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

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  • Pooyan Kazemian
  • Jonathan E. Helm
  • Mariel S. Lavieri
  • Joshua D. Stein
  • Mark P. Van Oyen

Abstract

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.

Suggested Citation

  • Pooyan Kazemian & Jonathan E. Helm & Mariel S. Lavieri & Joshua D. Stein & Mark P. Van Oyen, 2019. "Dynamic Monitoring and Control of Irreversible Chronic Diseases with Application to Glaucoma," Production and Operations Management, Production and Operations Management Society, vol. 28(5), pages 1082-1107, May.
  • Handle: RePEc:bla:popmgt:v:28:y:2019:i:5:p:1082-1107
    DOI: 10.1111/poms.12975
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    Cited by:

    1. Zhao, Heng & Liu, Zixian & Li, Mei & Liang, Lijun, 2022. "Optimal monitoring policies for chronic diseases under healthcare warranty," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    2. Naumzik, Christof & Feuerriegel, Stefan & Nielsen, Anne Molgaard, 2023. "Data-driven dynamic treatment planning for chronic diseases," European Journal of Operational Research, Elsevier, vol. 305(2), pages 853-867.
    3. Isaac A. Jones & Mark P. Van Oyen & Mariel S. Lavieri & Christopher A. Andrews & Joshua D. Stein, 2021. "Predicting rapid progression phases in glaucoma using a soft voting ensemble classifier exploiting Kalman filtering," Health Care Management Science, Springer, vol. 24(4), pages 686-701, December.
    4. Gong, Jue & Liu, Shan, 2023. "Partially observable collaborative model for optimizing personalized treatment selection," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1409-1419.
    5. Wei Chen & Yixin Lu & Liangfei Qiu & Subodha Kumar, 2021. "Designing Personalized Treatment Plans for Breast Cancer," Information Systems Research, INFORMS, vol. 32(3), pages 932-949, September.

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