Exploring optimal air ambulance base locations in Norway using advanced mathematical modelling

Journal article (2016)

Authors

Jo Røislien Norwegian Air Ambulance Foundation, University of Stavanger, Universitetet i Oslo
P.L. van den Berg Discrete Mathematics and Optimization - EEMCS
Thomas Lindner Norwegian Air Ambulance Foundation, SAFER (Stavanger Acute Medicine Foundation for Education and Research) and Stavanger University Hospital
Erik Zakariassen University of Bergen, Norwegian Air Ambulance Foundation
K.I. Aardal Discrete Mathematics and Optimization - EEMCS , Centrum Wiskunde & Informatica (CWI)
J.T. van Essen Discrete Mathematics and Optimization - EEMCS , Centrum Wiskunde & Informatica (CWI)
Research Group
Discrete Mathematics and Optimization (EEMCS) (TU Delft)
Copyright: © 2016 Jo Røislien, P.L. van den Berg, Thomas Lindner, Erik Zakariassen, K.I. Aardal, J.T. van Essen
DOI: https://doi.org/10.1136/injuryprev-2016-041973
More Info
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Published Date

2016

Language

English

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Delft Institute of Applied Mathematics

Research Group

Discrete Mathematics and Optimization

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.