Exploring optimal air ambulance base locations in Norway using advanced mathematical modelling
Jo Røislien (Norwegian Air Ambulance Foundation, University of Stavanger, Universitetet i Oslo)
Pieter van den Berg (TU Delft - Discrete Mathematics and Optimization)
Thomas Lindner (SAFER (Stavanger Acute Medicine Foundation for Education and Research) and Stavanger University Hospital , Norwegian Air Ambulance Foundation)
Erik Zakariassen (Norwegian Air Ambulance Foundation, University of Bergen)
Karen Aardal (Centrum Wiskunde & Informatica (CWI), TU Delft - Discrete Mathematics and Optimization)
Theresia van Essen (TU Delft - Discrete Mathematics and Optimization, Centrum Wiskunde & Informatica (CWI))
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Abstract
Background
Helicopter emergency medical services are an important part of many healthcare systems. Norway has a nationwide physician staffed air ambulance service with 12 bases servicing a country with large geographical variations in population density. The aim of the study was to estimate optimal air ambulance base locations.
Methods
We used high resolution population data for Norway from 2015, dividing Norway into >300 000 1 km×1 km cells. Inhabited cells had a median (5–95 percentile) of 13 (1–391) inhabitants. Optimal helicopter base locations were estimated using the maximal covering location problem facility location optimisation model, exploring the number of bases needed to cover various fractions of the population for time thresholds 30 and 45 min, both in green field scenarios and conditioning on the current base structure. We reanalysed on municipality level data to explore the potential information loss using coarser population data.
Results
For a 45 min threshold, 90% of the population could be covered using four bases, and 100% using nine bases. Given the existing bases, the calculations imply the need for two more bases to achieve full coverage. Decreasing the threshold to 30 min approximately doubles the number of bases needed. Results using municipality level data were remarkably similar to those using fine grid information.
Conclusions
The whole population could be reached in 45 min or less using nine optimally placed bases. The current base structure could be improved by moving or adding one or two select bases. Municipality level data appears sufficient for proper analysis.
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