Determining fleet size and vehicle locations in a distributed medical service network: Development and application of a dynamic location expected coverage model

The goal of emergency medical service (EMS) systems is the reduction of mortality and morbidity resulting from acute illness or trauma. The size of the ambulance fleet and the location of ambulances within the service area are two factors within the control of EMS system planners which directly affect the system's response time, and thus contribute to the attainment of this goal. Planners of urban EMS systems seek a design which will meet the national standard of system response within ten minutes to at least ninety-five percent of the demands for service.; A statistical analysis of data from 43,640 ambulance calls in Louisville, Kentucky, is conducted. Principal among the findings are: (1) the data do not support the assumption of exponentially distributed service times; and (2) in addition to the previously recognized spatial variation in the demand for EMS, a temporal variation is identified.; An EMS simulation model is developed and shown to be a valid abstraction of an actual EMS system. The simulation model is used to evaluate changes in system coverage under alternative dispatch policies. The results suggest that at higher utilizations, a minimum utilization dispatch priority provides greater coverage than a closest available dispatch priority.; Although some of the more recent location optimization models have included spatial variation in demand, none has included both spatial and temporal variation. The TIMEXCLP location model, which includes both of these sources of variance in prescribing the fleet size and locations necessary to achieve the 95 percent service standard, is developed. The most recently developed prescriptive model, MEXCLP, and TIMEXCLP are applied to the Louisville, Kentucky, EMS system.; Simulated performance of the system under each model's deployment strategy is compared to the system performance projected by each of the models. TIMEXCLP achieves greater accuracy than previous models in forecasting system performance.; The dynamic location of ambulances is an additional controllable factor which influences system coverage. TIMEXCLP is the first prescriptive model capable of yielding both static and dynamic deployment strategies. Dynamic deployments are shown to provide greater coverage than static deployments of the same fleet.; The EMS simulation model and TIMEXCLP are integrated into an operational decision support system (DSS). The DSS is applied to the Louisville, Kentucky, EMS system. It is shown to provide a process through which the EMS planner can conduct an efficient and effective search for a system design which meets the coverage objective with the minimum expenditure of resources.