Department of Industrial Engineering, Yonsei University, D1010, 50, Yonsei-ro, Seodaemun-gu, Seoul, Republic of Korea.
J Healthc Eng. 2019 Dec 9;2019:6031789. doi: 10.1155/2019/6031789. eCollection 2019.
A two-tiered ambulance system, consisting of advanced and basic life support for emergency and nonemergency patient care, respectively, can provide a cost-efficient emergency medical service. However, such a system requires accurate classification of patient severity to avoid complications. Thus, this study considers a two-tiered ambulance dispatch and redeployment problem in which the average patient severity classification errors are known. This study builds on previous research into the ambulance dispatch and redeployment problem by additionally considering multiple types of patients and ambulances, and patient classification errors. We formulate this dynamic decision-making problem as a semi-Markov decision process and propose a mini-batch monotone-approximate dynamic programming (ADP) algorithm to solve the problem within a reasonable computation time. Computational experiments using realistic system dynamics based on historical data from Seoul reveal that the proposed approach and algorithm reduce the risk level index (RLI) for all patients by an average of 11.2% compared to the greedy policy. In this numerical study, we identify the influence of certain system parameters such as the percentage of advanced-life support units among all ambulances and patient classification errors. A key finding is that an increase in undertriage rates has a greater negative effect on patient RLI than an increase in overtriage rates. The proposed algorithm delivers an efficient two-tiered ambulance management strategy. Furthermore, our findings could provide useful guidelines for practitioners, enabling them to classify patient severity in order to minimize undertriage rates.
双层救护车系统，分别为紧急和非紧急患者护理提供高级和基本生命支持，可以提供具有成本效益的紧急医疗服务。然而，这样的系统需要准确地对患者严重程度进行分类，以避免并发症。因此，本研究考虑了一种双层救护车调度和重新部署问题，其中平均患者严重程度分类错误是已知的。本研究在之前的救护车调度和重新部署问题的研究基础上，进一步考虑了多种类型的患者和救护车，以及患者分类错误。我们将这个动态决策问题建模为一个半马尔可夫决策过程，并提出了一种小批量单调近似动态规划（ADP）算法，以便在合理的计算时间内解决这个问题。使用基于首尔历史数据的现实系统动态进行的计算实验表明，与贪婪策略相比，所提出的方法和算法平均将所有患者的风险水平指数（RLI）降低了 11.2%。在这个数值研究中，我们确定了某些系统参数的影响，例如所有救护车中高级生命支持单位的百分比和患者分类错误。一个关键发现是，分诊不足率的增加对患者 RLI 的负面影响大于分诊过度率的增加。所提出的算法提供了一种有效的双层救护车管理策略。此外，我们的研究结果可以为从业者提供有用的指导，帮助他们对患者的严重程度进行分类，以尽量减少分诊不足率。
