A two-tiered ambulance system, consisting of advanced and basic life support for emergency and nonemergency patient care,\nrespectively, can provide a cost-efficient emergency medical service. However, such a system requires accurate classification of\npatient severity to avoid complications. Thus, this study considers a two-tiered ambulance dispatch and redeployment problem in\nwhich the average patient severity classification errors are known. This study builds on previous research into the ambulance\ndispatch and redeployment problem by additionally considering multiple types of patients and ambulances, and patient classification\nerrors. We formulate this dynamic decision-making problem as a semi-Markov decision process and propose a minibatch\nmonotone-approximate dynamic programming (ADP) algorithm to solve the problem within a reasonable computation\ntime. Computational experiments using realistic system dynamics based on historical data from Seoul reveal that the proposed\napproach and algorithm reduce the risk level index (RLI) for all patients by an average of 11.2% compared to the greedy policy. In\nthis numerical study, we identify the influence of certain system parameters such as the percentage of advanced-life support units\namong all ambulances and patient classification errors. A key finding is that an increase in undertriage rates has a greater negative\neffect on patient RLI than an increase in overtriage rates. The proposed algorithm delivers an efficient two-tiered ambulance\nmanagement strategy. Furthermore, our findings could provide useful guidelines for practitioners, enabling them to classify\npatient severity in order to minimize undertriage rates.
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