MANAGEMENT OF A HOSPITAL EMERGENCY DEPARTMENT IN A HEAVY TRAFFIC SCENARIO

Emergency department crowding has been one of the major issues in healthcare systems. One solution to this is the prediction of admissions in the emergency department and allocating resources needed for the admitted patients. In this paper, predictive model is developed using neural networks for predicting the admissions. An expert selection algorithm is developed to assign the department/doctors for the admitted patients. After getting the references the availability of resources needed for the admission is done and the available resources are allocated. Neural networks are used to build the predictive model because of its ability to develop an artificial system which can do intelligent tasks similar to human brain and its ability to represent both linear and non-linear relationships. The system developed effectively reduced crowding in the emergency department by predicting the admissions and does the needful for the admissions. It also helped effectively manage the admissions from the emergency department when there was a crowded scenario.