Recently, mass casualty incidents (MCIs) have been occurring frequently and have gained international attention. There is an urgent need for scientifically proven and effective emergency responses to MCIs, particularly as the severity of incidents is continuously increasing. The emergency response to MCIs is a multi-dimensional and multi-participant dynamic process that changes in real-time. The evacuation decisions that assign casualties to different hospitals in a region are very important and impact both the results of emergency treatment and the efficiency of medical resource utilization. Previously, decisions related to casualty evacuation were made by an incident commander with emergency experience and in accordance with macro emergency guidelines. There are few decision-supporting tools available to reduce the difficulty and psychological pressure associated with the evacuation decisions an incident commander must make. In this study, we have designed a mobile-based system to collect medical and temporal data produced during an emergency response to an MCI. Using this information, our system's decision-making model can provide personal evacuation suggestions that improve the overall outcome of an emergency response. The effectiveness of our system in reducing overall mortality has been validated by an agent-based simulation model established to simulate an emergency response to an MCI.