Introducing fairness in Norwegian air ambulance base location planning

Abstract

Background A primary task of the Norwegian helicopter emergency medical services (HEMS) is to provide advanced medical care to the critical ill and injured outside of hospitals. Where HEMS bases are located, directly influences who in the population can be reached within a given response time threshold and who cannot. When studying the locations of bases, the focus is often on efficiency, that is, maximizing the total number of people that can be reached within a given set time. This approach is known to benefit people living in densely populated areas, such as cities, over people living in remote areas. The most efficient solution is thus typically not necessarily a fair one. This study aims to incorporate fairness in finding optimal air ambulance base locations. Methods We solve multiple advanced mathematical optimization models to determine optimal helicopter base locations, with different optimization criteria related to the level of aversion to inequality, including the utilitarian, Bernoulli-Nash and iso-elastic social welfare functions. This is the first study to use the latter social welfare function for HEMS. Results Focusing on efficiency, a utilitarian objective function focuses on covering the larger cities in Norway, leaving parts of Norway largely uncovered. Including fairness by rather using an iso-elastic social welfare function in the optimization avoids leaving whole areas uncovered and in particular increases service levels in the north of Norway. Conclusions Including fairness in determining optimal HEMS base locations has great impact on population coverage, in particular when the number of base locations is not enough to give full coverage of the country. As results differ depending on the mathematical objective, the work shows the importance of not only looking for optimal solutions, but also raising the essential question of ‘optimal with respect to what’.

Top Research Priorities in Prehospital Care in Spain

“…Appropriate resource management seems to be a recurring concern among emergency professionals, since they move in an environment with scarce resources that must be used and positioned in the most effective and efficient way possible. 16 In addition, there are multiple studies that link new technologies, big data, and artificial intelligence (identified as a priority) with the management of calls and the sending of resources by the Emergency Call Center. 17,18 Therefore, there are links between research priorities, which highlights the need for a cross-sectional approach as one of the elements to consider in prehospital emergency research.…”

Towards Fairness-Aware Multi-Objective Optimization

“…5) Fairness in resource allocation: Resource allocation problems (RAPs) [156] are ubiquitous in many areas, such as task scheduling [157], [158], emergency service allocation [159], [160] and Cloud service allocation [44], [43]. In recent decades, equitable allocation of resources that ensures equitable distribution of resources to users [39], [40], [41], has become a popular research topic.…”

“…In [166], the notion of fairness is to ensure that the maximization of the covered demand of one facility does not negatively impact the demand coverage of others. In ambulance location problem [160], [167], the fairness is denoted by the maximization of Bernoulli-Nash social welfare [168], i.e., maximizing the joint probability that everyone receives ambulance on time.…”

“…By contrast, the fairness considered in optimization is to ensure the equity among the individuals (or groups) and there is no concern with the sensitive attributes. Therefore, fairness in optimization may lie in all aspects influencing the optimization process, such as the fair-sampling [145], [146], the design of fairness-aware objective [36], fairness between the preferences from DMs [150], [154], and fair-outcomes [158], [160], [177].…”

Utopia for Norwegian helicopter emergency medical services: Estimating the number of bases needed to radically bring down response times, and lives needed to be saved for cost effectiveness

“…Norway currently has 13 HEMS bases, established through historical local engagement from the late 1970s, with gradual expansion covering an increasing part of Norway [ 15 ]. Several studies have used mathematical models to study the optimal location of HEMS bases in Norway based on minor adjustments to the existing resources [ 3 , 5 , 15 ]. However, what it takes to bring response times in Norway down to times comparable to those on mainland Europe remains unexamined.…”