Simulation and Predictive Modeling for Organ Donation Process Optimization

Organ transplantation significantly improves both life quality and longevity by replacing damaged organs. Its success depends not only on medical expertise, but also on how efficiently and swiftly the organ donation process is managed, as delays can lead to organ degradation and reduced transplant success rates. Using real-world data from the Regional Transplant Center of Lazio (Italy), we model the entire donation process and quantify the duration and cost of each activity. We employ probabilistic models, simulation, and predictive modeling to analyze the process and evaluate alternative management policies, with a particular focus on the critical and uncertain step of consent acquisition. By integrating these elements, we examine trade-offs between time efficiency and cost effectiveness. The findings provide a decision support system that helps decision-makers at the Transplant Center select the most appropriate strategy for each case, while minimizing delays and avoiding unnecessary costs.