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A Simulation-Based Optimization Model to Study the Impact of Multiple-Region Listing and Information Sharing on Kidney Transplant Outcomes

Author

Listed:
  • Zahra Gharibi

    (Department of Management, Information Systems and Analytics, State University of New York at Plattsburgh, Plattsburgh, NY 12901, USA)

  • Michael Hahsler

    (Department of Engineering Management, Information, and Systems and Department of Computer Science, Southern Methodist University, Dallas, TX 75205, USA)

Abstract

More than 8000 patients on the waiting list for kidney transplantation die or become ineligible to receive transplants due to health deterioration. At the same time, more than 4000 recovered kidneys from deceased donors are discarded each year in the United States. This paper develops a simulation-based optimization model that considers several crucial factors for a kidney transplantation to improve kidney utilization. Unlike most proposed models, the presented optimization model incorporates details of the offering process, the deterioration of patient health and kidney quality over time, the correlation between patients’ health and acceptance decisions, and the probability of kidney acceptance. We estimate model parameters using data obtained from the United Network of Organ Sharing (UNOS) and the Scientific Registry of Transplant Recipients (SRTR). Using these parameters, we illustrate the power of the simulation-based optimization model using two related applications. The former explores the effects of encouraging patients to pursue multiple-region waitlisting on post-transplant outcomes. Here, a simulation-based optimization model lets the patient select the best regions to be waitlisted in, given their demand-to-supply ratios. The second application focuses on a system-level aspect of transplantation, namely the contribution of information sharing on improving kidney discard rates and social welfare. We investigate the effects of using modern information technology to accelerate finding a matching patient to an available donor organ on waitlist mortality, kidney discard, and transplant rates. We show that modern information technology support currently developed by the United Network for Organ Sharing (UNOS) is essential and can significantly improve kidney utilization.

Suggested Citation

  • Zahra Gharibi & Michael Hahsler, 2021. "A Simulation-Based Optimization Model to Study the Impact of Multiple-Region Listing and Information Sharing on Kidney Transplant Outcomes," IJERPH, MDPI, vol. 18(3), pages 1-20, January.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:3:p:873-:d:483776
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    References listed on IDEAS

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    1. Stefanos A. Zenios & Glenn M. Chertow & Lawrence M. Wein, 2000. "Dynamic Allocation of Kidneys to Candidates on the Transplant Waiting List," Operations Research, INFORMS, vol. 48(4), pages 549-569, August.
    2. Xuanming Su & Stefanos A. Zenios, 2005. "Patient Choice in Kidney Allocation: A Sequential Stochastic Assignment Model," Operations Research, INFORMS, vol. 53(3), pages 443-455, June.
    3. Roth, Alvin E. & Sonmez, Tayfun & Utku Unver, M., 2005. "Pairwise kidney exchange," Journal of Economic Theory, Elsevier, vol. 125(2), pages 151-188, December.
    4. 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.
    5. Hyunwoo Lee & Seokhyun Chung & Taesu Cheong & Sang Hwa Song, 2018. "Accounting for Fairness in a Two-Stage Stochastic Programming Model for Kidney Exchange Programs," IJERPH, MDPI, vol. 15(7), pages 1-16, July.
    6. Xuanming Su & Stefanos A. Zenios, 2006. "Recipient Choice Can Address the Efficiency-Equity Trade-off in Kidney Transplantation: A Mechanism Design Model," Management Science, INFORMS, vol. 52(11), pages 1647-1660, November.
    7. Dimitris Bertsimas & Vivek F. Farias & Nikolaos Trichakis, 2013. "Fairness, Efficiency, and Flexibility in Organ Allocation for Kidney Transplantation," Operations Research, INFORMS, vol. 61(1), pages 73-87, February.
    8. Howard, David H., 2002. "Why do transplant surgeons turn down organs?: A model of the accept/reject decision," Journal of Health Economics, Elsevier, vol. 21(6), pages 957-969, November.
    9. Jae-Hyeon Ahn & John C. Hornberger, 1996. "Involving Patients in the Cadaveric Kidney Transplant Allocation Process: A Decision-Theoretic Perspective," Management Science, INFORMS, vol. 42(5), pages 629-641, May.
    10. R. Jean Ruth & Leon Wyszewianski & Gary Herline, 1985. "Kidney Transplantation: A Simulation Model for Examining Demand and Supply," Management Science, INFORMS, vol. 31(5), pages 515-526, May.
    11. 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.
    12. Stefanos A. Zenios, 2002. "Optimal Control of a Paired-Kidney Exchange Program," Management Science, INFORMS, vol. 48(3), pages 328-342, March.
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