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. 2010 Jan 28:9:4.
doi: 10.1186/1476-072X-9-4.

Using genetic algorithms to optimise current and future health planning--the example of ambulance locations

Satoshi Sasaki  1 , Alexis J Comber, Hiroshi Suzuki, Chris Brunsdon
Affiliations

Using genetic algorithms to optimise current and future health planning--the example of ambulance locations

Satoshi Sasaki et al. Int J Health Geogr. .

Abstract

Background: Ambulance response time is a crucial factor in patient survival. The number of emergency cases (EMS cases) requiring an ambulance is increasing due to changes in population demographics. This is decreasing ambulance response times to the emergency scene. This paper predicts EMS cases for 5-year intervals from 2020, to 2050 by correlating current EMS cases with demographic factors at the level of the census area and predicted population changes. It then applies a modified grouping genetic algorithm to compare current and future optimal locations and numbers of ambulances. Sets of potential locations were evaluated in terms of the (current and predicted) EMS case distances to those locations.

Results: Future EMS demands were predicted to increase by 2030 using the model (R2 = 0.71). The optimal locations of ambulances based on future EMS cases were compared with current locations and with optimal locations modelled on current EMS case data. Optimising the location of ambulance stations locations reduced the average response times by 57 seconds. Current and predicted future EMS demand at modelled locations were calculated and compared.

Conclusions: The reallocation of ambulances to optimal locations improved response times and could contribute to higher survival rates from life-threatening medical events. Modelling EMS case 'demand' over census areas allows the data to be correlated to population characteristics and optimal 'supply' locations to be identified. Comparing current and future optimal scenarios allows more nuanced planning decisions to be made. This is a generic methodology that could be used to provide evidence in support of public health planning and decision making.

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Figures

Figure 1
Figure 1
Estimated and future demands of prehospital medical emergency services from 2010 to 2040. Note: Emergency cases in each census small area (y) were projected with the no of population of 0-4 years (x1), the no of population of 15-64 years (x2), no of population of 80 and over (x3) and the no of companies with more than five employees (x4), based on the predicted model: y = 0.006*x1+0.021*x2+0.102*x3+0.433*x4-0.268.
Figure 2
Figure 2
Emergency cases and current and optimal ambulance locations with their catchment areas. a) Distribution of emergency cases and 35 fire stations (potential ambulance locations). b) Current location of 27 ambulances. c) The optimal location of 27 ambulance stations based on emergency cases in 2007. d) The optimal location of 27 ambulance stations based on predicted emergency cases for 2030. The following legend applies to figures 3c) and 3d): solid circle - optimal locations that are the same as current locations; ringed circle - new locations; hollow circle with a cross - current sites not selected during optimisation.
Figure 3
Figure 3
A comparison of the optimised location for n = 25 (left) and n = 30 (right) ambulances. These are evaluated against current emergency cases in 2007 (top) and predicted emergency cases in 2030 (bottom). The size of the point indicates the 'demand' in terms of emergency case distances.
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