Abstract
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.
Highlights
Ambulance operating methods are highly important for the emergency medical service (EMS) system as they directly affect the patient survival rate and medical service quality
We propose an approximate dynamic programming (ADP) model that runs on a discrete event simulation to optimize the dispatch-and-redeployment policy of a two-tiered ambulance system by considering errors in patient-severity classification. e computational experiment environment was created based on actual historical data from Seoul by considering the probability distribution of demand-and-service time, time-varying demand, and traffic speed. e computational experiments show that our proposed algorithm performs better than the greedy policy
Ese data reveal an average of 127.9 calls per day, of which 24.8% are assumed to be high risk [32] e area contains three hospitals with emergency rooms, six ambulances, and six fire stations that function as waiting locations (Figure 5)
Summary
Ambulance operating methods are highly important for the emergency medical service (EMS) system as they directly affect the patient survival rate and medical service quality. E goal of ambulance operations is to provide patients with appropriate emergency treatment within a short time period and transport the patient to the hospital for specific advanced treatment. Erefore, real-time decision-making is required, which must consider system dynamics such as time-varying demands (emergency calls), time-varying traffic, and the different first-aid times required by patients. Another important consideration in ambulance operations is the different severity of the transported patients.
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