Optimal follow-up policies for monitoring chronic diseases based on virtual age

Abstract

Follow-up policies following treatment are indispensable and effective in reducing the number of complications of chronic diseases, and hence the cost of treating complications, but bring additional follow-up cost inevitably. This paper introduces the virtual age method to measure the effect of follow-up on the patient’s risk of developing a complication, and further proposes a mixed integer nonlinear programming model to develop the optimal periodic follow-up policies from a cost perspective. By means of the proposed model, the optimal timing and type of follow-up checkups for heterogeneous patients can be derived, achieving a tradeoff between costs of treating complications and follow-up. A case study of pediatric type 1 diabetes mellitus patients is presented to illustrate the applicability of the proposed method and analyse the impacts of significant input parameters on the optimal model solutions. The findings form the basis to design flexible and effective follow-up policies for monitoring patients with chronic diseases.