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Patient Choice in Kidney Allocation: The Role of the Queueing Discipline


  • Xuanming Su

    () (Walter A. Haas School of Business, University of California, Berkeley, California 94720)

  • Stefanos Zenios

    () (Graduate School of Business, Stanford University, Stanford, California 94305)


This paper develops and analyzes a queueing model to examine the role of patient choice on the high rate of organ refusals in the kidney transplant waiting system. The model is an M/M/1 queue with homogeneous patients and exponential reneging. Patients join the waiting system and organ transplants are reflected by the service process. In addition, unlike the standard M/M/1 model, each service instance is associated with a variable reward that reflects the quality of the transplant organ, and patients have the option to refuse an organ (service) offer if they expect future offers to be better. Under an assumption of perfect and complete information, it is demonstrated that the queueing discipline is a potent instrument that can be used to maximize social welfare. In particular, first-come-first-serve (FCFS) amplifies patients' desire to refuse offers of marginal quality, and generates excessive organ wastage. By contrast, last-come-first-serve (LCFS) contains the inefficiencies engendered by patient choice and achieves optimal organ utilization. A numerical example calibrated using data from the U.S. transplantation system demonstrates that the welfare improvements possible from a better control of patient choice are equivalent to a 25% increase in the supply of organs.

Suggested Citation

  • Xuanming Su & Stefanos Zenios, 2004. "Patient Choice in Kidney Allocation: The Role of the Queueing Discipline," Manufacturing & Service Operations Management, INFORMS, vol. 6(4), pages 280-301, June.
  • Handle: RePEc:inm:ormsom:v:6:y:2004:i:4:p:280-301

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    Cited by:

    1. repec:pal:jorsoc:v:60:y:2009:i:4:d:10.1057_palgrave.jors.2602587 is not listed on IDEAS
    2. Caulkins, Jonathan P., 2010. "Might randomization in queue discipline be useful when waiting cost is a concave function of waiting time?," Socio-Economic Planning Sciences, Elsevier, vol. 44(1), pages 19-24, March.
    3. Bendersky, Michael & David, Israel, 2016. "Deciding kidney-offer admissibility dependent on patients’ lifetime failure rate," European Journal of Operational Research, Elsevier, vol. 251(2), pages 686-693.
    4. Juanjuan Zhang, 2010. "The Sound of Silence: Observational Learning in the U.S. Kidney Market," Marketing Science, INFORMS, vol. 29(2), pages 315-335, 03-04.
    5. Francis Bloch & David Cantala, 2017. "Dynamic Assignment of Objects to Queuing Agents," American Economic Journal: Microeconomics, American Economic Association, vol. 9(1), pages 88-122, February.
    6. Fermín Mallor & Cristina Azcárate & Julio Barado, 2015. "Optimal control of ICU patient discharge: from theory to implementation," Health Care Management Science, Springer, vol. 18(3), pages 234-250, September.
    7. Murat Kurt & Mark S. Roberts & Andrew J. Schaefer & M. Utku Ünver, 2011. "Valuing Prearranged Paired Kidney Exchanges: A Stochastic Game Approach," Boston College Working Papers in Economics 785, Boston College Department of Economics, revised 14 Oct 2011.
    8. R. K. Jha & B. S. Sahay & P. Charan, 2016. "Healthcare operations management: a structured literature review," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 43(3), pages 259-279, September.
    9. Senthil Veeraraghavan & Laurens Debo, 2009. "Joining Longer Queues: Information Externalities in Queue Choice," Manufacturing & Service Operations Management, INFORMS, vol. 11(4), pages 543-562, April.
    10. Dimitris Bertsimas & Vivek F. Farias & Nikolaos Trichakis, 2012. "On the Efficiency-Fairness Trade-off," Management Science, INFORMS, vol. 58(12), pages 2234-2250, December.
    11. repec:eee:ejores:v:265:y:2018:i:1:p:169-177 is not listed on IDEAS


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