Utilization of AI-Based Predictive Analytics in Public Health Planning

Abstract

The public health planning faces on increasing challenges related to disease burden, limited resources, and the need of more proactive decision-making. In this context, artificial intelligence–based on predictive analytics offers potential support for evidence-based public health planning through data utilization and health trend forecasting. The study aims to analyze the utilization of artificial intelligence–based predictive analytics in public health planning and its role in support of planning quality. A quantitative descriptive-analytical design was employed by structured questionnaires data collection involving stakeholders engaged in public health planning. Data were analyzed by using descriptive statistics and descriptive relational analysis to map patterns of predictive analytics utilization and planning quality. The findings indicate that predictive analytics utilization is a moderate to high level and is positively associated with public health planning quality, particularly in data-driven decision-making and anticipatory capacity for future health needs. However, predictive analytics is more frequently applied to forecasting purposes than for direct resource allocation and operational decision-making. The study concludes that artificial intelligence–based predictive analytics serves as an important decision-support instrument in evidence-based public health planning, while further institutional capacity building and governance improvements data are required to maximize its impact.

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