Using genetic algorithms to optimise current and future health planning--the example of ambulance locations.
Using genetic algorithms to optimise current and future health planning--the example of ambulance locations.
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DOI：
10.1186/1476-072x-9-4
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发表时间：
2010-01-28
影响因子：
4.9
通讯作者：
Brunsdon C
中科院分区：
文献类型：
--
作者：
Sasaki S;Comber AJ;Suzuki H;Brunsdon C
救护车响应时间是患者生存的关键因素。由于人口统计学的变化，需要救护车的紧急情况（EMS情况）的数量正在增加。这减少了救护车到紧急现场的反应时间。本文通过将目前的EMS病例与普查区水平的人口因素和预测的人口变化相关联，预测了从2020年到2050年的5年间隔内的EMS病例。然后，它适用于修改后的分组遗传算法比较当前和未来的最佳位置和救护车的数量。根据（当前和预测的）EMS病例到这些位置的距离对潜在位置集进行了评估。使用该模型预测，到2030年，未来的EMS需求将增加（R2 = 0.71）。将基于未来EMS病例的救护车最佳位置与当前位置以及基于当前EMS病例数据建模的最佳位置进行比较。优化救护站的位置使平均响应时间减少了57秒。计算和比较了模拟地点当前和预测的未来EMS需求。将救护车重新分配到最佳地点可以缩短反应时间，并有助于提高危及生命的医疗事件的存活率。EMS的情况下，“需求”的人口普查区建模允许数据与人口特征和最佳的“供应”的位置被确定。比较当前和未来的最佳方案可以做出更细致的规划决策。这是一种通用方法，可用于提供证据，支持公共卫生规划和决策。
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. 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. 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.