Transforming Population Health Management through Digital Health, Artificial Intelligence and Predictive Analytics

Abstract

Population health management is rapidly gaining importance because of several factors like chronic diseases, ageing populations, and rising healthcare costs. Digital health, artificial intelligence (AI), and predictive analytics are helping to shape healthcare systems deliver services, monitor health outcomes, and support disease prevention in recent times. This review summarizes recent evidence on the role of these technologies in population health management. A structured literature search was conducted using PubMed, Scopus, Web of Science and Google Scholar to identify relevant studies published between 2016 and June 2026, along with clinical guidelines and reports from recognized organizations. The findings show that digital health technologies, like electronic health records, telemedicine, mobile health applications, wearable devices, and remote patient monitoring, improve access to healthcare and support continuous patient monitoring. AI assists in disease diagnosis, clinical decision-making, medical image analysis, and population-level health assessment, while predictive analytics identifies individuals and communities who are at risk of disease and supports early intervention and healthcare planning. The review also discusses important challenges, including data privacy, cybersecurity, interoperability, algorithm bias, regulatory oversight, and digital literacy. The successful integration of these technologies depends on reliable digital infrastructure, high-quality health data, ethical governance, and collaboration among healthcare professionals, researchers, policymakers, and technology developers. Digital health, AI, and predictive analytics have the potential to assist in disease prevention, improve healthcare delivery, and support better population health outcomes when implemented responsibly.