Supporting Fair and Efficient Emergency Medical Services in a Large Heterogeneous Region

Emergency Medical Services (EMS) are crucial in delivering timely and effective medical care to patients in need. However, the complex and dynamic nature of operations poses challenges for decision-making processes at strategic, tactical, and operational levels. This paper proposes an action-driven strategy for EMS management, employing a multi-objective optimizer and a simulator to evaluate potential outcomes of decisions. The approach combines historical data with dynamic simulations and multi-objective optimization techniques to inform decision-makers and improve the overall performance of the system. The research focuses on the Friuli Venezia Giulia region in north-eastern Italy. The region encompasses various landscapes and demographic situations that challenge fairness and equity in service access. Similar challenges are faced in other regions with comparable characteristics. The Decision Support System developed in this work accurately models the real-world system and provides valuable feedback and suggestions to EMS professionals, enabling them to make informed decisions and enhance the efficiency and fairness of the system

Ambulance Emergency Response Optimization in Developing Countries

The lack of emergency medical transportation is viewed as the main barrier to the access of emergency medical care in low and middle-income countries (LMICs). In this paper, we present a robust optimization approach to optimize both the location and routing of emergency response vehicles, accounting for uncertainty in travel times and spatial demand characteristic of LMICs. We traveled to Dhaka, Bangladesh, the sixth largest and third most densely populated city in the world, to conduct field research resulting in the collection of two unique datasets that inform our approach. This data is leveraged to develop machine learning methodologies to estimate demand for emergency medical services in a LMIC setting and to predict the travel time between any two locations in the road network for different times of day and days of the week. We combine our robust optimization and machine learning frameworks with real data to provide an in-depth investigation into three policy-related questions. First, we demonstrate that outpost locations optimized for weekday rush hour lead to good performance for all times of day and days of the week. Second, we find that significant improvements in emergency response times can be achieved by re-locating a small number of outposts and that the performance of the current system could be replicated using only 30% of the resources. Lastly, we show that a fleet of small motorcycle-based ambulances has the potential to significantly outperform traditional ambulance vans. In particular, they are able to capture three times more demand while reducing the median response time by 42% due to increased routing flexibility offered by nimble vehicles on a larger road network. Our results provide practical insights for emergency response optimization that can be leveraged by hospital-based and private ambulance providers in Dhaka and other urban centers in LMICs

Data driven approaches to improve operational efficiency of emergency medical services

We study data-driven approaches to maximize the service level of Emergency Medical Services (EMS) in emerging economies. These systems usually operate under heavy resource constraints and face significant operational challenges, making them structurally and operationally different from systems in developed countries. In this thesis we study two specific issues - (i) modeling human behavior, and (ii) accounting for risk metrics due to tail behavior. First, we address the issue of ambulance abandonment that occurs when a patient's willingness to wait is less than the ambulance response time resulting in the vehicle not being utilized. We present a maximum likelihood estimation approach to estimate willingness to wait for different types of patients. We then use the estimate of waiting times in a greedy simulation based optimization model to redesign the EMS network to maximize the number of patients served within their waiting time thresholds. Computational experiments using data from an Indian metropolitan city show that our proposed resource allocation model reduces abandonment by approximately 2 percentage points with the current ambulance fleet size, 5 percentage points by doubling the fleet size and 6 percentage points by tripling the fleet size. Next, we present a risk-based optimization approach to make the EMS network robust to unexpected changes in demand patterns. This is motivated by the fact that when few parts of the network face heavy-tailed demand patterns, the demand for entire network under the resource constrained setting behaves in a heavy-tailed manner. To achieve a robust location strategy we include risk metrics, specifically the Conditional Value at Risk, that focus on tail behavior in addition to average case performance metrics. Computational experiments show that planning with a view of minimizing risk leads to solutions that perform well in heavy-tailed settings

Modelling Emergency Medical Services

Emergency Medical Services (EMS) play a pivotal role in any healthcare organisation. Response and turnaround time targets are always of great concern for the Welsh Ambulance NHS Trust (WAST). In particular, the more rural areas in South East Wales consistently perform poorly with respect to Government set response standards, whilst delayed transfer of care to Emergency Departments (EDs) is a problem publicised extensively in recent years. Many Trusts, including WAST, are additionally moving towards clinical outcome based performance measures, allowing an alternative system-evaluation approach to the traditional response threshold led strategies, resulting in a more patient centred system. Three main investigative parts form this thesis, culminating in a suite of operational and strategic decision support tools to aid EMS managers. Firstly, four novel allocation model methods are developed to provide vehicle allocations to existing stations whilst maximising patient survival. A detailed simulation model then evaluates clinical outcomes given a survival based (compared to response target based) allocation, determining also the impact of the fleet, its location and a variety of system changes of interest to WAST (through ‘what-if?’ style experimentation) on entire system performance. Additionally, a developed travel time matrix generator tool, enabling the calculation and/or prediction of journey times between all pairs of locations from route distances is utilised within the aforementioned models. The conclusions of the experimentation and investigative processes suggest system improvements can in fact come from better allocating vehicles across the region, by reducing turnaround times at hospital facilities and, in application to South East Wales, through alternative operational policies without the need to increase resources. As an example, a comparable degree of improvement in patient survival is witnessed for a simulation scenario where the fleet capacity is increased by 10% in contrast to a scenario in which ideal turnaround times (within the target) occur

Queue analysis of public healthcare system to reduce waiting time using flexsim 6.0

Public healthcare is a health service facility from the government at a low cost. The problem is the long queue, which makes long patients’ waiting times. The patients are waiting for a maximum of more than 3 hours in the general polyclinic. Besides, the registration counter is almost busy all the time. The utilization is about 96.96%. Therefore, the objective of this research is to reduce the patients’ waiting time using the simulation method. Flexsim 6.0 software is employed to develop the public healthcare system and also develop some alternatives to improve the problem. The simulation model has been verified and validated. The result shows the waiting time is decreased by more than 80% by adding the resource in the registration counter. For managerial insight, this research could help the public healthcare system in satisfying the patients