Chronic conditions monitoring often relies on one-size-fits-all routine monitoring guideline. Without considering heterogeneity in patients’ disease progression, routine monitoring guideline may lead to inadequate monitoring on sick individuals and unnecessary monitoring on healthy individuals. Prognostic-based monitoring that stratifies the individual’s disease progression risk into different levels and adaptively allocates monitoring resource to high-risk individuals has the potential to improve patient health outcome and cost-effectiveness of the monitoring service. However, challenges include how to best apply prognostic models to inform the design of monitoring strategies and identify the cost-effective strategies. To address these challenges, we develop a decision support framework that integrates individual prognostics, monitoring strategy design and cost-effectiveness analysis. We apply the proposed framework to simulate the adaptive monitoring of a depression treatment population from electronic health record data. Several prediction algorithms with increasing complexity, including natural history matching, logistic regression, rule-based method and Markov-based collaborative model, are designed to monitor the high-risk individuals for severe depression over time. We find six cost-effective monitoring strategies and demonstrate that two routine monitoring strategies are dominated by the prognostic-based monitoring strategies. Methods from this research show promise to implement prognostic-based monitoring of chronic conditions in clinical practice.