Finding the Maximum Safe Treatment Interval for Patients With Chronic Conditions

• 成果类型: Article
• 署名作者: DeRoos, Luke; Lavieri, Mariel; Stein, Joshua
• 署名单位: University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1177/10591478241281077
• 发表日期: 2025
• 页码: 279-297
• 关键词: Markov decision process; Treatment Scheduling; Chronic Disease

摘要

For patients diagnosed with a chronic condition, appropriately timing treatment is critically important. Many conditions, such as age-related macular degeneration (AMD), have a maximum safe treatment interval (MSTI) in which treatment is required to prevent disease progression. We introduce a Markov decision process (MDP) with an ordinal action space as a way to quickly and safely identify the MSTI for an individual patient. The MDP's ordinal action space allows users to update the expected outcomes of multiple actions with only a single decision. We solve the MDP and show how the expected outcomes of each action can be presented to clinicians as a menu of treatment options. We describe conditions under which the MDP is guaranteed to find the MSTI under uncertainty. We illustrate the model's effectiveness through an application to AMD, and show that following the MDP can reduce patient exposure to symptoms by 38% and find the optimal interval 7% faster when compared to current clinical protocols.