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

Steven M. Shechter, Cindy L. Bryce, Oguzhan Alagoz, Jennifer E. Kreke, James E. Stahl, Andrew J. Schaefer, Derek C. Angus and Mark S. Roberts
Additional contact information
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

Medical Decision Making, 2005, vol. 25, issue 2, 199-209

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.

Keywords: liver transplantation; discrete-event simulation; simulation modeling; Monte Carlo simulation; organ allocation; patient survival; graft survival; policy analysis (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:25:y:2005:i:2:p:199-209

DOI: 10.1177/0272989X04268956

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