Stochastic Control for Organ Donations: A Review

TL;DR

This paper reviews the application of Markov Decision Process models to optimize organ acceptance decisions in transplantation, highlighting control policies and future research directions.

Contribution

It introduces an MDP framework for organ acceptance decisions, classifies existing research, and emphasizes control limit policies as practical solutions.

Findings

Control limit policies are optimal under certain conditions.

The MDP framework effectively models organ acceptance decisions.

The review identifies open problems and future research directions.

Abstract

We review the literature on individual patient organ acceptance decision making by presenting a Markov Decision Process (MDP) model to formulate the organ acceptance decision process as a stochastic control problem. Under the umbrella of the MDP framework, we classify and summarize the major research streams and contributions. In particular, we focus on control limit-type policies, which are shown to be optimal under certain conditions and easy to implement in practice. Finally, we briefly discuss open problems and directions for future research.