Interpretable Policies and the Price of Interpretability in Hypertension Treatment Planning

Gian-Gabriel P. Garcia (), Lauren N. Steimle (), Wesley J. Marrero () and Jeremy B. Sussman ()
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Gian-Gabriel P. Garcia: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
Lauren N. Steimle: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
Wesley J. Marrero: Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire 03755
Jeremy B. Sussman: Department of Internal Medicine, Michigan Medicine, University of Michigan, Ann Arbor, Michigan 48109

Manufacturing & Service Operations Management, 2024, vol. 26, issue 1, 80-94

Abstract: Problem definition : Effective hypertension management is critical to reducing the consequences of atherosclerotic cardiovascular disease, a leading cause of death in the United States. Clinical guidelines for hypertension can be enhanced using decision-analytic approaches capable of capturing complexities in treatment planning. However, model-generated recommendations may be uninterpretable/unintuitive, limiting their clinical acceptability. We address this challenge by investigating interpretable treatment plans. Methodology/results : We formulate interpretable treatment plans as Markov decision processes (MDPs) and analyze the problems of optimizing monotone policies, which prohibit decreasing treatment intensity for sicker patients, and class-ordered monotone policies , which generalize monotone policies. We establish that both policies depend on initial state distributions and that optimal monotone policies can be generated tractably for many treatment planning problems. Next, we propose exact formulations for optimizing interpretable policies broadly. Then, we analyze the price of interpretability , proving that the class-ordered monotone policy’s price of interpretability does not exceed the monotone policy’s price of interpretability. Finally, we formulate and evaluate MDPs for hypertension treatment planning using a large nationally representative data set of the U.S. population. We compare the structure and performance of optimal monotone policies and class-ordered monotone policies with optimal MDP-based policies and current clinical guidelines. At the patient level, optimal MDP-based policies may be unintuitive, recommending more aggressive treatment for healthier patients than sicker patients. Conversely, monotone policies and class-ordered monotone policies never deescalate treatment, reflecting clinical intuition. Across 66.5 million patients, optimized monotone policies and class-ordered monotone policies outperform clinical guidelines, saving over 3,246 quality-adjusted life years per 100,000 patients, with both policies paying a low price of interpretability. Sensitivity analysis illustrates that monotone policies and class-ordered monotone policies are robust to various definitions of “interpretability.” Managerial implications : Interpretable policies can be tractably optimized, drastically outperform existing guidelines, and perform near optimally—potentially increasing the acceptability of decision-analytic approaches in practice.

Keywords: Markov decision processes; healthcare applications; medical decision making; interpretability; cardiovascular disease; personalized treatment planning (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:26:y:2024:i:1:p:80-94

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