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The global healthcare landscape is undergoing a digital transformation, driven by the integration of Artificial Intelligence (AI), Big Data, and Telemedicine. These technologies have become essential not only in addressing immediate clinical demands but also in shaping long-term system-wide reforms. However, digital health adoption remains uneven across countries, influenced by variations in infrastructure, policy frameworks, and socioeconomic conditions. This study aims to explore how the integration of AI, Big Data, and Telemedicine contributes to healthcare system transformation through a comparative qualitative-descriptive analysis of four countries: the United States, India, Indonesia, and Rwanda. Secondary data were collected from peer-reviewed journals, government reports, and institutional publications from 2019 to 2024. Thematic analysis focused on policy direction, infrastructure readiness, implementation models, and observed outcomes. Findings reveal that while the U.S. leads with private-sector-driven innovation, India emphasizes national-scale integration, Indonesia navigates digital transformation in a geographically dispersed setting, and Rwanda demonstrates scalable solutions in low-resource environments. Despite different contexts, common challenges include interoperability gaps, ethical data governance, and disparities in digital literacy. The study synthesizes strategic insights and highlights the importance of inclusive, adaptable, and well-regulated digital ecosystems. It concludes that successful digital health implementation depends not only on technology, but also on governance, investment, and equity-focused design.

The digital revolution in global health presents great opportunities and challenges through the integration of artificial intelligence (AI), big data and telemedicine. However, the absence of a transcendent ethical foundation in the development of medical technology risks undermining human values and justice. This study aims to critically examine the direction of digital health development by integrating the ethical and spiritual values of the Qur'an. The method used is a critical qualitative approach based on library research, with normative analysis of verses related to health principles, information ethics, and maqāṣid al-syarī'ah. The results of the study show that in QS. Al-Isrā': 70 emphasizes human dignity as the basis for medical data protection; likewise in QS. Al-Syu'arā': 80 emphasizes that healing is divine and should not be separated from spiritual values; while in QS. Al-Baqarah: 286 provides guidance on the limits of responsibility and human capabilities in the utilization of technology. The critical discussion underlines the importance of building Qur'anic-based digital ethics that emphasize justice ('adālah), trustworthiness, and compassion (raḥmah) in designing equitable and inclusive artificial intelligence (AI) systems and remote health services. It is hoped that this study will provide recommendations for the development of Qur'anic Digital Ethics as a future normative framework for global digital health innovation.

The digital revolution in global health presents great opportunities and challenges through the integration of artificial intelligence (AI), big data and telemedicine. However, the absence of a transcendent ethical foundation in the development of medical technology risks undermining human values and justice. This study aims to critically examine the direction of digital health development by integrating the ethical and spiritual values of the Qur'an. The method used is a critical qualitative approach based on library research, with normative analysis of verses related to health principles, information ethics, and maqāṣid al-syarī'ah. The results of the study show that in QS. Al-Isrā': 70 emphasizes human dignity as the basis for medical data protection; likewise in QS. Al-Syu'arā': 80 emphasizes that healing is divine and should not be separated from spiritual values; while in QS. Al-Baqarah: 286 provides guidance on the limits of responsibility and human capabilities in the utilization of technology. The critical discussion underlines the importance of building Qur'anic-based digital ethics that emphasize justice ('adālah), trustworthiness, and compassion (raḥmah) in designing equitable and inclusive artificial intelligence (AI) systems and remote health services. It is hoped that this study will provide recommendations for the development of Qur'anic Digital Ethics as a future normative framework for global digital health innovation.

The global healthcare landscape is undergoing a digital transformation, driven by the integration of Artificial Intelligence (AI), Big Data, and Telemedicine. These technologies have become essential not only in addressing immediate clinical demands but also in shaping long-term system-wide reforms. However, digital health adoption remains uneven across countries, influenced by variations in infrastructure, policy frameworks, and socioeconomic conditions. This study aims to explore how the integration of AI, Big Data, and Telemedicine contributes to healthcare system transformation through a comparative qualitative-descriptive analysis of four countries: the United States, India, Indonesia, and Rwanda. Secondary data were collected from peer-reviewed journals, government reports, and institutional publications from 2019 to 2024. Thematic analysis focused on policy direction, infrastructure readiness, implementation models, and observed outcomes. Findings reveal that while the U.S. leads with private-sector-driven innovation, India emphasizes national-scale integration, Indonesia navigates digital transformation in a geographically dispersed setting, and Rwanda demonstrates scalable solutions in low-resource environments. Despite different contexts, common challenges include interoperability gaps, ethical data governance, and disparities in digital literacy. The study synthesizes strategic insights and highlights the importance of inclusive, adaptable, and well-regulated digital ecosystems. It concludes that successful digital health implementation depends not only on technology, but also on governance, investment, and equity-focused design.

Attendance management is an essential component in educational institutions, companies, and organizations to monitor the presence and punctuality of participants. Traditional attendance systems, such as manual signatures or identification cards, are prone to various issues including human error, time inefficiency, and identity fraud. To address these challenges, this study aims to develop a smart attendance system using facial recognition technology based on Python and the OpenCV library. The system is designed to automatically detect and recognize faces in real time using a webcam or camera module. It employs computer vision techniques to capture facial images, extract unique features, and match them against a stored database of registered participants. Once the face is verified, the system records the attendance along with a timestamp, ensuring data accuracy and security. The development process involved several stages, including image acquisition, preprocessing, feature extraction, and classification. OpenCV was utilized for image processing tasks, while Python provided the programming framework to integrate all components. To enhance recognition accuracy, the system applied techniques such as histogram equalization for lighting normalization and Haar Cascade classifiers for initial face detection. An experimental evaluation was conducted under various conditions, including different lighting environments and facial orientations. The results demonstrated that the system achieved an accuracy rate of 96% under normal lighting conditions, with only a small decrease in performance under dim or uneven lighting. These findings indicate that the system is reliable for practical applications, especially in controlled environments. Conclusion: The Python-based facial recognition attendance system offers a more efficient, secure, and accurate alternative to conventional attendance methods. Future improvements may include the integration of deep learning models to enhance recognition robustness in diverse real-world scenarios.

Indonesia's healthcare system continues to face significant challenges in delivering equitable services across diverse and remote regions. The digital transformation of healthcare—through the integration of Artificial Intelligence (AI), big data, and telemedicine—offers promising solutions to overcome disparities in access, infrastructure, and service delivery. This study aims to comprehensively analyze global and national research trends related to the digital transformation of healthcare using a Systematic Literature Review (SLR) approach, supported by bibliometric analysis through the VOSviewer software. Following the PRISMA protocol, a total of 30 relevant articles published between 2020 and 2025 were identified and analyzed. The network and overlay visualizations generated reveal four major thematic clusters: digital transformation and service quality, big data and pandemic response, AI and data privacy, and community engagement in digital health services. Overlay visualization also shows a clear shift in research focus—from early pandemic responses toward system optimization, ethical governance, and technological inclusivity in recent years. The findings highlight that digital healthcare transformation has increasingly evolved from emergency responses to COVID-19 into a strategic framework for long-term system improvement. Moreover, AI and big data have played pivotal roles in enhancing diagnostics, predicting outbreaks, and improving resource allocation. However, concerns related to privacy, digital literacy, and unequal technological access remain prominent. The study concludes that the integration of AI, big data, and telemedicine not only enhances healthcare efficiency but also requires strong regulatory frameworks, infrastructure readiness, and public engagement. Future research should incorporate co-citation and cross-country comparative analyses to enrich the understanding of digital health transformation in a global context, especially in low- and middle-income countries like Indonesia.

The development of critical thinking skills is essential for preparing future generations to effectively navigate the challenges posed by rapid technological and scientific advancements. This study examines the effectiveness of STEM-based learning (Science, Technology, Engineering, and Mathematics) in enhancing critical thinking skills among elementary school students in Indonesia. Using a quasi-experimental method with a pre-test and post-test control group design, this research involved 120 students from three elementary schools in East Java. The experimental group was taught using an integrated STEM approach, while the control group continued with a traditional curriculum. The primary objective was to assess how STEM-based learning impacts students' critical thinking, particularly in areas such as problem-solving, reasoning, and analysis. The results revealed a significant improvement in the critical thinking abilities of students in the STEM group compared to the control group. Students in the experimental group demonstrated enhanced skills in problem-solving and reasoning, with notable improvements in their ability to analyze and evaluate information. Additionally, the STEM group exhibited higher levels of engagement, curiosity, and motivation in their learning, along with a greater ability to apply interdisciplinary knowledge to real-life contexts. These findings suggest that STEM-based learning can play a crucial role in developing critical thinking and other essential skills required in the 21st century. This research underscores the potential benefits of incorporating STEM education into elementary school curricula to foster critical thinking and prepare students for future challenges. The study recommends that policymakers and educators focus on providing the necessary training, resources, and support to effectively implement STEM-based learning in primary education. By doing so, we can ensure that students develop the skills needed to succeed in a rapidly evolving world.  

Picky eating is a prevalent issue among preschool children, often leading to nutritional deficiencies, disrupted growth, and increased parental stress. This systematic literature review aims to explore the management of picky eating through complementary and alternative therapies (CAT) and compares these findings with contemporary interventions. Picky eating behaviors can significantly affect children’s eating patterns, leading to challenges in maintaining a balanced diet. Although traditional medical interventions focus on behavioral modification and nutritional counseling, many parents are seeking alternative approaches to manage these behaviors in a more holistic and natural manner. This review encompasses studies published between 2015 and 2025, sourced from PubMed, Scopus, and Google Scholar, using keywords such as "Alternative Therapy," "Picky Eating," and "Preschool Children." The review population consists of 20 articles, and the sample includes 5 selected studies that meet the criteria for evaluating the effectiveness of complementary and alternative therapies in managing picky eating behaviors. Data analysis utilized thematic analysis, with the findings analyzed thematically to draw conclusions regarding the efficacy of CAT. The results indicated that mindfulness practices, dietary practices, Tuina massage as a traditional therapy, taste exposure, sensory learning, and nutrition education were all effective in reducing picky eating behavior among children aged 1 to 5 years. These approaches not only helped to improve children’s acceptance of a wider variety of foods but also contributed to the reduction of stress for both children and parents. The findings suggest that integrating these alternative therapies into conventional practices can provide a comprehensive and effective strategy to address picky eating in preschool children. Future research is needed to further explore the long-term impacts of these therapies and to identify the most suitable combinations for different individual needs.

Anxiety in pregnant women is often characterized by intense fear or panic, and if left unaddressed, it can lead to various complications such as prolonged labor, maternal fatigue, and even labor stall. This study aimed to determine the relationship between family support and maternal preparedness with the anxiety levels of pregnant women prior to childbirth. The research utilized a quantitative design with a cross-sectional approach, conducted in the working area of the Bongo Nol Health Center. The study population consisted of pregnant women in their second and third trimesters (TM II and TM III). A total of 40 pregnant women participated in the study, selected through simple random sampling. Data were collected using questionnaires designed to assess family support, maternal preparedness, and anxiety levels. The results revealed that 20% of the pregnant women who received inadequate family support experienced severe anxiety, with a statistically significant p-value of 0.001. Similarly, 15% of mothers with lower levels of preparedness reported severe anxiety, also with a p-value of 0.001. These findings suggest a significant relationship between both family support and maternal preparedness with anxiety levels in pregnant women prior to delivery. The study concludes that increased family support and maternal readiness can help reduce anxiety levels in pregnant women, particularly in the lead-up to childbirth. Healthcare providers should prioritize interventions to strengthen family involvement and support maternal readiness, especially for women at risk of high anxiety, to improve birth outcomes and maternal well-being. Future research should explore additional factors contributing to anxiety and the effectiveness of targeted interventions for high-risk pregnancies

Indonesia's healthcare system continues to face significant challenges in delivering equitable services across diverse and remote regions. The digital transformation of healthcare—through the integration of Artificial Intelligence (AI), big data, and telemedicine—offers promising solutions to overcome disparities in access, infrastructure, and service delivery. This study aims to comprehensively analyze global and national research trends related to the digital transformation of healthcare using a Systematic Literature Review (SLR) approach, supported by bibliometric analysis through the VOSviewer software. Following the PRISMA protocol, a total of 30 relevant articles published between 2020 and 2025 were identified and analyzed. The network and overlay visualizations generated reveal four major thematic clusters: digital transformation and service quality, big data and pandemic response, AI and data privacy, and community engagement in digital health services. Overlay visualization also shows a clear shift in research focus—from early pandemic responses toward system optimization, ethical governance, and technological inclusivity in recent years. The study concludes that the integration of AI, big data, and telemedicine not only enhances healthcare efficiency but also requires strong regulatory frameworks, infrastructure readiness, and public engagement. Future research should incorporate co-citation and cross-country comparative analyses to enrich the understanding of digital health transformation in a global context.

Indonesia's healthcare system continues to face significant challenges in delivering equitable services across diverse and remote regions. The digital transformation of healthcare—through the integration of Artificial Intelligence (AI), big data, and telemedicine—offers promising solutions to overcome disparities in access, infrastructure, and service delivery. This study aims to comprehensively analyze global and national research trends related to the digital transformation of healthcare using a Systematic Literature Review (SLR) approach, supported by bibliometric analysis through the VOSviewer software. Following the PRISMA protocol, a total of 30 relevant articles published between 2020 and 2025 were identified and analyzed. The network and overlay visualizations generated reveal four major thematic clusters: digital transformation and service quality, big data and pandemic response, AI and data privacy, and community engagement in digital health services. Overlay visualization also shows a clear shift in research focus—from early pandemic responses toward system optimization, ethical governance, and technological inclusivity in recent years. The findings highlight that digital healthcare transformation has increasingly evolved from emergency responses to COVID-19 into a strategic framework for long-term system improvement. Moreover, AI and big data have played pivotal roles in enhancing diagnostics, predicting outbreaks, and improving resource allocation. However, concerns related to privacy, digital literacy, and unequal technological access remain prominent. The study concludes that the integration of AI, big data, and telemedicine not only enhances healthcare efficiency but also requires strong regulatory frameworks, infrastructure readiness, and public engagement. Future research should incorporate co-citation and cross-country comparative analyses to enrich the understanding of digital health transformation in a global context, especially in low- and middle-income countries like Indonesia.

Attendance management is an essential component in educational institutions, companies, and organizations to monitor the presence and punctuality of participants. Traditional attendance systems, such as manual signatures or identification cards, are prone to various issues including human error, time inefficiency, and identity fraud. To address these challenges, this study aims to develop a smart attendance system using facial recognition technology based on Python and the OpenCV library. The system is designed to automatically detect and recognize faces in real time using a webcam or camera module. It employs computer vision techniques to capture facial images, extract unique features, and match them against a stored database of registered participants. Once the face is verified, the system records the attendance along with a timestamp, ensuring data accuracy and security. The development process involved several stages, including image acquisition, preprocessing, feature extraction, and classification. OpenCV was utilized for image processing tasks, while Python provided the programming framework to integrate all components. To enhance recognition accuracy, the system applied techniques such as histogram equalization for lighting normalization and Haar Cascade classifiers for initial face detection. An experimental evaluation was conducted under various conditions, including different lighting environments and facial orientations. The results demonstrated that the system achieved an accuracy rate of 96% under normal lighting conditions, with only a small decrease in performance under dim or uneven lighting. These findings indicate that the system is reliable for practical applications, especially in controlled environments. Conclusion: The Python-based facial recognition attendance system offers a more efficient, secure, and accurate alternative to conventional attendance methods. Future improvements may include the integration of deep learning models to enhance recognition robustness in diverse real-world scenarios.

Chronic and degenerative diseases have a profound effect on patients' overall well-being, often accompanied by psychological distress such as anxiety and chronic stress. While pharmacological treatments are commonly used, they may have limitations, including accessibility issues and side effects, which have prompted interest in non-pharmacological interventions. This scoping review aims to systematically map and synthesize the empirical evidence on the physiological and psychological effects of the 4-7-8 breathing technique, a structured slow-breathing method that involves inhaling for 4 seconds, holding the breath for 7 seconds, and exhaling for 8 seconds. The review adhered to PRISMA-ScR guidelines and included 15 studies published between 2013 and 2024. These studies examined diverse populations and employed various methodological approaches. The findings were categorized into five major themes: (1) the 4-7-8 technique's effectiveness in reducing stress and anxiety, (2) improvements in cardiovascular markers such as heart rate variability and blood pressure, (3) its adaptability in both clinical and community-based multimodal interventions, (4) its preventive benefits for healthy individuals, and (5) its impact on parasympathetic activity via vagal pathways, enhancing autonomic regulation and emotional stability. The technique is supported by both theoretical and empirical evidence, positioning it as an accessible, low-cost psychoregulatory intervention. The results suggest that the 4-7-8 breathing technique could play a key role in holistic nursing care, health education, and public health promotion strategies, offering a simple yet effective approach to managing stress, improving mental health, and enhancing cardiovascular health. Future studies could explore long-term benefits and its integration into more diverse health interventions.

Low birth weight (LBW) is a significant public health problem, with varying prevalence across countries. According to data from the World Health Organization (WHO), approximately 15% of all births worldwide are LBW babies. LBW can be caused by various factors, including maternal health conditions, nutritional status, and environmental factors. One factor that is often overlooked is maternal age during pregnancy. This study aims to explore the relationship between maternal age and the incidence of LBW. Research This study used an observational design with a cross-sectional approach. The sample consisted of 200 pregnant women who gave birth at the Hospital in 2024. Data were collected through medical record data tracing, which included maternal age, infant birth weight, and other risk factors. Statistical analysis was performed using the chi-square test to determine the relationship between maternal age variables and LBW. Results: The results of the analysis showed that 32% of the total babies born had LBW. From the age group under 20 years, the prevalence of LBW reached 15%, while in the age group over 35 years it reached 9%. After conducting the Spearman test in the Halsil test, the significant relationship between maternal age and the incidence of LBW was p (Sig) 0.02 (<0.05). Public health programs should focus more on education and intervention for pregnant women, especially those in their teens and the elderly. This is important to reduce the number of LBW and improve maternal and infant health. In the future, further research is needed to explore other factors that may affect LBW.

Stunting is a serious nutritional problem in Indonesia, affecting the growth and development of children under five years of age. Non-ideal pregnancy spacing is one of the factors that can contribute to stunting, as closely spaced pregnancies can lead to inadequate maternal nutrition, which affects the child’s development. This study aims to analyze the relationship between pregnancy spacing and the incidence of stunting in children under five. The research design employed is observational analytic with a case-control approach. Data collection was conducted from February to October 2024, focusing on children with stunting and children with normal growth as control cases. The case-control study involved a total sample size of 60 children, with 30 stunted children as cases and 30 non-stunted children as controls. The control cases were selected using simple random sampling to ensure unbiased representation. The primary analysis technique used was the chi-square test to examine the relationship between pregnancy spacing and the incidence of stunting. The results indicated that children born with a pregnancy spacing of less than 24 months had a 1.5 times higher risk of experiencing stunting compared to those born with a pregnancy spacing of more than 24 months. Statistical analysis using the chi-square test yielded a significant p-value of 0.002 (< 0.05), which confirms a strong association between short pregnancy spacing and stunting. This study concludes that insufficient pregnancy spacing is a significant factor contributing to the incidence of stunting in children under five. Therefore, it is recommended that public health campaigns and education programs on family planning and reproductive health be strengthened to reduce the risk of stunting in Indonesia. Such initiatives will help prevent future generations from experiencing the negative effects of inadequate growth and development.

Posyandu is a community-based health program managed by and for the community, aiming to strengthen local health systems and facilitate access to essential health services. The temporary disruption of Posyandu services during the COVID-19 pandemic has had adverse effects on vulnerable populations such as pregnant women and toddlers, highlighting the importance of resilient volunteer performance post-pandemic. This study aims to examine the correlation between post-pandemic competence and the implementation of the five main activities by Posyandu volunteers in Malang, Indonesia. Utilizing a quantitative correlational research design, the study involved a total sample of 123 Posyandu volunteers from two villages in Malang. Data collection was conducted over July to August 2023. The findings indicate a statistically significant but weak positive correlation between volunteer knowledge and motivation, with a p-value of 0.007 and a correlation coefficient of 0.242. This suggests that while increased knowledge is associated with higher motivation levels, the strength of this relationship is limited. Further analysis assessed the relationship between length of service and volunteer motivation, revealing no significant correlation (p = 0.675, r = -0.038). These results imply that the duration of volunteer service does not influence motivational levels among Posyandu volunteers. Given these findings, enhancing volunteers’ knowledge through regular training and refresher programs is essential to maintain motivation and improve service delivery. The study recommends continuous capacity-building efforts to empower Posyandu volunteers, thereby ensuring effective implementation of health activities post-pandemic. Future research should investigate additional factors that may affect volunteer motivation, such as social support, recognition, and workload. Understanding these dimensions can further strengthen Posyandu programs and support the vulnerable community members they serve.

Anaemia among teenagers is a significant public health concern that can adversely affect their quality of life, cognitive function, and academic performance. Nutritional status, commonly assessed using Body Mass Index (BMI), is suspected to influence the risk of developing anaemia. This study aims to analyse the relationship between BMI and the incidence of anaemia among students at Senior High School II Rilamuta. The primary objective is to determine whether there is a statistically significant association between BMI and anaemia occurrence in this population. A quantitative correlational research design was employed, involving a purposive sample of 30 students. Data collection comprised measuring each student’s BMI and examining haemoglobin levels to assess anaemia status. The BMI was calculated based on height and weight measurements, while haemoglobin concentrations were measured using standard clinical laboratory techniques. Statistical analysis was performed using the Pearson correlation test to evaluate the strength and direction of the relationship between BMI and anaemia incidence.The results revealed a significant positive correlation between BMI and anaemia, with a Pearson correlation coefficient of 0.746 and a p-value of 0.000, indicating strong statistical significance. This finding suggests that students with higher BMI tend to have a lower risk of anaemia, highlighting the protective role of adequate nutritional status. The study confirms that BMI is a relevant factor in predicting anaemia risk among teenagers. Based on these findings, the study recommends promoting healthy nutritional habits and improving nutritional status as crucial strategies to prevent anaemia in school-aged adolescents. School health programs should emphasize balanced diets and nutritional education to address this issue effectively. Future research with larger sample sizes and longitudinal designs is encouraged to further explore causal relationships and additional factors influencing anaemia in this population.

Picky eating is a prevalent issue among preschool children, often leading to nutritional deficiencies, disrupted growth, and increased parental stress. This systematic literature review aims to explore the management of picky eating through complementary and alternative therapies (CAT) and compares these findings with contemporary interventions. Picky eating behaviors can significantly affect children’s eating patterns, leading to challenges in maintaining a balanced diet. Although traditional medical interventions focus on behavioral modification and nutritional counseling, many parents are seeking alternative approaches to manage these behaviors in a more holistic and natural manner. This review encompasses studies published between 2015 and 2025, sourced from PubMed, Scopus, and Google Scholar, using keywords such as "Alternative Therapy," "Picky Eating," and "Preschool Children." The review population consists of 20 articles, and the sample includes 5 selected studies that meet the criteria for evaluating the effectiveness of complementary and alternative therapies in managing picky eating behaviors. Data analysis utilized thematic analysis, with the findings analyzed thematically to draw conclusions regarding the efficacy of CAT. The results indicated that mindfulness practices, dietary practices, Tuina massage as a traditional therapy, taste exposure, sensory learning, and nutrition education were all effective in reducing picky eating behavior among children aged 1 to 5 years. These approaches not only helped to improve children’s acceptance of a wider variety of foods but also contributed to the reduction of stress for both children and parents. The findings suggest that integrating these alternative therapies into conventional practices can provide a comprehensive and effective strategy to address picky eating in preschool children. Future research is needed to further explore the long-term impacts of these therapies and to identify the most suitable combinations for different individual needs.

Anxiety in pregnant women is often characterized by intense fear or panic, and if left unaddressed, it can lead to various complications such as prolonged labor, maternal fatigue, and even labor stall. This study aimed to determine the relationship between family support and maternal preparedness with the anxiety levels of pregnant women prior to childbirth. The research utilized a quantitative design with a cross-sectional approach, conducted in the working area of the Bongo Nol Health Center. The study population consisted of pregnant women in their second and third trimesters (TM II and TM III). A total of 40 pregnant women participated in the study, selected through simple random sampling. Data were collected using questionnaires designed to assess family support, maternal preparedness, and anxiety levels. The results revealed that 20% of the pregnant women who received inadequate family support experienced severe anxiety, with a statistically significant p-value of 0.001. Similarly, 15% of mothers with lower levels of preparedness reported severe anxiety, also with a p-value of 0.001. These findings suggest a significant relationship between both family support and maternal preparedness with anxiety levels in pregnant women prior to delivery. The study concludes that increased family support and maternal readiness can help reduce anxiety levels in pregnant women, particularly in the lead-up to childbirth. Healthcare providers should prioritize interventions to strengthen family involvement and support maternal readiness, especially for women at risk of high anxiety, to improve birth outcomes and maternal well-being. Future research should explore additional factors contributing to anxiety and the effectiveness of targeted interventions for high-risk pregnancies

Indonesia's healthcare system continues to face significant challenges in delivering equitable services across diverse and remote regions. The digital transformation of healthcare—through the integration of Artificial Intelligence (AI), big data, and telemedicine—offers promising solutions to overcome disparities in access, infrastructure, and service delivery. This study aims to comprehensively analyze global and national research trends related to the digital transformation of healthcare using a Systematic Literature Review (SLR) approach, supported by bibliometric analysis through the VOSviewer software. Following the PRISMA protocol, a total of 30 relevant articles published between 2020 and 2025 were identified and analyzed. The network and overlay visualizations generated reveal four major thematic clusters: digital transformation and service quality, big data and pandemic response, AI and data privacy, and community engagement in digital health services. Overlay visualization also shows a clear shift in research focus—from early pandemic responses toward system optimization, ethical governance, and technological inclusivity in recent years. The study concludes that the integration of AI, big data, and telemedicine not only enhances healthcare efficiency but also requires strong regulatory frameworks, infrastructure readiness, and public engagement. Future research should incorporate co-citation and cross-country comparative analyses to enrich the understanding of digital health transformation in a global context.