Optimal Screening for Hepatocellular Carcinoma: A Restless Bandit Model

被引:25
|
作者
Lee, Elliot [1 ]
Lavieri, Marie S. [2 ]
Volk, Michael [3 ]
机构
[1] Ctr Naval Anal, Arlington, VA 22201 USA
[2] Univ Michigan, Dept Ind & Operat Engn, Ann Arbor, MI 48109 USA
[3] Loma Linda Univ, Gastroenterol, Loma Linda, CA 92354 USA
基金
美国国家科学基金会;
关键词
dynamic programming; healthcare management; simulation; medical decision making; multiarmed bandits; OPTIMIZATION; MANAGEMENT; PATIENT;
D O I
10.1287/msom.2017.0697
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
This paper seeks an efficient way to screen a population of patients at risk for hepatocellular carcinoma when (1) each patient's disease evolves stochastically and (2) there are limited screening resources shared by the population. Recent medical discoveries have shown that biological information can be learned at each screening to differentiate patients into varying levels of risk for cancer. We investigate how to exploit this knowledge to choose which patients to screen to maximize early-stage cancer detections while limiting resource usage. We model the problem as a family of restless bandits, with each patient's disease progression evolving as a partially observable Markov decision process. We derive an optimal policy for this problem and discuss managerial insights into what characterizes more effective screening. To provide numerical evidence, we use two independent data sets of over 800 patients, one to train the optimal policy, and the other to build a computer simulation to act as a test bed for said policy. We are able to show that our policy detects 22% more early-stage cancers than current practice, while using the same amount of resource expenditure. We provide insights into the structure underlying our policy and discuss the implications of our findings.
引用
收藏
页码:198 / 212
页数:15
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