Data Driven Ambulance Optimization Considering Dynamic and Economic Aspects

Providing high quality emergency medical services (EMS) and ensuring accessibility to these services for the general public is a key task for health care systems. Given a limited budget available resources, e.g. ambulances, have to be used economically in order to ensure a high quality coverage. Emergency vehicles have to be positioned and repositioned such that emergencies can be reached within a legal time frame. Empirical studies have shown temporal and spatial variations of emergency demand as well as variations of travel times during a day. The numbers of emergency calls within 24 h differ significantly between night and day and show peaks especially during rush hours. We provide a data driven model considering time and spatial dependent degrees of coverage. This allows a simultaneous optimization of empirically required coverage with minimal number of ambulances, respectively costs. Therefore utilization and quality criteria are to be implemented. An integer linear program is formulated using time periods in order to model time-dependent demand and time-dependent travel times. It is shown on large empirical data records that the presented dynamic model outperforms existing static models with respect to coverage and utilization of resources.