System Dynamics Modeling for Diabetes Treatment and Prevention Planning

Area of Science:

• Health economics
• Population health management
• Computational modeling

Background:

• Rising rates of preventable chronic diseases, like Type 2 Diabetes (T2D), strain Canadian healthcare budgets and individual finances.
• Emerging AI-driven predictive tools and life-prolonging treatments necessitate evaluation of their impact on population health and healthcare expenditures.

Purpose of the Study:

• To analyze the long-term effects of AI predictive tools, novel treatments, and digital behavior change applications on T2D prevalence and healthcare costs using system dynamics modeling.
• To forecast the secondary effects of healthcare policy decisions on chronic disease management and costs.

Main Methods:

• System dynamics modeling was employed to simulate three distinct scenarios over a 50-year timeframe.
• The model assessed the interplay between technological innovations, treatment interventions, and preventive measures on T2D incidence and healthcare spending.

Main Results:

• AI and novel treatments may reduce T2D complications but could paradoxically increase T2D prevalence and overall healthcare expenditures.
• Preventive measures are essential to counteract potential cost increases and manage T2D prevalence when implementing new health technologies.

Conclusions:

• System dynamics models are valuable for forecasting the complex, long-term consequences of healthcare innovations and policy choices.
• Effective management of the evolving chronic disease landscape requires integrated strategies that combine advanced treatments with robust preventive measures.