This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Su, X. Articles by Zenios, S. A. Search for Related Content

Patient Choice in Kidney Allocation: A Sequential Stochastic Assignment Model

Walter A. Haas School of Business, University of California, Berkeley, California 94720
Graduate School of Business, Stanford University, 518 Memorial Way, Stanford, California 94305
xuanming{at}haas.berkeley.edu
stefzen{at}stanford.edu

This paper investigates the effect of patient choice on kidney allocation using the following sequential stochastic assignment model. There are n transplant patients to be allocated n kidneys that will arrive sequentially. Each patient and each kidney has its own type, kidney types are random and revealed upon arrival, and the reward from allocating a kidney to a particular patient depends on both their types. Patients may choose to accept or decline any kidney offer.

The objective is to determine a kidney allocation policy that maximizes total expected reward subject to the constraint that patients will only accept offers that maximize their own expected reward. A partition policy, in which the space of kidney types is divided into different domains (each corresponding to a different patient type) and in which each kidney is allocated to the patient type corresponding to its domain, is shown to be asymptotically optimal when patients must accept all kidney offers. To reflect patient choice, an incentive compatibility condition is derived to ensure that the offers made by the allocation policy are never declined. This condition is then used to derive a "second-best" partition policy. A numerical example, based on data from the US transplantation system, demonstrates that patient choice may introduce substantial inefficiencies, but the second-best policy recovers all the losses by minimizing the variability in the type of offers expected by each patient. Thus, policy makers should explicitly recognize the effect of patient choice when designing a kidney allocation system.

Subject classifications: kidney allocation:strategic behavior; incentive compatibility; assignment models:dynamic; stochastic.
History: Received February 2002; revision received April 2004; accepted April 2004.

This article has been cited by other articles:

				B. Sandikci, L. M. Maillart, A. J. Schaefer, O. Alagoz, and M. S. Roberts Estimating the Patient's Price of Privacy in Liver Transplantation Operations Research, November 1, 2008; 56(6): 1393 - 1410. [Abstract] [PDF]

				O. Alagoz, L. M. Maillart, A. J. Schaefer, and M. S. Roberts Choosing Among Living-Donor and Cadaveric Livers Management Science, November 1, 2007; 53(11): 1702 - 1715. [Abstract] [PDF]

				O. Alagoz, L. M. Maillart, A. J. Schaefer, and M. S. Roberts Determining the Acceptance of Cadaveric Livers Using an Implicit Model of the Waiting List Operations Research, January 1, 2007; 55(1): 24 - 36. [Abstract] [PDF]

				X. Su and S. A. Zenios Recipient Choice Can Address the Efficiency-Equity Trade-off in Kidney Transplantation: A Mechanism Design Model Management Science, November 1, 2006; 52(11): 1647 - 1660. [Abstract] [PDF]