Int J Health Geogr - Using genetic algorithms to optimise current and future health planning--the example of ambulance locations.


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CKGROUND: 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.

Resumo Limpo

ckground ambul respons time crucial factor patient surviv number emerg case em case requir ambul increas due chang popul demograph decreas ambul respons time emerg scene paper predict em case year interv correl current em case demograph factor level census area predict popul chang appli modifi group genet algorithm compar current futur optim locat number ambul set potenti locat evalu term current predict em case distanc locationsresult futur em demand predict increas use model r optim locat ambul base futur em case compar current locat optim locat model current em case data optimis locat ambul station locat reduc averag respons time second current predict futur em demand model locat calcul comparedconclus realloc ambul optim locat improv respons time contribut higher surviv rate lifethreaten medic event model em case demand census area allow data correl popul characterist optim suppli locat identifi compar current futur optim scenario allow nuanc plan decis made generic methodolog use provid evid support public health plan decis make

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