Acute Zorg Euregio (AZE) is one of eleven regional network organisations in the Netherlands designated to optimise the collaboration of all regional providers in the emergency care chain. Besides the regular emergency care for individual patients, the preparation for emergency care in case of a disaster or crisis also belongs to the tasks of AZE. In order to be prepared for mass casualty incidents, full scale disaster exercises are important. For this purpose, exercises in our region are executed by using the validated simulation system Emergo Train System (ETS). The core of ETS consists of a victim database with specific casualties and each victim has a defined medical need within a certain time frame. Next to that, all available resources within the region are known, such as number and location of emergency ambulance and helicopter services and travel times to an incident location, emergency department and hospital treatment capacity.
AZE was curious if a mathematical model could improve the allocation of patients to a hospital in case of a mass casualty incident. Like for severely injured patients the survival rate will be improved if the patient is quicker in the right hospital and with respect of capacity, the number of patients offered to the emergency department on the same time frame also matters. Therefore an Integer Linear Programming model was made to assign patients to hospitals (https://essay.utwente.nl/73173/). Comparing the results of the mathematical model to the results of the exercise, we saw that the average arrival time in the hospital can be shortened by 10 minutes. In addition, the number of hospitals involved in the disaster is reduced and both the sum of all travel times and the time when the last persons arrive at a hospital are shortened.
These results were promising but it is recommended to made improvements on data collection (some data was lacking and assumptions were made) in future exercises and besides this other mathematical techniques can be used. For Autumn 2019 new exercises were plannend and these new data in combination with new thechniques can be used to improve the model in allocation of patients.
Can the decision-making in distribution of patients to the most suitable hospital in the most fastest way from a mass casualty incident be improved by using mathematical optimalization?
SUPERVISON / MORE INFORMATION
Dr. Derya Demirtas, firstname.lastname@example.org
Dr. Nancy ter Bogt, email@example.com