Optimal Screening for Hepatocellular Carcinoma: A Restless Bandit Model

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.