Increasing and Assessing the Impact of Patient Choice in Liver Transplantation

In 2009, almost 1,500 Americans died of end-stage liver disease (ESLD), which is the twelfth leading cause of death in the U.S. As liver transplantation is the only possible therapy for ESLD and there is a considerable difference between the number of donated organs and patients, it is important to manage donor-patient match and investigate alternative treatments to transplantation.Every patient lists in at least one waiting list (OPO) in order to be eligible for a donated organ. However, patients may list in additional OPOs. This practice is called multiple listing. Currently, multiple listing is one of the most debated topics in organ allocation.Although transplantation is a successful procedure, it may not be available on time due to the massive shortage of donated organs. Therefore, an alternative therapy to transplantation is needed. Liver Assist Devices (LADs) are an emerging therapy for ESLD that aim to stabilize a patient until transplantation or her own organ recovers.In this dissertation, we discuss three models that are related to ESLD. In the first model, we optimize the three-stage decision process faced by a single patient. The patient decides her geographic location, in which OPOs to multiple list, and which organ offers to accept. This problem is formulated as a continuous-time Markov Decision Process (MDP). We derive structural properties of this model and solve it using clinical data.The second model analyzes multiple listing from the societal perspective. Utilizing an existing simulation of the U.S. liver allocation system, we give every patient the flexibility to multiple list. Therefore, we evaluate the effects of multiple listing on every wait-listed patient, rather than on a single patient. We also study the same problem where multiple listing is a more widespread practice in the U.S.The third model considers a hypothetical system in which an internal LAD is available.So, in addition to the liver accept/reject decision, patients can decide to accept an LAD. This model aims to help manufacturers by estimating potential demand for an LAD. We model this problem as a discrete-time MDP and give sufficient conditions under which an LAD will be worthwhile