Ambulance Allocation Research Articles

Managing EMS systems with user abandonment in emerging economies

In many emerging economies, callers may abandon ambulance requests due to a combination of operational (small fleet size), infrastructural (long travel times) and behavioral factors (low trust in the ambulance system). As a result, ambulance capacity, which is already scarce, is wasted in serving calls that are likely to be abandoned later. In this article, we investigate the design of an ambulance system in the presence of abandonment behavior, using a two-step approach. First, because the callers’ actual willingness to wait for ambulances is censored, we adopt a Maximum Likelihood Estimator estimation approach suitable for interval censored data. Second, we employ a simulation-based optimization approach to explicitly incorporate customers’ willingness to wait in: (i) tactical short-term decisions such as modification of dispatch policies and ambulance allocations at existing base locations; and (ii) strategic long-term network design decisions of increasing fleet size and re-designing base locations. We calibrate our models using data from a major metropolitan city in India where historically 81.3% of calls were successfully served without being abandoned. We find that modifying dispatch policies or reallocating ambulances provide relatively small gains in successfully served calls (around 1%). By contrast, increasing fleet size and network re-design can more significantly increase the fraction of successfully served calls with the latter being particularly more effective. Redesigning bases with the current fleet size is equivalent to increasing the fleet size by 8.6% at current base locations. Similarly, adding 29% more ambulances and redesigning the base locations is equivalent to doubling the fleet size at the current base locations and adding 34% more ambulances and redesigning base locations is equivalent to a three-fold increase. Our results indicate that in the absence of changes in behavioral factors, significant investment is required to modify operational factors by increasing fleet size, and to modify infrastructural factors by redesigning base locations.

Effective plans for hospital system response to earthquake emergencies

Hospital systems play a critical role in treating injuries during disaster emergency responses. Simultaneously, natural disasters hinder their ability to operate at full capacity. Thus, cities must develop strategies that enable hospitals’ effective disaster operations. Here, we present a methodology to evaluate emergency response based on a model that assesses the loss of hospital functions and quantifies multiseverity injuries as a result of earthquake damage. The proposed methodology can design effective plans for patient transfers and allocation of ambulances and mobile operating rooms. This methodology is applied to Lima, Peru, subjected to a disaster scenario following a magnitude 8.0 earthquake. Our results show that the spatial distribution of healthcare demands mismatches the post-earthquake capacities of hospitals, leaving large zones on the periphery significantly underserved. This study demonstrates how plans that leverage hospital-system coordination can address this demand-capacity mismatch, reducing waiting times of critically injured patients by factors larger than two.

A multi-period ambulance location and allocation problem in the disaster

To achieve quick response in the disaster, this paper addresses the issue of ambulance location and allocation, as well as the location problem of temporary medical centers. Considering budget and capacity limitations, a multi-period mixed integer programming model is proposed and two hybrid heuristic algorithms are designed to solve this complex problem. The proposed model and algorithm are further verified in a real case study, and the numerical experiments demonstrate the effectiveness of our proposed model. Specifically, we obtain several findings based on the computational results: (1) The best locations of ambulance stations should change in each period because the demand rate changes over time. (2) Involving temporary medical centers is necessary to reduce the average waiting time of injured people. (3) It may not be optimal to allocate ambulances from the nearest ambulance stations because of potentially limited station capacity.

Leveraging Machine Learning Techniques and Engineering of Multi-Nature Features for National Daily Regional Ambulance Demand Prediction.

The accurate prediction of ambulance demand provides great value to emergency service providers and people living within a city. It supports the rational and dynamic allocation of ambulances and hospital staffing, and ensures patients have timely access to such resources. However, this task has been challenging due to complex multi-nature dependencies and nonlinear dynamics within ambulance demand, such as spatial characteristics involving the region of the city at which the demand is estimated, short and long-term historical demands, as well as the demographics of a region. Machine learning techniques are thus useful to quantify these characteristics of ambulance demand. However, there is generally a lack of studies that use machine learning tools for a comprehensive modeling of the important demand dependencies to predict ambulance demands. In this paper, an original and novel approach that leverages machine learning tools and extraction of features based on the multi-nature insights of ambulance demands is proposed. We experimentally evaluate the performance of next-day demand prediction across several state-of-the-art machine learning techniques and ambulance demand prediction methods, using real-world ambulatory and demographical datasets obtained from Singapore. We also provide an analysis of this ambulatory dataset and demonstrate the accuracy in modeling dependencies of different natures using various machine learning techniques.

Robust ambulance base allocation strategy with social media and traffic congestion information

At present, traffic congestion has become a major problem in many metropolitan areas globally, affecting the economy and social conditions in these areas. Of special concern, the response time of ambulances has increased, causing the death or disability of patients during emergencies. Various ambulance allocation strategies have been developed to locate a base that can achieve coverage of the demand point within a prescribed time or distance frame. However, real-time ambulance deployment is required to determine the number of ambulance and their bases. In particular, the dynamic relocation of an ambulance base is complicated, and each relocation does not guarantee that the next period will change again, thus increasing the workloads of the ambulance crew and potentially reducing their capability of responding to an emergency call. Furthermore, these models only considered covering all demand points but lacked the ability to consider uncertain factors, such as traffic congestion, patient conditions, public events, and population movement. Therefore, this study focused on formulating a covering model based on traffic congestion from web-based services and social media analysis, using the Markov-chain traffic speed assignment to allocate ambulance bases and trade-off the number of ambulance facilities between the current period and next period while considering the number of ambulance vehicles. Moreover, the proposed model was demonstrated using a case study of Bangkok emergency medical services. According to the results, obtained through collecting data on social media and traffic speed, the average traveling time of an ambulance can be improved by more than 70% with the trade-off between different periods if several emergency calls are received.

Risk-averse flexible policy on ambulance allocation in humanitarian operations under uncertainty

Proactive ambulance management is constructive to improve the response efficiency for emergency medical service (EMS) systems under uncertainty. In this paper, we present a dynamic optimisation model concerning the ambulance dispatching and relocation. We develop a flexible operation policy driven by the interval rolling to match vehicles with calls in batch. We formulate the problem in Markov Decision Process and incorporate queues to minimise the average response and delay time. Considering the curse-of-dimensionality, we provide a simulation-based empirical dynamic programming with the state aggregation and post-decision state to solve the model. To further accelerate the computational efficiency, a greedy heuristic method is introduced to improve the quality of sampling operations. Then, a risk-averse model is developed based on the stochastic dominance strategy to improve operational reliability. We develop an equivalent linear programming to evaluate concave dominating functions. We test the performance by a numerical case and extract managerial insights for practitioners. Our results show that the proposed flexible and risk-averse solution outperforms the classic model on reducing the delay under uncertain calls. And the improvement is more active during peak hours, when real-time needs exceed available ambulances.

Abstract 35: Dispatcher Impression of Stroke: Concordance With Paramedic Identification and Effect on Allocation of Standard Ambulances and Mobile Stroke Units

Background: EMS 911 dispatchers must rapidly assess patient calls and allocate appropriate responding resources including, for stroke, advanced (rather than basic) life support standard ambulances and, in an increasing number of regions, specialty Mobile Stroke Units. While dispatcher accuracy in distinguishing stroke from other high risk conditions is not crucial for selecting ALS rather than BLS response, specialized MSU response necessitates a higher level of accuracy. Methods: We analyzed all transports between January 2018-August 2019 identified as stroke by dispatchers at sendout or by paramedics upon patient assessment in a large urban EMS system -LA County FD EMS, serving 4.1 million lives, 59 cities, 3,000 square miles, via 174 fire stations. Dispatchers are instructed to decide upon the appropriate diagnosis and responding resource within 60s of start of 911 call conversation. “Dispatcher Impression” (DI) and on scene paramedic “Provider Impression” (PI) were compared, and other care metrics analyzed. Results: In total, 12,528 runs were evaluated, of which 79.1% (9913) were dispatched as stroke and paramedics identified stroke in 47.6%. The sensitivity of dispatcher impression of stroke was 56.2% and the positive predictive value was 33.9%. The most common paramedic impressions in non-concordant patients dispatched as stroke were: generalized weakness in 24% (1587), altered mental status in 17% (1111), and syncope in 6% (403). In patients with a dispatcher impression of non-stroke but paramedic impression of stroke, the most common dispatcher impressions were: sick person in 29% (758), unconscious in 28.7% (750), and seizure in 4.6% (121). In dispatcher impressions of stroke, 100% were dispatched at ALS level; and in dispatcher impressions of non-stroke but eventual paramedic impression of stroke, 98.3% were dispatched at ALS level. Patient age over/under 40 & patient sex did not modify rates of dispatcher - paramedic impression concordance. Conclusions: Dispatchers identify more than half of paramedic-identified stroke patients and allocate appropriate ALS standard ambulances in almost all stroke patients. However, for efficient attaching of specialized MSU resources, improved dispatcher identification of stroke is desirable.

Simulation modeling and optimization for ambulance allocation considering spatiotemporal stochastic demand

To ensure a timely response to emergencies, governments are obliged to implement effective ambulance allocation plans. In practice, an emergency medical service (EMS) system works in an uncertain environment, with stochastic demand, response-times, and travel-times. This uncertainty significantly affects ambulance allocation planning. However, few studies in this field adequately consider the effect of spatiotemporal uncertainty in demand, because it is difficult to measure it quantitatively. As a result, few analytic models capture the dynamic nature of an EMS system and, thus, the allocation plans they generate are not efficient in practice. Therefore, this study proposes a simulation-based optimization method for ambulance allocation. A simulation model is constructed to mimic the operational processes of an EMS system, and to evaluate the performance of an ambulance allocation plan in an uncertain environment. Gaussian mixture model clustering is used to derive the uncertain spatial demand. Then, the simulation generates emergency demand based on the obtained spatial distribution. A Gaussian-process-based search algorithm is used together with the simulation model to identify optimal solutions. To validate the proposed method, a case study is conducted using data on emergency patients in the Shanghai Songjiang District. Compared with the current plan adopted in Songjiang, the experimental results demonstrate that the delay time and frequency of the EMS system can be reduced significantly by employing the proposed methods. Furthermore, nearly 41% of the allocation cost can be saved.

Ambulance allocation considering the spatial randomness of demand

To guarantee that emergency calls can be responded to in time, the government is obliged to implement an effective ambulance location plan. In practice, emergency medical service (EMS) systems work in an uncertain environment with stochastic demand, response time, and travel time. The uncertainty of these factors significantly affects ambulance location planning. However, most recent studies in this field fail to adequately consider the effect of the spatial uncertainty of demand since it is difficult to describe quantitatively. As a result, most allocation plans are not efficient. In this study, Gaussian mixture model clustering is innovatively utilized to quantitatively describe spatially uncertain demand. Accordingly, the chance constraint programming model for ambulance allocation planning is developed. The objective is to minimize the sum of the cost of patient lives lost and the operational cost of the emergency facilities. Two years of data from the Shanghai Songjiang District are used to validate the proposed method. The data from 2013 are utilized to fit the spatial distribution of demand. The data from 2014 are used to test and verify the obtained models. The experimental results demonstrate that the delay time can be significantly decreased with the proposed methods. Furthermore, compared with other classic assumptions for the spatial randomness of demand in this field, better service performance and lower cost are obtained with the proposed methods.

Using optimization to provide decision support for strategic emergency medical service planning – Three case studies

To achieve high performing emergency medical services (EMS), planning is of vital importance. EMS planners face several challenges when managing ambulance stations and the fleet of ambulances. In this paper, three strategic cases for EMS planners are presented together with potential solutions. In the first case, the effects of closing down a local emergency room (ER) are analyzed together with how adding an ambulance station and an ambulance to the area affected by the closing of the ER can be used to mitigate the negative consequences from the closing. The second case investigates a change in the organization of EMS. Currently, many non-urgent transport assignments are performed by ambulances which make them unavailable for more urgent calls. The potential for a more effective utilization of the ambulances is explored through transferring these assignments to designated transport vehicles. The third case is more technical and challenges the common practice regarding how time dependent demand is handled. Looking at the busiest hour or the average daily demand, is compared with taking time varying demand into account. The cases and solutions are studied using a recently developed strategic ambulance station location and ambulance allocation model for the Maximum Expected Performance Location Problem with Heterogeneous Regions (MEPLP-HR). The model has been extended to also include multiple time periods. This article demonstrates an innovative use of the model and how it can be applied to find and evaluate solutions to real cases within the field of strategic planning of EMS. The model is found to be a useful decision support tool when analyzing the cases and the expected performance of potential solutions.

Ambulance allocation optimization model for the overcrowding problem in US emergency departments: A case study in Florida

In the last decade, emergency department (ED) overcrowding has become a national crisis for the US healthcare system. Increasing mortality rates, decreasing quality of care, financial losses due to walkouts, and ambulance diversion are some of the consequences of ED overcrowding. Given the increasing demand in terms of ambulance utilization, being able to assign service requests to EDs efficiently, becomes a key function of the emergency medical services. This paper presents new ambulance allocation optimization models to reduce patients’ total time to treatment, waiting times; therefore, ED overcrowding. Disparities and fairness are considered in the development of the mixed integer programming models. Under a set of assumptions, we apply our strategies to allocate 75 ambulance emergencies to 11 EDs in a specific county in Florida. Heterogeneous types of patients, demand characteristics, and geographical/facility information are considered in the models. Based on numerical experiments and the situation in Florida, we show that the optimization techniques can be utilized for large problems and result in up to 31% improvement of the current decentralized model. Further analysis reveals the negative or positive impact that the strategies have on each patient, giving new insights for future policy modifications. Bi-objective, single objective, and game theory optimization models are implemented in this study.

Managing EMS Systems with User Abandonment in Emerging Economies

In many emerging economies, callers may abandon ambulance requests due to a combination of operational (small fleet size), infrastructural (long travel times) and behavioral factors (low trust in the ambulance system). As a result, ambulance capacity, which is already scarce, is wasted in serving calls that are likely to be abandoned later. In this paper, we investigate the design of an ambulance system in the presence of abandonment behavior, using a two-step approach. First, because the callers' actual willingness to wait for ambulances is censored, we adopt a Maximum Likelihood Estimator estimation approach suitable for interval censored data. Second, we employ a simulation-based optimization approach to explicitly incorporate customers' willingness to wait in: (a) tactical short-term decisions such as modification of dispatch policies and ambulance allocations at existing base locations; and (b) strategic long-term network design decisions of increasing fleet size and re-designing base locations. We calibrate our models using data from a major metropolitan city in India where historically 81.3% of calls were successfully served without being abandoned. We find that modifying dispatch policies or reallocating ambulances provide relatively small gains in successfully served calls (around 1%). By contrast, increasing fleet size and network re-design can more significantly increase the fraction of successfully served calls with the latter being particularly more effective. Redesigning bases with the current fleet size is equivalent to increasing the fleet size by 8.6% at current base locations. Similarly, adding 29% more ambulances and redesigning the base locations is equivalent to doubling the fleet size at the current base locations and adding 34% more ambulances and redesigning base locations is equivalent to a three-fold increase. Our results indicate that in the absence of changes in behavioral factors, significant investment is required to modify operational factors by increasing fleet size, and to modify infrastructural factors by redesigning base locations.

Demand Forecast and Multi-Objective Ambulance Allocation

This study considers the two-fold dynamic ambulance allocation problem, which includes forecasting the distribution of Emergency Medical Service (EMS) requesters and allocating ambulances dynamically according to the predicted distribution of requesters. EMSs demand distribution forecasting is based on on-record historical demands. Subsequently, a multi-objective ambulance allocation model (MOAAM) is solved by a mechanism called Jumping Particle Swarm Optimization (JPSO) according to the forecasted distribution of demands. Experiments were conducted using recorded historical data for EMS requesters in New Taipei City, Taiwan, for the years 2014 and 2015. EMS demand distribution for 2015 is forecasted according to the on-record historical demand of 2014. Ambulance allocation for 2015 is determined according to the anticipated demand distribution. The predicted demand distribution and ambulance allocation solved by JPSO are compared with historic data of 2015. The comparisons verify that the proposed methods yield lower forecasting error rates and better ambulance allocation than the actual one.

A Novel Algorithm for Identifying and Analyzing Humanitarian Relief Logistics Problems: Studying Uncertainty on the Basis of Interaction with the Decision Maker

Integration of decision-making in relief logistics intends establishing a better condition in fast response. Also, regarding the unpredictability of relief demand, uncertain conditions should be studied in a more applicable way of the planning process. This paper investigates a comprehensive novel algorithm in order to analyze a multi-level location-allocation routing emergency problem in uncertain conditions considering a fast and stable response to the different situations. Due to this reason, system dynamics and its control are studied. In the presented model, the demand is simulated by Mont Carlo simulation. Sum of allocated resources is determined by the expected value of Poisson’s distribution based on the approximation of its confidence interval. This model is capable to cover all demands existing in the network. Moreover, allocation of ambulances is considered based on the fuzzy cost. A simulated annealing (SA) algorithm is developed to solve the problem. The obtained results show that the proposed algorithm has good performance and it has been found out that total cost of the emergency system decreases with the increase of covering of stochastic demands, and the responsiveness in each scenario increases. However, the proposed algorithm is highly able to recognize the problem space to create a more precise and adequate description of the problem regarding the conditions.

Research of an emergency medical system for mass casualty incidents in Shanghai, China: a system dynamics model.

ObjectivesEmergency medical system for mass casualty incidents (EMS-MCIs) is a global issue. However, China lacks such studies extremely, which cannot meet the requirement of rapid decision-support system. This study aims to realize modeling EMS-MCIs in Shanghai, to improve mass casualty incident (MCI) rescue efficiency in China, and to provide a possible method of making rapid rescue decisions during MCIs.MethodsThis study established a system dynamics (SD) model of EMS-MCIs using the Vensim DSS program. Intervention scenarios were designed as adjusting scales of MCIs, allocation of ambulances, allocation of emergency medical staff, and efficiency of organization and command.ResultsMortality increased with the increasing scale of MCIs, medical rescue capability of hospitals was relatively good, but the efficiency of organization and command was poor, and the prehospital time was too long. Mortality declined significantly when increasing ambulances and improving the efficiency of organization and command; triage and on-site first-aid time were shortened if increasing the availability of emergency medical staff. The effect was the most evident when 2,000 people were involved in MCIs; however, the influence was very small under the scale of 5,000 people.ConclusionThe keys to decrease the mortality of MCIs were shortening the prehospital time and improving the efficiency of organization and command. For small-scale MCIs, improving the utilization rate of health resources was important in decreasing the mortality. For large-scale MCIs, increasing the number of ambulances and emergency medical professionals was the core to decrease prehospital time and mortality. For super-large-scale MCIs, increasing health resources was the premise.

A Data-Driven Districting to Improve Emergency Medical Service Systems

Emergency Medical Services (EMS) are responsible of providing medical assistance to any person who requires it in case of emergency, with the main goal of reducing the response time. To do so, several management strategies have been proposed, e.g., districting and preassigned dispatching lists. However, districts are often a priori defined at the strategical or tactical level, without considering the dynamics of service provisioning in detail. This may create problems at the operational level, e.g., the need to send ambulances out of their district to compensate local criticalities and keep a good service quality. To overcome this problem, we propose a data-driven districting that minimizes out-of-district assignments while compacting the districts and balancing their workloads. The approach consists of an iterative matheuristic in which, at each iteration, we first solve the operational problem (location and allocation of ambulances) for a given districting, and then we redistrict based on the operational solution. To validate our proposal, we apply the approach considering realistic data from the city of Montréal, Canada, working on a small region with 30 demand zones extracted from the entire city. Results show that the approach generates balanced districts in which the ambulances satisfy the emergency calls within a low response time. Results also suggest that the increment of the response time to keep the workloads balanced is limited.

Evaluating public ambulance service levels by applying a GIS based accessibility analysis approach

Ambulance vehicles are required to respond rapidly to medical emergencies. A number of factors may affect response times, most importantly the location of emergency services stations, the number of ambulance vehicles available at each station, road conditions and traffic volumes. To support spatial planning and provide input to the Integrated Development Plan of the City of Tshwane, a GIS based analysis of ambulance response time was undertaken. The purpose was to make recommendations aimed at the improved placement of stations and allocation of ambulances per station to improve service delivery through reducing the likely response times of ambulance services within the Tshwane Metropolitan area. The key input parameters used included the spatial distribution of demand (population), supply (station locations and ambulance numbers) and emergency road speeds These were tested against a provision standard of one ambulance per 30 000 people. The response target applied in suburban areas was 15 minutes but 40 minutes in low density outlying areas. The results show that the current emergency services stations’ locations are well distributed within the metropole; however, there seems to be a spatial mismatch in vehicle allocation. The paper discusses the impact of the two demand scenarios, namely: universal reliance on public ambulance services versus public ambulances only serving those without medical insurance. The paper concludes that GIS provide an extremely versatile and useful means of testing the implications of demand scenarios and standards relating to these factors of service provision and thereby contribute to better informed decision making.Keywords: Ambulance vehicles, response time, emergency services, accessibility, GIS

Emergency logistics for wildfire suppression based on forecasted disaster evolution

This paper aims to develop a two-layer emergency logistics system with a single depot and multiple demand sites for wildfire suppression and disaster relief. For the first layer, a fire propagation model is first built using both the flame-igniting attributes of wildfires and the factors affecting wildfire propagation and patterns. Second, based on the forecasted propagation behavior, the emergency levels of fire sites in terms of demand on suppression resources are evaluated and prioritized. For the second layer, considering the prioritized fire sites, the corresponding resource allocation problem and vehicle routing problem (VRP) are investigated and addressed. The former is approached using a model that can minimize the total forest loss (from multiple sites) and suppression costs incurred accordingly. This model is constructed and solved using principles of calculus. To address the latter, a multi-objective VRP model is developed to minimize both the travel time and cost of the resource delivery vehicles. A heuristic algorithm is designed to provide the associated solutions of the VRP model. As a result, this paper provides useful insights into effective wildfire suppression by rationalizing resources regarding different fire propagation rates. The supporting models can also be generalized and tailored to tackle logistics resource optimization issues in dynamic operational environments, particularly those sharing the same feature of single supply and multiple demands in logistics planning and operations (e.g., allocation of ambulances and police forces).

Data-Driven Omniscient Bounds and Greedy Policies for Ambulance Allocation and Dynamic Redeployment

We present an efficient data-driven computational bounding and solution approach for static allocation of ambulance fleet and its dynamic redeployment, where the goal is to position (or re-position) ambulances to bases to maximize the systems service level. We leverage the framework presented, to find data-driven omniscient bounds on the performance of a static location or dynamic redeployment policy. Central to our approach, is a discrete-event simulator to evaluate the impact of ambulance deployments to given call logs of emergency requests. We model ambulance allocation as an approximate submodular maximization problem, and devise a simple and efficient greedy algorithm that produces good solutions for static allocation, and can also be repeatedly employed in real-time for dynamic repositioning. Although the objective is not submodular, we generate data-driven guarantees on solution quality by formulating an omniscient upper bound (in both static and dynamic cases) using integer programming. We show that omniscient dispatch, as well as omniscient location combined with dispatch, are submodular upper bounds. Our experiments based on real data from an Asian city’s EMS, demonstrate how optimality gaps can be computed, and that they are small in this setting. The bound formulation that we present is general and can be applied for any alternative data-driven simulation framework or solution. We also test settings with customer abandonment and demonstrate the working of our algorithmic and bounding approach in that setting.

Genetic algorithm hybridized by a guided local search to solve the emergency coverage problem

The management of emergency logistics is addressed by several researchers. This paper addresses the ambulance allocation in order to cover sectors in the Rabat region of Morocco. Our model takes into account the dynamic and stochastic nature of emergency calls arrival. This work proposes a mathematical model of the coverage problem, resolved using a genetic algorithm (GA) initialised by a heuristic and hybridised by a guided local search (GLS). We consider 12 emergency locations; seven hospitals of the region and five fire stations. These algorithms are approved comparing to the optimal solutions done by Cplex software. As a result, the GA hybridised by a GLS provides a distribution of ambulances in each potential waiting site (hospital or fire station), and minimises the total lateness of emergency intervention.