Karl Landsteiner University of Health Sciences, University Hospital Krems, Dept. of Anesthesia and Intensive Care Medicine, Mitterweg 10, A-3500 Krems, Austria.
Austrian Institute of Technology, Center for Mobility Systems, Giefinggasse 2, A-1210 Vienna, Austria.
Int J Med Inform. 2018 Mar;111:24-36. doi: 10.1016/j.ijmedinf.2017.12.008. Epub 2017 Dec 14.
Emergency medical services have been established in many countries all over the world. Good first care improves the outcome of patients in terms of hospital stay duration, chances of full recovery and of treatment costs. In this paper, we present an integrated approach combining spatial information and integer optimization for emergency medical service location planning. The research is motivated by a recent call for bids to restructure the location of emergency medical services in the Austrian federal state of Lower Austria by the local state government.
Our framework allows for constraints on the places where an emergency care physician is stationed, accounting for the fact that - for economical reasons - it might not be feasible to arbitrarily place emergency care physicians. We use maximum coverage linear programs to get accurate solutions for the problem instances (depending on the maximum allowed number of emergency care physicians and the constraints of their placement). We optimize for the maximum number of covered residents given certain parameters. The travelling distances are calculated by means of a digital road graph. Moreover we analyze the coverage of the day population as there are significant shifts in the number of persons present at daytime. For every problem instance we have calculated the ten best solutions and examined the variance among them. For the demand point aggregation we have used a cell grid.
Using our method we can show that with less emergency care physicians more residents can be covered. This is highly applicable to low populated areas where the coverage becomes better. There is little variance from the best to the second best solution: There are only small changes (usually only one cell is shifted) between the best and the second best solution. The coverage of the day population - except for a few problem instances - is always better than the coverage of the residents (reflecting the fact that many residents commute to more densely populated areas).
In our study, we show that our solutions provide better coverage of residents with fewer emergency care physicians than the current status quo.
世界上许多国家都建立了紧急医疗服务体系。良好的初步治疗可以改善患者的住院时间、完全康复的机会和治疗成本。在本文中，我们提出了一种结合空间信息和整数优化的综合方法，用于紧急医疗服务地点规划。这项研究的动机是最近奥地利下奥地利州地方政府为重组紧急医疗服务地点而发布的招标。
我们的框架允许对驻站急救医生的地点进行限制，考虑到出于经济原因，任意安置急救医生可能不可行。我们使用最大覆盖线性规划来为问题实例获得准确的解决方案（取决于最大允许的急救医生数量和他们的安置限制）。我们在给定某些参数的情况下，针对覆盖的最大居民数量进行优化。旅行距离通过数字道路图计算。此外，我们还分析了日间人口的覆盖情况，因为日间的人数存在明显的变化。对于每个问题实例，我们都计算了前十个最佳解决方案，并对它们之间的差异进行了检查。对于需求点聚合，我们使用了一个单元格网格。
使用我们的方法，我们可以证明，使用更少的急救医生可以覆盖更多的居民。这在人口较少的地区非常适用，因为覆盖范围会更好。从最佳到第二最佳解决方案的差异很小：最佳和第二最佳解决方案之间只有很小的变化（通常只有一个单元格发生了变化）。日间人口的覆盖范围——除了少数几个问题实例之外——总是优于居民的覆盖范围（反映了许多居民通勤到人口更密集地区的事实）。
在我们的研究中，我们表明，与当前的现状相比，我们的解决方案可以用更少的急救医生提供更好的居民覆盖范围。
