Emergency services worldwide face increasing cost pressure that potentially limits their existing resources. In many countries, emergency services also face the issues of staff shortage-creating extra challenges and constraints, especially during crisis times such as the COVID-19 pandemic-as well as long distances to sparsely populated areas resulting in longer response times. To overcome these issues and potentially reduce consequences of daily (medical) emergencies, several countries, such as Sweden, Germany, and the Netherlands, have started initiatives using new types of human resources as well as equipment, which have not been part of the existing emergency systems before. These resources are employed in response to medical emergency cases if they can arrive earlier than emergency medical services (EMS). A good number of studies have investigated the use of these new types of resources in EMS systems, from medical, technical, and logistical perspectives as their study domains. Several review papers in the literature exist that focus on one or several of these new types of resources. However, to the best of our knowledge, no review paper that comprehensively considers all new types of resources in emergency medical response systems exists. We try to fill this gap by presenting a broad literature review of the studies focused on the different new types of resources, which are used prior to the arrival of EMS. Our objective is to present an application-based and methodological overview of these papers, to provide insights to this important field and to bring it to the attention of researchers as well as emergency managers and administrators.

Initiatives with mobile phone dispatched volunteers to out-of-hospital cardiac arrest (OHCA) cases, can be found today in some countries, e.g. Sweden, the Netherlands, Switzerland and Italy. When an OHCA case is reported, an alarm is sent to the registered volunteers’ phones. However, the allocation of which volunteers to send to the automatic external defibrillator (AED) and who to send directly to the patient, is today based on simple rules of thumb. In this paper, we propose a model to optimally select how many and which volunteers to send directly to the patient, and who should pick up and deliver an AED. The results show that the model can help increase the survivability of the patients, compared to simple decision rules.

In countries such as Sweden, Italy, Switzerland, and the Netherlands, projects in which volunteers are dispatched to out-of-hospital cardiac arrest (OHCA) patients with the use of mobile phones exist. Once an OHCA case is reported, a notification is sent to the mobile phones of registered volunteers that are in the vicinity of the patient. These projects mostly use static dispatch methods to determine which volunteers should be sent directly to the patient and which ones should pick up an automatic external defibrillator (AED). However, such schemes do not consider uncertainties associated with these task assignment decisions (e.g., if volunteers will do as instructed, or do something else). In this paper, we propose a method for optimized task assignment and dispatch of volunteers to OHCA patients that considers the uncertainty related to volunteers actions once assigned a task. We then compare the results of our method to those of a static dispatch method used in an ongoing mobile phone volunteer dispatch project in Sweden and validate them using simulation. Furthermore, we perform a sensitivity analysis on several parameters to investigate their effect on the performance of the proposed method. With the comparative results we show that the proposed method may help increase the survivability of OHCA patients.

Accurate forecast of the demand for emergency medical services (EMS) can help in providing quick and efficient medical treatment and transportation of out-of-hospital patients. The aim of this research was to develop a forecasting model and investigate which factors are relevant to include in such model. The primary data used in this study was information about ambulance calls in three Swedish counties during the years 2013 and 2014. This information was processed, assigned to spatial grid zones and complemented with population and zone characteristics. A Zero-Inflated Poisson (ZIP) regression approach was then used to select significant factors and develop the forecasting model. The model was compared to the forecasting model that is currently incorporated in the EMS information system used by the ambulance dispatchers. The results show that the proposed model performs better than the existing one.