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Top1. Introduction
Full-time and volunteer staff is operating in relief organizations such as German Red Cross in order to ensure Germany’s medical civil protection on duty for federal, state and local authorities. In addition to regular emergency medical services (EMS) facing small daily-happening incidents, they provide medical civil protection facing larger crisis scenarios. A mass casualty incident (MCI) is defined as: “an emergency with a larger number of injured, diseased or affected people that cannot be managed with existing resources from the operational area” (BBK, 2013). In disaster medicine, special mission tactics apply, such as a triage workflow assigning triage categories (T1–T3) to each patient. Since such missions occur quite seldom, both regular emergency medical service staff and voluntary personnel lack of routine in accomplishing such situations. This can result in sub-optimal handling of MCIs. Practitioners and experts therefore desire improved resource planning across county boundaries as well as improved training for decision makers (DRK, 2012).
For strengthening MCI mission accomplishment, mission workflows could be enhanced by new interactive systems as e.g. tablets for assessing triage categories. However, with new technology also new uncertainties for responders appear (Ellebrecht & Kaufmann, 2015). Instead of the response phase, the preparedness phase of MCIs could be enhanced by the use of interactive modeling and simulation systems and applications that allow testing of mission tactics while taking into account local constraints on medical rescue resources and hospitals. The main requirements for such tools are: (1) less effort compared to e.g. organization of field trainings; (2) trustworthy analytical predictions which can be validated against base data; and (3) intuitive user guidance to assure user acceptance (Sautter et al., 2015).
Such simulation tools should be used regularly by leading personnel in civil protection to discuss and elaborate different tactical options under local boundary conditions such as available resources and hospital capacities (Sautter et al., 2014a). Users have to be able to both express their simulation needs and process the resulting data in a way that is as compatible with their current workflows, procedures and organization as possible.
Nowadays, most simulation tools are operated by simulation experts, e.g. in the domain of logistics. A simulation based analysis has been done for the capacity of the planned new underground railway station “Stuttgart 21” (SMA und Partner AG, 2011), which raised an enormous public dialogue (stuttgarter-zeitung.de, 2015). Hence trust in model and modeler is crucial and users need to be aware of uncertainties they deal with. Unfortunately, the civil protection is a particularly critical usage context for interactive information systems (Nestler, 2014). Therefore designing systems that accept parameter inputs and show the results in an adequate manner for non-expert users in the field of modelling is challenging for designers of interaction concepts (Sautter et al., 2012).