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Problem definition: Uncrewed aerial vehicles (UAVs) are transforming emergency service logistics applications across sectors, offering easy deployment and rapid response. In the context of emergency medical services (EMS), UAVs have the potential to augment ambulances by leveraging bystander assistance, thereby reducing response times for delivering urgent medical interventions and improving EMS outcomes. Notably, the use of UAVs for opioid overdose cases is particularly promising as it addresses the challenges faced by ambulances in delivering timely medication. This study aims to optimize the integration of UAVs and bystanders into EMS in order to minimize average response times for overdose interventions. Methodology/results: We formulate the joint operation of UAVs with ambulances through a Markov decision process that captures random emergency vehicle travel times and bystander availability. We apply an approximate dynamic programming approach to mitigate the solution challenges from high-dimensional state variables and complex decisions through a neural network-based approximation of the value functions (NN-API). To design the approximation, we construct a set of basis functions based on queueing and geographic properties of the UAV-augmented EMS system. Managerial implications: The simulation results suggest that our NN-API policy tends to outperform several noteworthy rule- and optimization-based benchmark policies in terms of accumulated rewards, particularly for situations that are primarily characterized by high request arrival rates and a limited number of available ambulances and UAVs. The results also demonstrate the benefits of incorporating UAVs into the EMS system and the effectiveness of an intelligent real-time operations strategy in addressing capacity shortages, which are often a problem in rural areas of the United States. Additionally, the results provide insights into specific contributions of each dispatching or redeployment strategy to overall performance improvement. Funding: This work was supported by the National Science [Grant 1761022]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0166 

The inconvenience of charging is one of the major concern for potential electric vehicle (EV) users. In addition to building more charging facilities, electric vehicle charging assistance service has emerged for making EV charging more convenient to customers. In this paper, we consider an optimal EV charging station location problem with two types of customers. One is ordinary self-charging customers whereas the other is customers using a new service mode called valet-charging. We formulate the problem via bi-level location optimization model, where the lower level problem is a game model that characterizes customers’ station choice behaviors. To solve the hard nonlinear mixed-integer optimization problem, we design an adaptive large neighbourhood search (ALNS) algorithm for the upper level problem and a construct-improve heuristic for the lower level problem. We conduct numerical experiments to justify the efficiency of our solution method. We also conduct a need-inspired case study to derive practical insights which will help EV charging assistant service providers make strategic decisions. The convenience of charging service is one major concern for EVs. In China, NIO Inc., NETA AUTO, and FAW-Volkswagen have started to provide valet-charging service. Charging station location problem becomes complicated while taking this service into account. We believe our work develops an effective tool for charging station planners to analyze station locations as well as the impact of valet charging services. 

Trauma presents a prominent health problem worldwide. However, trauma centers are often clustered in urban areas and sparsely located in rural areas. The geographic maldistribution of trauma centers leads to system-related mistriage errors. While some local governments oer subsidy to incentivize the affiliated hospital group to redesign the trauma care network, the approach is ad hoc. To address this issue, we propose a bilevel integer programming model to investigate the subsidized trauma care network redesign problem, which considers the government as the leader and the hospital group as the follower. To solve the resultant problem efficiently, we propose a branching idea to exclude additional infeasible solutions and suboptimal solutions, in turn speeding up the branch-and-bound algorithm. In a case study, we redesign a trauma care network in the midwestern area of the U.S. based on closed-form approximate functions of system-related mistriage errors. The results show that the optimal network redesign redistributes the network by slightly reducing the number of trauma centers to relieve the crowded trauma care resource, and achieves an overall improvement of about 11% over the original network. 

Abstract BackgroundOpioid overdose is the leading cause of injury-related death in the United States. Individuals who overdose outside of clinical settings have more positive clinical outcomes when they receive naloxone, an opioid antagonist, from a bystander as an early intervention before emergency personnel arrive. However, there is a gap in knowledge about successful instantaneous learning and intervention in a real-life stressful environment. The objective of this study is to explore the efficacy of different instructional delivery methods for bystanders in a stressful environment. We aim to evaluate which methods are most effective for instantaneous learning, successful intervention, and improved clinical outcomes. MethodsTo explore instantaneous learning in a stressful environment, we conducted a quantitative randomized controlled trial to measure how accurately individuals responded to memory-based survey questions guided by different instructional methods. Students from a large university in the Midwest (n = 157) were recruited in a public space on campus and accessed the six-question survey on their mobile devices. The intervention group competed the survey immediately while the research team created a distracting environment. The control group was asked to complete the survey later in a quiet environment. ResultsThe intervention group correctly answered 0.72 questions fewer than the control group (p = .000, CI [0.337, 1.103]). Questions Q1 and Q5 contained direct instructions with a verbal component and showed the greatest accuracy with over 90% correct for both stressful and controlled environments. ConclusionsThe variability in the responses suggests that there are environmental factors as well as instructional design features which influence instantaneous learning. The findings of this study begin to address the gap in knowledge about the effects of stress on instantaneous learning and the most effective types of instruction for untrained bystanders in emergency situations. 

Trauma injuries continue to be the leading cause of mortality and morbidity among US citizens aged 44 years and under. Government agencies are often in charge of designing an effective trauma network in their region to provide prompt and definitive care to their citizens. This process is, however, largely manual, experience-based and often leads to a suboptimal network in terms of patient safety. To support effective decision making, we propose a Nested Trauma Network Design Problem (NTNDP), which can be characterized as a nested multi-level, multi-customer, multi-transportation, multi-criteria, capacitated model with the bi-objective of maximizing the weighted sum of equity and effectiveness in patient safety. We use mistriages (system-related under- and over-triages) as surrogates for patient safety. To add realism, we include intermediate trauma centers that are set up in many states in the US to serve as feeder centers to major trauma centers to improve patient safety and three criteria to mimic EMS’s on-scene decisions. We propose a ‘3-phase’ solution approach that first solves a relaxed version of the model, then solves a Constraint Satisfaction Problem, and then a modified version of the original optimization problem (if needed), all using a commercial solver. Our findings suggest that solutions are sensitive to (i) the proportion of assignments attributed to various destination determination criteria, (ii) distribution of trauma patients, and (iii) relative emphasis on equity vs. effectiveness. We also illustrate the use of our approach using real data from a midwestern US state; results show over 30% performance improvement in the objective value. 

Healthcare capacity shortage contributes to poor access in many countries. Moreover, rapid urbanization often occurring in these countries has exacerbated the imbalance between healthcare capacity and need. One way to address the above challenge is expanding the total capacity and redistributing the capacity spatially. In this research, we studied the problem of locating new hospitals in a two-tier outpatient care system comprising multiple central and district hospitals, and upgrading existing district hospitals to central hospitals. We formulated the problem with a discrete location optimization model. To parameterize the optimization model, we used a multinomial logit model to characterize individual patients’ diverse hospital choice and to quantify the patient arrival rates at each hospital accordingly. To solve the hard nonlinear combinatorial optimization problem, we developed a queueing network model to approximate the impact of hospital locations on patient flows. We then proposed a multi-fidelity optimization approach, which involves both the aforementioned queuing network model as a surrogate and a self-developed stochastic simulation as the high-fidelity model. With a real-world case study of Shanghai, we demonstrated the changes in the care network and examined the impacts on the network design by population center emergence, governmental budget change and considering patients with different age groups or income levels. Note to Practitioners —Our work focuses on improving system-wide care access in a two-tier care network. We believe that our work can lead to effective development of a location analytics tool for city-wide healthcare system planners. We also think the importance of this study is further strengthened by the case studies based on real-world hospital choice experimental data from Shanghai, China, a region suffering from the imbalance between healthcare capacity and need. Our case studies are expected to make recommendations on care facility expansion and dispersion to better align with the spatial distribution of residential communities and patient hospital choice behavior. 

Nursing homes (NHs) are critical facilities for caring frail older adults with around-the-clock formal care and personal assistance. To ensure quality of care for NH residents, an adequate staffing level is of great importance. Current NH staffing practice is mainly based on experience and regulation. The objective of this paper is to investigate the viability of experience-based and regulation-based strategies, as well as alternative staffing strategies to meet the heterogeneous service demand of NH residents at reduced labor cost under various scenarios of census compositions. We propose a predictive analytics integrated computer simulation model to characterize the heterogeneous service demand of NH residents, and further evaluate and identify promising staffing strategies at the facility level. Specifically, we propose a predictive model based on latent survival analysis to characterize diverse length-of-stay (LOS) with multiple discharge dispositions among NH residents. Further, we develop a simulation model with the incorporation of predictive analytics and domain knowledge to characterize the heterogeneous service demand of NH residents on different types of caregivers over time. Based on the simulation model, we develop a graphical user interface for the simulator to evaluate different staffing strategies at the facility level and inform NH administrators about promising strategies. We use real NH data to validate the proposed model and demonstrate its effectiveness. The proposed predictive LOS model considering multiple discharge dispositions exhibits superior prediction performance and offers better staffing decisions at reduced costs than those without the consideration. With the improved modeling fidelity via integrating predictive analytics with computer simulation, the proposed model is flexible to evaluate various staffing strategies using total labor cost as a performance metric, and can identify promising staffing strategies to meet the service demand of NH residents. Promising staffing strategies with the suggested staff-to-resident (SR) ratio can significantly reduce the total labor cost of multiple types of caregivers, as compared to the benchmark strategies, such as the SR ratios based on industrial practice or minimum requirement of state regulation. Moreover, we construct multiple scenarios of different census compositions of NH residents to demonstrate the capability of the proposed model. Our proposed model can facilitate NH staffing decision making to meet the heterogeneous service demand of NH residents at reduced labor costs.