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All Journal Jurnal Kesehatan Reproduksi At-Tasyrih: Jurnal Pendidikan dan Hukum Islam West Science Interdisciplinary Studies West Science Interdisciplinary Studies Journal of Multidisciplinary Sustainability Asean Journal of World Future Medicine, Health and Nursing Journal of Social Science Utilizing Technology
Benny Novico Zani
Sekolah Tinggi Ilmu Kesehatan Raflesia
Humanities Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Nursing Public Health Social Sciences Other

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E-Health and Digital Transformation in Increasing Accessibility of Health Services loso judijanto; Hendra Nusa Putra; Benny Novico Zani; Dadang Muhammad Hasyim; Muntasir Muntasir
Journal of World Future Medicine, Health and Nursing Vol. 2 No. 1 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v2i1.720

Abstract

In many countries, accessibility of healthcare remains a major challenge, especially in remote rural and urban areas. This research is relevant because of the push towards the application of technology in the healthcare sector to improve healthcare accessibility worldwide. The objectives of this study are to evaluate the role of e-Health in improving healthcare accessibility, explore the impact of digital transformation in improving the quality and coverage of healthcare services, and to draw conclusions about the implications of e-Health implementation and digital transformation in the context of improving healthcare accessibility. This research method uses a literature analysis approach to collect and analyze data from various sources of information related to e-Health and digital transformation in health services. The results of this study show that the implementation of e-Health and digital transformation has brought significant impact in improving the accessibility of health services, especially through the utilization of telemedicine, electronic medical records, and health applications. This has enabled easier access for individuals to obtain medical consultations and health information, especially for those living in remote areas. The conclusion of this study shows that e-Health and digital transformation have great potential in improving healthcare accessibility. By continuing to develop and integrate technology in the health sector, we can achieve the greater goal of providing more equitable and affordable healthcare to the global community. These steps can help reduce disparities in access to healthcare and improve people's overall quality of life.
Artificial Intelligence in Precision Medicine: Transforming Genetic-Based Diagnostics and Patient Care Fitriah Handayani; Nurul Huda; Benny Novico Zani; Muntasir Muntasir
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i4.2520

Abstract

Precision medicine aims to tailor healthcare strategies to individual genetic, environmental, and lifestyle factors, enhancing diagnostic accuracy and treatment efficacy. Traditional approaches to genetic-based diagnostics often face challenges such as high complexity, large-scale data interpretation, and time-intensive analyses. Artificial intelligence (AI) offers transformative potential by enabling rapid, data-driven analysis of genomic information, supporting personalized patient care, and improving clinical decision-making. This study investigates the role of AI in enhancing genetic-based diagnostics and patient care within precision medicine. A systematic review and critical analysis were conducted, integrating findings from peer-reviewed research, clinical reports, and AI-based diagnostic applications. The methodology focused on evaluating AI algorithms for genetic variant detection, risk prediction, and therapeutic recommendations, as well as assessing their clinical integration and outcomes. Results indicate that AI significantly improves the speed, accuracy, and interpretability of genomic analyses, facilitating early disease detection, individualized treatment planning, and predictive risk assessment. Challenges include data privacy, algorithmic transparency, and the need for robust validation in diverse populations. The study concludes that AI integration in precision medicine represents a pivotal advancement in genetic diagnostics and patient-centered care, offering scalable solutions for complex healthcare challenges. Ethical, regulatory, and technical considerations are essential to ensure safe, equitable, and effective implementation in clinical practice.
Community-Based Health Promotion Programs: Innovations for Sustainable Preventive Care Rini Ambarwati; Minarti Minarti; Nur Hasanah; Benny Novico Zani
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i5.2800

Abstract

Growing concern over rising rates of preventable diseases and disparities in access to healthcare has intensified the need for innovative community-based health promotion models that support long-term preventive care. Sustainable preventive strategies increasingly rely on community engagement, participatory education, and context-sensitive interventions capable of addressing social, behavioral, and environmental determinants of health. This study aims to examine how innovative approaches within community-based health promotion programs can strengthen preventive practices and enhance community resilience. A mixed-methods design was employed, integrating survey data from program participants, in-depth interviews with community health workers, and field observations conducted across three urban and rural intervention sites. The findings reveal that community-driven innovations such as culturally tailored health education, peer-led initiatives, and locally adapted preventive tools significantly improve health literacy, encourage sustained behavioral change, and expand access to preventive resources. The programs demonstrated enhanced community ownership, stronger intersectoral collaboration, and measurable. The study concludes that community-based health promotion programs, when supported by innovative and participatory frameworks, serve as an effective pathway toward sustainable preventive care.  
Digital Epidemiology: Using Social Media Data and Machine Learning to Forecast Influenza Outbreaks and Inform Public Health Responses Benny Novico Zani; Ravi Raj Pandey; Le Thi Lan Anh
Journal of Social Science Utilizing Technology Vol. 3 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jssut.v3i3.2735

Abstract

Background. Traditional influenza surveillance systems inherently suffer from a critical one-to-two-week reporting lag, severely hindering timely public health interventions and resource allocation. Purpose. This research aims to develop and validate a hybrid digital epidemiology model using unstructured social media data and advanced Machine Learning (ML) to provide accurate, long-range influenza outbreak forecasts. Method. The methodology involved quantitative time-series forecasting, training Long Short-Term Memory (LSTM) and XGBoost models on five years of social media data, and benchmarking against official clinical reports. Results. The optimized LSTM model achieved significantly superior accuracy, recording a Root Mean Square Error (RMSE) of 0.145 for the four-week forecasting horizon, less than half the error of the traditional ARIMA baseline. This high predictive power confirms that social media is a statistically reliable, non-clinical leading indicator. Conclusion. The study establishes a transparent policy translation framework, linking predicted incidence rates (e.g., exceeding 0.20) directly to required operational responses (e.g., hospital surge activation). This model offers a robust, actionable template for transforming public health surveillance from a reactive system into a proactive intelligence platform for epidemic preparedness.
Co-Authors Amir Raza Dadang Muhammad Hasyim Dhaniswara, Erwin Didik Cahyono Dito Anurogo Eri Mardiani Faridah Mohd Said Fitriah Handayani Hendra Nusa Putra Herawati Herawati Intan Rosmita Dewi Le Thi Lan Anh Loso Judijanto Minarti Minarti Muhammad Subhan Iswahyudi Muhammad Syafral Muntasir, Muntasir Nisha Nambiar Nur Hasanah Nurhikmah Paddiyatu Nurul Huda Rahaju Ningtyas Ravi Raj Pandey Rini Ambarwati Roya Zahir Sakati, Sandy Novryanto Siska Jeanete Saununu Siti Sholihat Waqiah Zulaekah Zulaekah