Dynamic Monitoring and Control of Irreversible Chronic Diseases with Application to Glaucoma

• 成果类型: Article
• 署名作者: Kazemian, Pooyan; Helm, Jonathan E.; Lavieri, Mariel S.; Stein, Joshua D.; Van Oyen, Mark P.
• 署名单位: Harvard University; Harvard University Medical Affiliates; Massachusetts General Hospital; Harvard University; Harvard Medical School; Indiana University System; IU Kelley School of Business; Indiana University Bloomington; 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: 10.1111/poms.12975
• 发表日期: 2019
• 页码: 1082-1107
• 关键词: chronic disease monitoring and treatment; personalized care; medical decision making; glaucoma; Kalman filter

摘要

To manage chronic disease patients effectively, clinicians must know (i) how to monitor each patient (i.e., when to schedule the next visit and which tests to take), and (ii) how to control the disease (i.e., what levels of controllable risk factors will sufficiently slow progression). Our research addresses these questions simultaneously and provides the optimal solution to a novel linear quadratic Gaussian state space model. For the objective of minimizing the relative change in state over time (i.e., disease progression), which is necessary for managing irreversible chronic diseases while also considering the cost of tests and treatment, we show that the classical two-way separation of estimation and control holds. This makes a previously intractable problem solvable by decomposition into two separate, tractable problems while maintaining optimality. The resulting optimization is applied to the management of glaucoma. Based on data from two large randomized clinical trials, we validate our model and demonstrate how our decision support tool can provide actionable insights to the clinician caring for a patient with glaucoma. This methodology can be applied to a broad range of irreversible chronic diseases to devise patient-specific monitoring and treatment plans optimally.