, published three times a year January, May and September,. The articles submitted for publication are subjected to double-blind reviewing process. The journal publishes original articles in English.
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Articles 7 Documents
 1 
Search results for , issue "vol. 4 no. 1 (2026)" : 7 Documents clear
SOCIAL ISOLATION AND COGNITIVE DECLINE IN AGING POPULATIONS: AI-POWERED MONITORING SYSTEMS FOR EARLY DETECTION Ava Lee; Thiago Costa; Clara Mendes
Journal of World Future Medicine, Health and Nursing Vol. 4 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v4i1.2373

Abstract

Social isolation is a significant and modifiable risk factor for accelerated cognitive decline and dementia in aging populations. Traditional methods for detecting cognitive changes, such as clinical screenings, are often infrequent and fail to capture the subtle, early behavioral shifts that precede a formal diagnosis. This study aimed to develop and validate an artificial intelligence model designed for the early detection of cognitive decline by passively monitoring behavioral and vocal biomarkers of social isolation in older adults living independently. A 24-month, prospective longitudinal study was conducted with a cohort of 200 community-dwelling adults aged 70 and older. A suite of unobtrusive in-home sensors was used to passively collect data on movement patterns, social communication (frequency and duration of conversations), and computer/phone usage. The AI-powered system identified individuals who would later show clinically significant cognitive decline with an accuracy of 91% and a lead time of approximately 7 months before formal assessment. Decreased frequency of vocal interactions and increased irregularity in daily routines were the most powerful predictors. AI-powered passive monitoring systems are a highly effective and ecologically valid tool for the pre-clinical detection of cognitive decline linked to social isolation.
CARDIOVASCULAR HEALTH SCREENING MANAGEMENT IN PEKANBARU CITY: ANALYSIS OF STRUCTURE, PROCESS, AND BARRIERS Ardenny Ardenny; Idayanti Idayanti; Ibnu Rusdi
Journal of World Future Medicine, Health and Nursing Vol. 4 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v4i1.3281

Abstract

Cardiovascular disease remains a leading cause of global morbidity and mortality. Cardiovascular health screening programs are essential public health strategies for early identification of heart disease risk. This study aimed to analyze the management of cardiovascular health screening in Pekanbaru City using a mixed-methods approach. Quantitative data were collected from 384 respondents aged 40 years and above, while qualitative data were obtained through in-depth interviews with 21 key informants and focus group discussions with 20 community participants. The results showed extremely low screening coverage (1.82% of the target population of 366,154). Analysis using the Donabedian model identified significant deficits in the structure dimension (only 26.7% of trained health workers, 66.7% of primary health centers lacked cholesterol testing equipment, and minimal budget allocation of Rp 15-25 million/year) and process dimension (absence of standardized SOPs, ineffective risk communication). Bivariate analysis revealed that good knowledge (OR=6.47; p<0.001), close proximity to facilities (OR=2.96; p<0.001), positive attitudes (OR=2.61; p<0.001), and family support (OR=2.27; p<0.001) were significantly associated with screening participation. These findings indicate the need for systematic improvements including capacity building of health workers, provision of adequate equipment, standardization of procedures, and strengthening of risk communication systems to optimize cardiovascular health screening programs in Pekanbaru City.
AI-ASSISTED DIAGNOSTICS IN NURSING PRACTICE: IMPACT ON PATIENT ASSESSMENT AND CARE Dhiana Setyorini; Rini Ambarwati; Minarti Minarti; Nur Hasanah
Journal of World Future Medicine, Health and Nursing Vol. 4 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v4i1.3367

Abstract

The rapid integration of artificial intelligence (AI) into healthcare has transformed clinical decision-making processes, including patient assessment and diagnostic support. In nursing practice, accurate and timely assessment is critical to patient safety and quality of care, yet increasing workload and clinical complexity often challenge nurses’ diagnostic performance. This study aims to examine the impact of AI-assisted diagnostic tools on nursing assessment accuracy, efficiency, and overall patient care outcomes. The research employs a mixed-methods design combining quantitative analysis of assessment performance indicators with qualitative exploration of nurses’ experiences in clinical settings where AI diagnostics are implemented. Data were collected from registered nurses across selected hospital units using standardized assessment records, questionnaires, and semi-structured interviews. The results indicate that AI-assisted diagnostics significantly improve assessment accuracy, reduce time to clinical decision-making, enhance early detection of patient deterioration, and increase nurses’ confidence in clinical judgment. Qualitative findings reveal that nurses perceive AI tools as supportive systems that augment, rather than replace, professional expertise when appropriately integrated into workflows. The study concludes that AI-assisted diagnostics represent a valuable advancement in nursing practice by strengthening patient assessment and promoting safer, more consistent care.  
THE FUTURE OF NURSING EDUCATION: THE INTEGRATION OF SIMULATION-BASED LEARNING AND TECHNOLOGY Catur Budi Susilo; Amin Zaki; Rina Farah
Journal of World Future Medicine, Health and Nursing Vol. 4 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v4i1.3486

Abstract

Rapid technological advancement and increasing complexity in healthcare systems require nursing education to evolve beyond traditional instructional approaches. Simulation-based learning supported by digital technologies has emerged as an innovative educational strategy that allows nursing students to develop clinical competence, critical thinking, and decision-making skills in safe and controlled environments. This study aims to examine the integration of simulation-based learning and technological tools in nursing education and evaluate their influence on students’ learning engagement, clinical confidence, and professional competence development. The research employs a mixed-method approach combining quantitative survey analysis with qualitative observations and interviews. Data were collected from nursing students and instructors participating in simulation-based learning sessions using high-fidelity mannequins and virtual simulation platforms. Statistical analysis was conducted to examine learning outcomes, while thematic analysis was used to interpret participants’ experiences. Findings indicate that simulation-based learning significantly improves students’ clinical confidence, engagement, and critical thinking abilities. High-fidelity simulations enhance clinical skill acquisition, while virtual simulations increase accessibility and flexibility in learning. Integration of simulation-based learning and technology represents a transformative strategy for modernizing nursing education and preparing future nurses for technologically advanced healthcare environments.  
EXPLORING THE IMPACT OF TELEMEDICINE ON ACCESS TO HEALTHCARE IN RURAL COMMUNITIES Ton Kiat; Siri Lek; Ravi Dara
Journal of World Future Medicine, Health and Nursing Vol. 4 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v4i1.3534

Abstract

The rapid advancement of telemedicine has revolutionized healthcare delivery, particularly in rural areas where access to healthcare services is often limited. This study explores the impact of telemedicine on improving healthcare access in rural communities, aiming to evaluate how virtual healthcare platforms can overcome geographical barriers and enhance patient outcomes. The research employs a mixed-methods approach, combining qualitative interviews with healthcare professionals and patients in rural settings, along with quantitative surveys to assess healthcare accessibility, satisfaction, and perceived effectiveness. The findings reveal that telemedicine significantly improves access to healthcare by reducing travel time, minimizing wait times for appointments, and offering continuous care, which is crucial for managing chronic conditions. Furthermore, patients expressed high satisfaction with the convenience and affordability of virtual consultations. However, challenges such as limited internet connectivity, technological literacy, and concerns about the quality of care were noted. In conclusion, telemedicine holds great potential in expanding healthcare access in rural communities, but it requires tailored strategies to address infrastructure gaps and ensure equitable access to all individuals. Future research should focus on the long-term outcomes and scalability of telemedicine initiatives in diverse rural settings.
ARTIFICIAL INTELLIGENCE IN EARLY DISEASE DETECTION: REVOLUTIONIZING DIAGNOSTIC PRACTICES IN MEDICINE Safiullah Aziz; Shazia Akhtar; Chen Mei
Journal of World Future Medicine, Health and Nursing Vol. 4 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v4i1.3535

Abstract

The integration of Artificial Intelligence (AI) in medicine has the potential to revolutionize early disease detection, improving diagnostic practices and patient outcomes. Early detection of diseases such as cancer, cardiovascular conditions, and neurological disorders significantly enhances treatment efficacy and survival rates. However, traditional diagnostic methods often suffer from limitations such as diagnostic errors, delayed results, and subjectivity. AI technologies, particularly machine learning (ML) and deep learning (DL), have demonstrated the ability to analyze large datasets, recognize patterns, and predict outcomes with greater accuracy and speed than conventional methods. This study aims to explore the impact of AI on early disease detection, focusing on its applications in diagnostic medicine. The research employs a systematic review of AI-based diagnostic tools and their clinical performance across various diseases. Data from peer-reviewed journals and clinical trials are analyzed to assess the accuracy, efficiency, and clinical implementation of AI technologies. The findings reveal that AI has the potential to significantly improve diagnostic accuracy, reduce diagnostic errors, and expedite disease detection, particularly in resource-limited settings. However, challenges remain regarding data privacy, algorithm transparency, and integration into clinical practice. In conclusion, AI stands poised to transform early disease detection, but careful consideration of ethical and technical challenges is essential for its widespread adoption.
THE ROLE OF MOBILE HEALTH APPLICATIONS IN MANAGING CHRONIC DISEASES: A SYSTEMATIC REVIEW Felipe Souza; Pedro Silva; Sakura Suzuki
Journal of World Future Medicine, Health and Nursing Vol. 4 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v4i1.3536

Abstract

Chronic diseases such as diabetes, hypertension, and cardiovascular diseases are leading causes of morbidity and mortality worldwide. The management of these conditions is often complex and requires ongoing monitoring and adherence to treatment plans. Mobile health (mHealth) applications have emerged as a promising tool for improving chronic disease management, providing patients with real-time monitoring, medication reminders, and lifestyle recommendations. This systematic review aims to evaluate the effectiveness of mHealth applications in managing chronic diseases, examining their impact on patient outcomes, engagement, and healthcare costs. A comprehensive search of electronic databases was conducted, and studies assessing the use of mHealth applications for chronic disease management were included. Data were analyzed for clinical outcomes, user satisfaction, and adherence rates. The findings suggest that mHealth applications can significantly improve patient engagement, enhance self-management behaviors, and lead to better health outcomes in chronic disease management. However, the effectiveness varies across different conditions, with more robust evidence for conditions like diabetes and hypertension. Challenges such as data privacy concerns, technology access issues, and patient adherence remain barriers to widespread adoption. In conclusion, mHealth applications have a valuable role in chronic disease management, but further research is needed to optimize their use and address existing challenges.  

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