The healthcare system is one of the most vital service sectors in society, and improving the efficiency of this system in different countries is of great importance. This system may face challenges in an emergency. Emergencies are unpredictable crises, and they require pro‎mp‎t response. A significant number of deaths are due to excessive delays in rescue operations during disasters and crises. Timely identification, and transfer of patients to medical centers in time, can be very effective in reducing mortality. Sophisticated technologies, along with advanced infrastructure, can improve the performance of the healthcare system. Therefore, these technologies play a vital role in reducing the mortality rate. However, these infrastructures and technologies are rarely available in less developed countries, and low-cost solutions must be used to address this issue. Telemedicine is one of the efficient ways that can accelerate the process of diagnosis and treatment of patients in disasters. In case of accidents and crises, the number of patients and injured will increase, so the time of transferring patients to a hospital will increase. Using telemedicine, treatment staff can start initial treatment during transferring of patients. The ambulance's personnel send patient's data to an emergency physician or a specialist, and ask for help. As a result, they start the treatment in an ambulance, and delays in rescue operations will decline. During disasters and crises, due to a large number of requests, the requests' response time increases, and in some cases, patients will arrive at the hospital before the personnel receive the response. Therefore, it is necessary to provide a model for the current healthcare system that can examine the effect of various parameters on delays in the treatment process. Then, we have to introduce strategies to reduce the mentioned delay. In this research, after getting acquainted with the traditional healthcare system, we model a fog-enabled pre-hospital healthcare system using queueing theory. In the following, we obtain various delays in the current healthcare system and the fog-enabled pre-hospital healthcare system. In what follows, we simulate the proposed models using Python and the Ciw library. The simulation results show that should the number of well-equipped ambulances (type C ambulances) and the number of physicians responding to ambulances' requests are sufficient, fog-enabled pre-hospital healthcare system will reduce the mortality rate by about 74 percent during fatal disasters.

حسين سعيدي

علي قياسيان

جلال ذهبي، امير خورسندي