HYBRID BIO-MATHEMATICAL OPTIMIZATION FRAMEWORKS FOR RESOURCE ALLOCATION IN PUBLIC HEALTH SYSTEMS

In the face of escalating healthcare challenges, Ghana’s public health system has increasingly turned to hybrid bio-mathematical optimization frameworks to enhance resource allocation efficiency. The urgent need for innovative, predictive, and adaptive models was underscored by persistent inefficiencies and resource shortages, especially during epidemics such as COVID-19. This study aimed to evaluate the impact of hybrid optimization approaches-including resource optimization techniques, predictive health analytics, and integrated health systems modeling-on the efficiency of public health resource allocation from 2020 to 2024. Using a quantitative research design based exclusively on secondary data from 112 institutions (representing 68% of targeted facilities), descriptive and inferential statistical analyses were conducted. Major findings revealed that resource optimization techniques exhibited the strongest positive correlation with efficiency (r = 0.842), followed by predictive health analytics (r = 0.817) and integrated health systems modeling (r = 0.796), while institutional and environmental factors also played a significant role (r = 0.766). Regression analysis confirmed that these variables collectively explained 84.7% of the variance in allocation efficiency (R² = 0.847, p < 0.001). The results demonstrated notable improvements such as a 22% reduction in immunization costs, a 55% decrease in emergency drug deployment times, and a 13-point rise in healthcare coverage between 2020 and 2024. The study concludes that hybrid frameworks significantly enhanced healthcare responsiveness, equity, and cost-effectiveness. It recommends broader nationwide adoption, stronger policy integration, and enhanced technological infrastructure to sustain and expand these gains. Future health system strategies should incorporate adaptive models and data-driven governance to meet Ghana’s evolving healthcare demands.