Improving fairness in ambulance planning by time sharing

Jagtenberg, Caroline; Mason, A. James

doi:10.1016/j.ejor.2019.08.003

Cited by 26 publications

(12 citation statements)

References 41 publications

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“…A review of methodologies and algorithms for ambulance locating problem in a coverage notion is summarized by Farahani et al (2012). The coverage can be defined as response time (Erdemir et al, 2010), total service time (Jagtenberg and Mason, 2020), and the expected survival possibility (Knight et al, 2012). The selected locations ensure that the estimated demand can be satisfied within a given time.…”

Section: Ambulance Locating Problemmentioning

confidence: 99%

Emergency Vehicle Routing in Urban Road Networks with Multistakeholder Cooperation

Zeng

Wang

et al. 2021

J. Transp. Eng., Part A: Systems

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The lack of multi-stakeholder cooperation is one of the main challenges faced by emergency medical services (EMS). Especially in the ambulance routing process, inactive traffic operators fail to provide coordination to prioritize the ambulance, while ignoring the choice of hospitals will lead to inevitable patient transfer between hospitals. To provide efficient decision support for EMS, this paper considers daily ambulance routing problems in a network with high spatial resolution in which two advanced technologies are introduced: pre-hospital screening that provides patient injury diagnosis and lane pre-clearing that ensures the pre-defined driving speed of ambulances. Three different types of ambulances are used to transport and offer first aids to patients based on the screened results. To manage the ambulance fleet properly, a mixedinteger linear programming (MIP) model is proposed to assign vehicles to the injured and plan routes with the shortest travel time. A semi-soft time window constraint is incorporated to reflect the late arrival penalty on-site and at hospitals. Since high-quality EMS responds to the call in seconds, a real-world case in Shenzhen, China, is presented to validate the computational performance by a commercial solver GAMS. In the case study, we further analyzed the effect of different stakeholders' involvement, like the hospitals and traffic operators. This information proves the efficiency of multi-stakeholder participation in ambulance routing.

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Section: Ambulance Locating Problemmentioning

confidence: 99%

Emergency Vehicle Routing in Urban Road Networks with Multistakeholder Cooperation

Zeng

Wang

et al. 2021

J. Transp. Eng., Part A: Systems

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“…While maximizing efficiency seems reasonable, such mathematical models will inadvertently put more focus on people in densely populated areas. By deciding to strive for efficiency one therefore implicitly affects who gets coverage and who does not [ 13 ]. Indeed, a less efficient solution might be preferable if it offers a more equitable service.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, a less efficient solution might be preferable if it offers a more equitable service. EMS providers around the world recognize fairness as a relevant factor [ 13 ] and in Norway in particular, where despite substantial differences in geography and population density equality in healthcare is a spoken aim.…”

Section: Introductionmentioning

confidence: 99%

Introducing fairness in Norwegian air ambulance base location planning

Jagtenberg

Vollebergh

Uleberg

et al. 2021

Scand J Trauma Resusc Emerg Med

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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’.

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“…Asimismo, en otros casos se extendió el MEXCLP, donde se minimizaba el costo de satisfacer la demanda, mientras se maximizaba la cobertura [21]. Por último, hubo un trabajo que definió un modelo no lineal que maximizaba el bienestar social [22].…”

Section: Introductionunclassified