Towards a Unified Understanding of Data-Driven Support for Emergency Medical Service Logistics

Reuter-Oppermann, Melanie
Wolff, Clemens
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Time-critical medical emergencies challenge emergency medical service (EMS) systems worldwide every day. In order to respond to these incidents as soon as possible, EMS logistics' approaches can help locating and dispatching ambulances. Many of these approaches use estimates for the demand as well as the driving, service and turnaround times. In order to determine useful solutions and make informed decisions, reliable forecasts are necessary that take the characteristics and constraints of the planning problems at different levels into account. While many different approaches have been presented and tested in literature, a common understanding is still missing. This paper therefore proposes a taxonomy on EMS forecasting that distinguishes between medical emergencies and patient transports, demand and time intervals in the response process, as well as the three planning levels strategic, tactical and operational. In addition, an illustrative example and a research agenda are presented based on the findings for the taxonomy.
Optimization of and the Use of IT for Healthcare Processes, ems, forecasting, machine learning, taxonomy
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