Comparing population and incident data for optimal air ambulance base locations in Norway

Røislien, Jo; Berg, Pieter L. van den; Lindner, Thomas; Zakariassen, Erik; Uleberg, Oddvar; Aardal, Karen; Essen, J. Theresia van

doi:10.1186/s13049-018-0511-4

Cited by 19 publications

(27 citation statements)

References 26 publications

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“…Discussion on the cost and benefits of HEMS demands accurate and comparable analysis methods. The proposed method, and the service areas defined by it, could also be used for planning new HEMS units or for developing HEMS systems; a previous study shows that using population density to define optimal base locations is not recommended, as the recommended location may not correspond to incident frequency [8].…”

Section: Discussionmentioning

confidence: 99%

Defining a mission-based method to determine a HEMS unit’s actual service area

Pappinen

Olkinuora²,

Laukkanen-Nevala³

2019

Scand J Trauma Resusc Emerg Med

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Background Geographical service areas are used as descriptive system indicators in Emergency Medical Service (EMS) related studies and reporting templates. The actual service area may differ significantly from administrative areas; this may lead to inaccuracy in determining indicator values, such as population or mission density, thus making it biased when comparing results between different areas and organizations. The aim of this study was to introduce a univocal, repeatable and easily adaptable method to determine the actual service area of a helicopter emergency medical service (HEMS) unit for statistical, quality measurement and research purposes using widely available geographical information (GIS) and statistical analysis tools. Methods The method was first tested with Tampere HEMS unit. All accepted missions in 2017 were extracted from FinnHEMS database (FHDB). We calculated distance from HEMS base to each accepted mission location. Missions were reordered based on the distance and 99th and 95th percentiles were calculated for mission distances. Convex hulls including 100, 99 and 95% of the missions, and the population and area covered by these missions, were then calculated. The method was repeated for all Finnish HEMS bases. Results Approximately 90% of Tampere HEMS unit’s accepted missions took place within 100 km from the base. 10.9% of the missions occurred outside of the administrative service area. 95% convex hull areas are most in line with the everyday experience of where the units actually operate. In Tampere, the 95% convex hull area corresponds to 76,5% of the administrative area’s population and to 89,8% of its area. Calculating the 95% convex hull areas for all Finnish HEMS units results in service areas that overlap at some points, and some areas of the country fall outside of all HEMS service areas. Conclusions Administrative areas do not correspond to the actual service areas of HEMS units. The service area of a HEMS unit defined by administrative boundaries may differ significantly from actual operations. Using historical mission data to create a convex hull that incorporates mission locations could offer a standardized and comparable solution for determining actual HEMS unit service areas, which can be used for statistical comparison, quality measurement and system development.

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Section: Discussionmentioning

confidence: 99%

Defining a mission-based method to determine a HEMS unit’s actual service area

Pappinen

Olkinuora²,

Laukkanen-Nevala³

2019

Scand J Trauma Resusc Emerg Med

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“…Population density and number of incidents are not necessarily highly correlated and using population data as a proxy for the expected number of incidents is not recommended [7]. In the analysis of optimal location of EMS bases, we use the number of ambulance assignments with the highest urgency level in the years from 2013 to 2016 as recorded by the Vestfold Hospital Trust as the expected incident intensity for each demand point.…”

Section: Methodsmentioning

confidence: 99%

“…The model can be used to find the location and necessary number of base stations in order to maximize the coverage given a specific response time target. The model has been used in a wide range of applications [5], such as establishing the optimal location of bases for emergency helicopters in Norway [6,7] and Australia [8] and medical drones in the United States [9,10]. While the MCLP model is suitable for finding locations of base stations, a more advanced model is needed in order to find the optimal number of ambulances at each base.…”

Section: Introductionmentioning

confidence: 99%

Improving ambulance coverage in a mixed urban-rural region in Norway using mathematical modeling

et al. 2019

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Background Ambulance services play a crucial role in providing pre-hospital emergency care. In order to ensure quick responses, the location of the bases, and the distribution of available ambulances among these bases, should be optimized. In mixed urban-rural areas, this optimization typically involves a trade-off between backup coverage in high-demand urban areas and single coverage in rural low-demand areas. The aim of this study was to find the optimal distribution of bases and ambulances in the Vestfold region of Norway in order to optimize ambulance coverage. Method The optimal location of bases and distribution of ambulances was estimated using the Maximum Expected Covering Location Model. A wide range of parameter settings were fitted, with the number of ambulances ranging from 1 to 15, and an average ambulance utilization of 0, 15, 35 and 50%, corresponding to the empirical numbers for night, afternoon and day, respectively. We performed the analysis both conditioned on the current base structure, and in a fully greenfield scenario. Results Four of the five current bases are located close to the mathematical optimum, with the exception of the northernmost base, in the rural part of the region. Moving this base, along with minor changes to the location of the four other bases, coverage can be increased from 93.46% to 97.51%. While the location of the bases is insensitive to the workload of the system, the distribution of the ambulances is not. The northernmost base should only be used if enough ambulances are available, and this required minimum number increases significantly with increasing system workload. Conclusion As the load of the system increases, focus of the model shifts from providing single coverage in low-demand areas to backup coverage in high-demand areas. The classification rule for urban and rural areas significantly affects results and must be evaluated accordingly.

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“…As a supplement to EMS, helicopter emergency medical services (HEMS) are expanding throughout the world, particularly in high-income countries [ 2 , 3 ]. The main purpose of HEMS is to provide advanced point-of-care diagnostic modalities, complex clinical decision-making, advanced interventions beyond the scope of most EMS, shorter transport times and access to locations outside the roadmap [ 4 , 5 ]. The service is resource-intensive and limited [ 2 ] so in order to optimize its utilization the location of HEMS bases is crucial.…”

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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Comparing population and incident data for optimal air ambulance base locations in Norway

Cited by 19 publications

References 26 publications

Defining a mission-based method to determine a HEMS unit’s actual service area

Defining a mission-based method to determine a HEMS unit’s actual service area

Improving ambulance coverage in a mixed urban-rural region in Norway using mathematical modeling

Introducing fairness in Norwegian air ambulance base location planning

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