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Diagnostic imaging services are essential to the diagnosis pathway for many patients arriving at hospital emergency departments with a suspected fracture. Commonly, these patients need to be seen again by a doctor or emergency nurse practitioner after an X-Ray image has been taken in order finalise the diagnosis and determine the next stage in the patients' pathway. Here, significant waiting times can accrue for these follow-up consultations after radiographic imaging although the vast majority of patients are discharged. Research evidence from pilot studies suggests that patients with minor appendicular injuries could be safely discharged by a suitably qualified radiographer directly after imaging thereby avoiding queues for repeated consultation. In this study, we model patient pathways through an emergency department (ED) at a hospital in the South West of England using process mapping, interviews with ED staff and discrete event simulation (DES). The DES model allowed us to compare the current practice at the hospital with scenarios using radiographer-led discharge of patients directly after imaging and assess the reduction in patients' length of stay in ED. We also quantified trade-offs between the provision of radiographer-led discharge and its effects, i.e. reduction in waiting times and ED workload. Finally, we discuss how this decision support tool can be used to support understanding for patients and members of staff.

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Empirical studies considering the location and relocation of emergency medical service (EMS) vehicles in an urban region provide important insight into dynamic changes during the day. Within a 24-hour cycle, the demand, travel time, speed of ambulances and areas of coverage change. Nevertheless, most existing approaches in literature ignore these variations and require a (temporally and spatially) fixed (double) coverage of the planning area. Neglecting these variations and fixation of the coverage could lead to an inaccurate estimation of the time-dependent fleet size and individual positioning of ambulances. Through extensive data collection, now it is possible to precisely determine the required coverage of demand areas. Based on data-driven optimization, a new approach is presented, maximizing the flexible, empirically determined required coverage, which has been adjusted for variations due to day-time and site. This coverage prevents the EMS system from unavailability of ambulances due to parallel operations to ensure an improved coverage of the planning area closer to realistic demand. An integer linear programming model is formulated in order to locate and relocate ambulances. The use of such a programming model is supported by a comprehensive case study, which strongly suggests that through such a model, these objectives can be achieved and lead to greater cost-effectiveness and quality of emergency care.

Patients presenting with chest pain at an emergency department in the United Kingdom receive troponin tests to assess the likelihood of an acute myocardial infarction (AMI). Until recently, serial testing with two blood samples separated by at least six hours was necessary in order to analyse the change in troponin levels over time. New high-sensitivity troponin tests, however, allow the inter-test time to be shortened from six to three hours. Recent evidence also suggests that the new generation of troponin tests can be used to rule out AMI on the basis of a single test if patients at low risk of AMI present with very low cardiac troponin levels more than three hours after onset of worst pain. This paper presents a discrete event simulation model to assess the likely impact on the number of hospital admissions if emergency departments adopt strategies for serial and single testing based on the use of high-sensitivity troponin. Data sets from acute trusts in the South West of England are used to quantify the resulting benefits.