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Optimizing patient treatment decisions in an era of rapid technological advances: the case of hepatitis C treatment

Author

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  • Shan Liu

    (University of Washington)

  • Margaret L. Brandeau

    (Stanford University)

  • Jeremy D. Goldhaber-Fiebert

    (Stanford University)

Abstract

How long should a patient with a treatable chronic disease wait for more effective treatments before accepting the best available treatment? We develop a framework to guide optimal treatment decisions for a deteriorating chronic disease when treatment technologies are improving over time. We formulate an optimal stopping problem using a discrete-time, finite-horizon Markov decision process. The goal is to maximize a patient’s quality-adjusted life expectancy. We derive structural properties of the model and analytically solve a three-period treatment decision problem. We illustrate the model with the example of treatment for chronic hepatitis C virus (HCV). Chronic HCV affects 3-4 million Americans and has been historically difficult to treat, but increasingly effective treatments have been commercialized in the past few years. We show that the optimal treatment decision is more likely to be to accept currently available treatment—despite expectations for future treatment improvement—for patients who have high-risk history, who are older, or who have more comorbidities. Insights from this study can guide HCV treatment decisions for individual patients. More broadly, our model can guide treatment decisions for curable chronic diseases by finding the optimal treatment policy for individual patients in a heterogeneous population.

Suggested Citation

  • Shan Liu & Margaret L. Brandeau & Jeremy D. Goldhaber-Fiebert, 2017. "Optimizing patient treatment decisions in an era of rapid technological advances: the case of hepatitis C treatment," Health Care Management Science, Springer, vol. 20(1), pages 16-32, March.
    • Handle: RePEc:kap:hcarem:v:20:y:2017:i:1:d:10.1007_s10729-015-9330-6
    DOI: 10.1007/s10729-015-9330-6
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    Cited by:

    1. Ting-Yu Ho & Shan Liu & Zelda B. Zabinsky, 2019. "A Multi-Fidelity Rollout Algorithm for Dynamic Resource Allocation in Population Disease Management," Health Care Management Science, Springer, vol. 22(4), pages 727-755, December.
    2. Douglas, Conor M.W. & Panagiotoglou, Dimitra & Dragojlovic, Nick & Lynd, Larry, 2021. "Methodology for constructing scenarios for health policy research: The case of coverage decision-making for drugs for rare diseases in Canada," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    3. Wesley J. Marrero & Mariel S. Lavieri & Jeremy B. Sussman, 2021. "Optimal cholesterol treatment plans and genetic testing strategies for cardiovascular diseases," Health Care Management Science, Springer, vol. 24(1), pages 1-25, March.
    4. Roy Lothan & Noa Gutman & Dan Yamin, 2022. "Country versus pharmaceutical company interests for hepatitis C treatment," Health Care Management Science, Springer, vol. 25(4), pages 725-749, December.
    5. Saligrama Agnihothri & Leon Cui & Mohammad Delasay & Balaraman Rajan, 2020. "The value of mHealth for managing chronic conditions," Health Care Management Science, Springer, vol. 23(2), pages 185-202, June.
    6. Bolin, Kristian & Caputo, Michael R., 2021. "Non-life-threatening ailments and rational patience when expected treatment outcomes are continuously improving," Journal of Health Economics, Elsevier, vol. 79(C).

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