Data-Driven Prevention: Transforming Public Health Through Integrated Digital Systems

Abstract

Preventive Medicine 2.0 represents a paradigm shift in public health, driven by the integration of digital technologies, big data analytics, and artificial intelligence. This study provides a comprehensive review of the role of integrated digital health data systems in improving preventive strategies and community-level health outcomes. A structured analysis of high-impact literature published from 2020 onwards was conducted, focusing on key domains such as data integration, interoperability, artificial intelligence, and digital health implementation. The findings indicate that while foundational components such as electronic health records and big data systems have achieved significant adoption, critical gaps remain in interoperability and advanced analytics. The results demonstrate that higher levels of data integration are associated with substantial improvements in early detection, risk stratification, and targeted interventions. However, persistent barriers—including data fragmentation, infrastructure limitations, workforce gaps, and health data poverty—continue to limit the full potential of digital health systems. The study also highlights regional disparities, particularly in Latin America, where countries such as Mexico, Colombia, and Ecuador show progress but remain in transitional stages of digital transformation. The findings emphasize that the success of Preventive Medicine 2.0 depends not only on technological advancements but also on governance, equity, and system-level integration. Ultimately, this work supports the need for coordinated strategies to strengthen data-driven public health systems and enhance community-level health outcomes.

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