System Dynamics Modeling for Diabetes Treatment and Prevention Planning.

Abstract

The increasing prevalence of preventable chronic disease in Canada poses significant challenges to both healthcare budgets and individual financial stability. New treatments and predictive technologies are creating an urgent need to evaluate the impact of these innovations on population health and healthcare costs. This paper explores the use of system dynamics modeling to analyze the effects of artificial intelligence (AI)-driven predictive tools, life-prolonging treatments, and digital behavior change applications on T2D prevalence and healthcare expenditures. Our model simulates three scenarios over a 50-year period, revealing that while AI and novel treatments can reduce complications, they may paradoxically increase T2D prevalence and overall costs unless combined with preventive measures. The study demonstrates the utility of system dynamics models in forecasting the secondary effects of policy decisions, providing policymakers with a valuable tool for evaluating trade-offs and optimizing health outcomes. The findings underscore the need for new tools to effectively manage the evolving landscape of chronic disease treatment and prevention.