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.

治疗后的随访政策对于减少慢性病并发症的数量和治疗并发症的成本是必不可少的，有效的，但不可避免地会带来额外的随访成本。本文介绍了虚拟年龄方法来衡量随访对患者发生并发症风险的影响，并进一步提出了混合整数非线性规划模型，从成本角度制定最优的定期随访政策。通过所提出的模型，可以推导出异质患者的最佳随访时间和类型，实现并发症治疗成本和随访成本之间的权衡。以小儿 1 型糖尿病患者的案例研究来说明所提出方法的适用性，并分析重要输入参数对最优模型解的影响。这些发现为设计灵活有效的慢性病患者监测随访政策奠定了基础。