Existing studies of organ transplantation report various disparities in access. Disparities have been found in terms of race, socioeconomic status, insurance type and the location of candidate's residency. While these disparities tend to coexist, disparity associated with candidates'locations or """"""""geographical disparity"""""""" is the first and foremost discussed. To remedy this, the importance of redrawing the organ distribution boundaries has been discussed in recent years. No rigorous method for redrawing has, however, been explored. Our research team thus proposes to develop an analytical framework for optimizing both organ allocation and boundary creation by applying some cutting-edge techniques involving Discrete Event Simulation (DES) and Geographic Information Systems (GIS) based mapping. The proposed system differs from the existing system in two main respects. First, the boundary is drawn real-time based on the location of liver retrieval and the maximum cold ischemia time allowed. Second, the proposed system minimizes geographical disparity in receiving an organ in addition to the other policy objectives embedded in the current system, such as prioritizing the candidates with a higher medical urgency and compatibility with the organ that became available. This is a multi-objective optimization problem in which two or more policy goals could exhibit trade-off relationships. The commonly-used method of weighting will be applied to solve this problem. As the main final product, the research team intends to provide policy makers with an interactive GIS-based open simulation webpage that visualizes: (i) geographical boundaries of organ distribution areas that meet specific objectives and conditions related to organ allocation, and (ii) important consequences of employing such an allocation and boundary system, in particular, alleviating geographical disparities in access to organ transplantation. The primary interest of the proposed research is to see how an optimal organ allocation system developed by our systems science technologies can achieve a more equitable and efficient organ allocation system. While the disparities in organ allocations have been evident irrespective of the type of organ, they are most disputed in liver transplants. The research team thus plans to focus on liver transplant in the proposed study although a successful model could be modified and extended to deal with the allocation systems of other organs. In addition, we will focus on the adult transplants although appropriate modifications could be made to establish an equivalent framework for pediatric transplants.
While the importance of redrawing the organ distribution boundaries has been discussed in recent years, no rigorous method for the redrawing has been explored. Our research team proposes an analytical framework using Geographical Information Systems and Discrete Event Simulations to identify such boundaries and build an allocation system that reduces geographical disparity within each boundary. Our work has the potential to revolutionize the system of organ allocation in the United States, making it more just and more efficient.
| Zhang, Si; Xu, Jie; Lee, Loo Hay et al. (2017) Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Optimization. IEEE Trans Evol Comput 21:206-219 |
| Koizumi, Naoru; DasGupta, Debasree; Patel, Amit V et al. (2015) Geographic Variation in Cold Ischemia Time: Kidney vs. Liver Transplantation in the United States, 2003-2011. Transplant Direct 1:e27 |
| LaPorte, G Jake; Branke, Juergen; Chen, Chun-Hung (2015) Adaptive Parent Population Sizing in Evolution Strategies. Evol Comput 23:397-420 |
| Brantley, Mark W; Lee, Loo Hay; Chen, Chun-Hung et al. (2014) An Efficient Simulation Budget Allocation Method Incorporating Regression for Partitioned Domains. Automatica (Oxf) 50:1391-1400 |
| Koizumi, Naoru; Ganesan, Rajesh; Gentili, Monica et al. (2014) Redesigning Organ Allocation Boundaries for Liver Transplantation in the United States. Proc Int Conf Health Care Syst Eng (2013) 61:15-27 |
| Jia, Qing-Shan; Zhou, Enlu; Chen, Chun-Hung (2013) Efficient Computing Budget Allocation for Finding Simplest Good Designs. IIE Trans 45:736-750 |
