Locating trauma centers considering patient safety
Locating trauma centers considering patient safety
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DOI：
10.1007/s10729-021-09576-y
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发表时间：
2022-01
影响因子：
3.6
通讯作者：
Sagar Hirpara;M. Vaishnav;Pratik J. Parikh;Nan Kong;Priti Parikh
中科院分区：
文献类型：
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作者：
Sagar Hirpara;M. Vaishnav;Pratik J. Parikh;Nan Kong;Priti Parikh
创伤仍然是美国44岁以下人群死亡和残疾的主要原因，使其成为一个突出的公共卫生问题。最近的文献表明，创伤中心的地理分布不均，以及由此导致的到达最近的创伤中心的时间增加，可能会影响患者的安全，增加残疾或死亡率。为了解决这个问题，我们引入了创伤中心选址问题(TCLP)，以确定创伤中心的最佳数量和位置，以提高患者的安全性。我们通过错误的替代措施来模拟患者的安全，这指的是创伤患者和目的地医院的伤害严重程度不匹配。我们提出的双目标优化模型直接考虑了两种类型的失误，系统相关的不足分类(SrUT)和过度分类(SrOT)，这两种错误都是使用概念性任务算法估计的。我们提出了一种基于粒子群优化框架的启发式算法，以有效地获得满足实际问题规模的TCLP的近似最优解。基于俄亥俄州2012年的数据，我们观察到解决方案对srUT和srOT权重的选择、TC的容量要求以及用于模拟EMS决策的两个阈值非常敏感。使用我们的方法来优化该网络，在仅增加1个TC的情况下，目标减少了31.5%以上；重新分配现有的21个TC导致了30.4%的减少。
Trauma continues to be the leading cause of death and disability in the U.S. for those under the age of 44, making it a prominent public health problem. Recent literature suggests that geographical maldistribution of Trauma Centers (TCs), and the resultant increase of the access time to the nearest TC, could impact patient safety and increase disability or mortality. To address this issue, we introduce the Trauma Center Location Problem (TCLP) that determines the optimal number and location of TCs in order to improve patient safety. We model patient safety through a surrogate measure of mistriages, which refers to a mismatch in the injury severity of a trauma patient and the destination hospital. Our proposed bi-objective optimization model directly accounts for the two types of mistriages, system-related under-triage (srUT) and over-triage (srOT), both of which are estimated using a notional tasking algorithm. We propose a heuristic based on the Particle Swarm Optimization framework to efficiently derive a near-optimal solution to the TCLP for realistic problem sizes. Based on 2012 data from the state of Ohio, we observe that the solutions are sensitive to the choice of weights for srUT and srOT, volume requirements at a TC, and the two thresholds used to mimic EMS decisions. Using our approach to optimize that network resulted in over 31.5% reduction in the objective with only 1 additional TC; redistribution of the existing 21 TCs led to 30.4% reduction.