Stunting remains one of the major public health issues at the national level in Indonesia. As an archipelagic country, Indonesia faces unique challenges in tackling stunting, particularly in regions with limited access to healthcare services. Advances in information technology offer new opportunities to support stunting prevention efforts, including through the utilization of health data. Such data can be used to detect stunting risks early and to monitor children's nutritional status more effectively. The use of health data applications or systems by Posyandu cadres is influenced by various factors, including availability of time, cost, level of trust, and perceptions of ease of use and usefulness. This study aims to identify the determinants of health data utilization by Posyandu cadres for toddlers as a stunting prevention effort in Geneng Subdistrict, Ngawi Regency. This research is an analytical quantitative study with a cross-sectional approach. A sample of 80 Posyandu cadres for toddlers in Geneng Subdistrict was selected using purposive sampling. Data were collected through questionnaires and analyzed using univariate, bivariate, and multivariate logistic regression tests to identify the factors influencing the use of health data in stunting prevention. The results show that the significant determinants include the age of the Posyandu cadre, their education level, and the amount of time they dedicate to Posyandu activities. The determinants of age, education level, and time significantly influence the utilization of health data and thereby affect the optimization of stunting prevention. Strengthening cadre capacity in these aspects is necessary to support more effective stunting prevention.

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.