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A Clinically Based Discrete-Event Simulation of End-Stage Liver Disease and the Organ Allocation Process

Author

Listed:
  • Steven M. Shechter

    (Department of Industrial Engineering, University of Pittsburgh, Pennsylvania)

  • Cindy L. Bryce

    (Center for Research on Health Care, University of Pittsburgh, Pennsylvania, the Section of Decision Sciences and Clinical Systems Modeling, Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pennsylvania)

  • Oguzhan Alagoz

    (Department of Industrial Engineering, University of Pittsburgh, Pennsylvania)

  • Jennifer E. Kreke

    (Department of Industrial Engineering, University of Pittsburgh, Pennsylvania)

  • James E. Stahl

    (MGH-Institute for Technology Assessment, Massachusetts General Hospital, Boston)

  • Andrew J. Schaefer

    (Department of Industrial Engineering, Center for Research on Health Care, University of Pittsburgh, Pennsylvania, the Section of Decision Sciences and Clinical Systems Modeling, Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pennsylvania)

  • Derek C. Angus

    (Center for Research on Health Care, University of Pittsburgh, Pennsylvania, CRISMA Laboratory, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pennsylvania)

  • Mark S. Roberts

    (Department of Industrial Engineering, Center for Research on Health Care, University of Pittsburgh, Pennsylvania, the Section of Decision Sciences and Clinical Systems Modeling, Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pennsylvania)

Abstract

Background . The optimal allocation of scarce donor livers is a contentious health care issue requiring careful analysis. The objective of this article was to design a biologically based discrete-event simulation to test proposed changes in allocation policies. Methods . The authors used data from multiple sources to simulate end-stage liver disease and the complex allocation system. To validate the model, they compared simulation output with historical data. Results . Simulation outcomes were within 1% to 2% of actual results for measures such as new candidates, donated livers, and transplants by year. The model overestimated the yearly size of the waiting list by 5% in the last year of the simulation and the total number of pretransplant deaths by 10%. Conclusion . The authors created a discrete-event simulation model that represents the biology of end-stage liver disease and the health care organization of transplantation in the United States.

Suggested Citation

  • Steven M. Shechter & Cindy L. Bryce & Oguzhan Alagoz & Jennifer E. Kreke & James E. Stahl & Andrew J. Schaefer & Derek C. Angus & Mark S. Roberts, 2005. "A Clinically Based Discrete-Event Simulation of End-Stage Liver Disease and the Organ Allocation Process," Medical Decision Making, , vol. 25(2), pages 199-209, March.
    • Handle: RePEc:sae:medema:v:25:y:2005:i:2:p:199-209
    DOI: 10.1177/0272989X04268956
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    Citations

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    Cited by:

    1. Ozge Ceren Ersoy & Diwakar Gupta & Timothy Pruett, 2021. "A critical look at the U.S. deceased‐donor organ procurement and utilization system," Naval Research Logistics (NRL), John Wiley & Sons, vol. 68(1), pages 3-29, February.
    2. Nan Kong & Andrew J. Schaefer & Brady Hunsaker & Mark S. Roberts, 2010. "Maximizing the Efficiency of the U.S. Liver Allocation System Through Region Design," Management Science, INFORMS, vol. 56(12), pages 2111-2122, December.
    3. Maria Bruni & Domenico Conforti & Nicola Sicilia & Sandro Trotta, 2006. "A new organ transplantation location–allocation policy: a case study of Italy," Health Care Management Science, Springer, vol. 9(2), pages 125-142, May.
    4. Durai Sundaramoorthi & Victoria Chen & Jay Rosenberger & Seoung Kim & Deborah Buckley-Behan, 2009. "A data-integrated simulation model to evaluate nurse–patient assignments," Health Care Management Science, Springer, vol. 12(3), pages 252-268, September.
    5. Duraikannan Sundaramoorthi & Victoria Chen & Jay Rosenberger & Seoung Kim & Deborah Buckley-Behan, 2010. "A data-integrated simulation-based optimization for assigning nurses to patient admissions," Health Care Management Science, Springer, vol. 13(3), pages 210-221, September.
    6. Jingyu Zhang & Brian T. Denton & Hari Balasubramanian & Nilay D. Shah & Brant A. Inman, 2012. "Optimization of Prostate Biopsy Referral Decisions," Manufacturing & Service Operations Management, INFORMS, vol. 14(4), pages 529-547, October.
    7. Karen T. Hicklin & Julie S. Ivy & James R. Wilson & Fay Cobb Payton & Meera Viswanathan & Evan R. Myers, 2019. "Simulation model of the relationship between cesarean section rates and labor duration," Health Care Management Science, Springer, vol. 22(4), pages 635-657, December.
    8. Cannon, Jeffrey W. & Mueller, Ute A. & Hornbuckle, Janet & Larson, Ann & Simmer, Karen & Newnham, John P. & Doherty, Dorota A., 2013. "Economic implications of poor access to antenatal care in rural and remote Western Australian Aboriginal communities: An individual sampling model of pregnancy," European Journal of Operational Research, Elsevier, vol. 226(2), pages 313-324.
    9. Sait Tunç & Burhaneddin Sandıkçı & Bekir Tanrıöver, 2022. "A Simple Incentive Mechanism to Alleviate the Burden of Organ Wastage in Transplantation," Management Science, INFORMS, vol. 68(8), pages 5980-6002, August.
    10. Burhaneddin Sandıkçı & Lisa M. Maillart & Andrew J. Schaefer & Oguzhan Alagoz & Mark S. Roberts, 2008. "Estimating the Patient's Price of Privacy in Liver Transplantation," Operations Research, INFORMS, vol. 56(6), pages 1393-1410, December.
    11. Sinem Savaşer & Ömer Burak Kınay & Bahar Yetis Kara & Pelin Cay, 2019. "Organ transplantation logistics: a case for Turkey," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(2), pages 327-356, June.
    12. 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.
    13. Burhaneddin Sandıkçı & Lisa M. Maillart & Andrew J. Schaefer & Mark S. Roberts, 2013. "Alleviating the Patient's Price of Privacy Through a Partially Observable Waiting List," Management Science, INFORMS, vol. 59(8), pages 1836-1854, August.
    14. Sommer Gentry & Eric Chow & Allan Massie & Dorry Segev, 2015. "Gerrymandering for Justice: Redistricting U.S. Liver Allocation," Interfaces, INFORMS, vol. 45(5), pages 462-480, October.
    15. Barış Ata & Anton Skaro & Sridhar Tayur, 2017. "OrganJet: Overcoming Geographical Disparities in Access to Deceased Donor Kidneys in the United States," Management Science, INFORMS, vol. 63(9), pages 2776-2794, September.
    16. Mehmet C. Demirci & Andrew J. Schaefer & H. Edwin Romeijn & Mark S. Roberts, 2012. "An Exact Method for Balancing Efficiency and Equity in the Liver Allocation Hierarchy," INFORMS Journal on Computing, INFORMS, vol. 24(2), pages 260-275, May.
    17. Mustafa Akan & Oguzhan Alagoz & Baris Ata & Fatih Safa Erenay & Adnan Said, 2012. "A Broader View of Designing the Liver Allocation System," Operations Research, INFORMS, vol. 60(4), pages 757-770, August.
    18. Oguzhan Alagoz & Lisa M. Maillart & Andrew J. Schaefer & Mark S. Roberts, 2007. "Choosing Among Living-Donor and Cadaveric Livers," Management Science, INFORMS, vol. 53(11), pages 1702-1715, November.

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