Priority dispatching strategies for EMS systems

Bandara, Damitha; Mayorga, Marı́a E.; McLay, Laura A.

doi:10.1057/jors.2013.95

Cited by 68 publications

(42 citation statements)

References 25 publications

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“…In emergency systems planning, Carter et al [44] showed that defining response areas for each ambulance will decrease the average response time. Bandara et al [39] proposed to incorporate better dispatching rule for ambulances together with defined response areas by ambulance in their future research. Without considering call priorities and operational constraints, the three-level integrated problem is related in a way by considering the ambulance location as a facility, response areas as allocated demand sites, and number of ambulances (at each location) as resource capacity.…”

Section: Discussionmentioning

confidence: 99%

“…Various researchers studied other dispatch strategies. Bandara et al [39] incorporated call urgency into their proposed dispatch heuristic which assigns the nearest available ambulance for Priority 1 calls and the less busy ambulance for Priority 2 calls. Results from simulation experiments reported an increase in patient survivability, decreased average response time, and higher percentage of Priority 1 calls served within 10 minutes.…”

Section: Dispatch Algorithms (Dynamic)mentioning

confidence: 99%

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Solving a Location, Allocation, and Capacity Planning Problem with Dynamic Demand and Response Time Service Level

Lin

2014

Mathematical Problems in Engineering

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Logistic systems with uncertain demand, travel time, and on-site processing time are studied here where sequential trip travel is allowed. The relationship between three levels of decisions: facility location, demand allocation, and resource capacity (number of service units), satisfying the response time requirement, is analysed. The problem is formulated as a stochastic mixed integer program. A simulation-based hybrid heuristic is developed to solve the dynamic problem under different response time service level. An initial solution is obtained from solving static location-allocation models, followed by iterative improvement of the three levels of decisions by ejection, reinsertion procedure with memory of feasible and infeasible service regions. Results indicate that a higher response time service level could be achieved by allocating a given resource under an appropriate decentralized policy. Given a response time requirement, the general trend is that the minimum total capacity initially decreases with more facilities. During this stage, variability in travel time has more impact on capacity than variability in demand arrivals. Thereafter, the total capacity remains stable and then gradually increases. When service level requirement is high, the dynamic dispatch based on first-come-first-serve rule requires smaller capacity than the one by nearest-neighbour rule.

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Section: Discussionmentioning

confidence: 99%

Section: Dispatch Algorithms (Dynamic)mentioning

confidence: 99%

Solving a Location, Allocation, and Capacity Planning Problem with Dynamic Demand and Response Time Service Level

Lin

2014

Mathematical Problems in Engineering

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“…However, it is a requirement on the police to be visible to the public in order to deter crime and hence they must have dynamic positions. A recent study, (Bandara, Mayorga, & McLay, 2014), noted that most ambulance location models involve the rule of dispatching the closest ambulance available to the incident irrespective of the incident severity. It was also noted that this method is not always optimal for minimising average response times (Carter, Chaiken, & Ignall, 1972).…”

Section: Background To Dispatchmentioning

confidence: 99%

Optimising police dispatch for incident response in real time

Dunnett

Leigh

Jackson

2018

Journal of the Operational Research Society

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aeronautical and automotive engineering, loughborough university, loughborough, england ABSTRACT It is crucial that police forces operate in a cost efficient manner and, in the case of incident response, that the most efficient resources are allocated. The current procedure is that police response units are allocated manually by a dispatcher using a resource list and mapping software. The efficiency of this process can be improved by the use of integrated mathematical approaches embedded within an automatic framework, yielding the optimal selection framework developed in this paper. The framework combines mapping and routing algorithms, and a decision process to facilitate optimal officer selection for incident response. The decision process considers information such as quickest response time, predicted traffic conditions, driving qualifications, response unit availability and demand coverage. The selection framework has been tested and validated through simulation and has shown to increase the efficiency of response units through reduced response times, increased response unit availability, and greater demand coverage.

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“…They also noted that dispatching the closest vehicle is not always optimal. Bandara et al [26] proposed a heuristic algorithm to dispatch ambulances based on priority of the call. Their computational experiments showed that the heuristic improved patient survival rate by 4% while decreasing the average response rate by 2%.…”

Section: Related Literaturementioning

confidence: 99%

Balancing Ambulance Crew Workloads via a Tiered Dispatch Policy

Saydam

2016

Pesqui. Oper.

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ABSTRACT. Emergency Medical Services (EMS) system's mission is to provide timely and effective treatment to anyone in need of urgent medical care throughout their jurisdiction. The default dispatch policy is to send the nearest ambulance to all medical emergencies and it is widely accepted by many EMS providers. However, sending nearest ambulance is not always optimal, often imposes heavy workloads on ambulance crews posted in high demand zones while reducing available coverage or requiring ambulance relocations to ensure high demand zones are covered adequately. In this paper we propose a tiered dispatch policy to balance the ambulance crew workloads while meeting fast response times for priority 1 calls. We use a tabu search algorithm to determine the initial ambulance locations and a simulation model to evaluate the impact of a tiered dispatch policy on ambulance crew workloads, coverage rates for priority 1-3 calls, and on survivability rate for out of hospital cardiac arrests. We present computational statistics and demonstrate the efficacy of the tiered dispatch policy using real-world data.

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Priority dispatching strategies for EMS systems

Cited by 68 publications

References 25 publications

Solving a Location, Allocation, and Capacity Planning Problem with Dynamic Demand and Response Time Service Level

Solving a Location, Allocation, and Capacity Planning Problem with Dynamic Demand and Response Time Service Level

Optimising police dispatch for incident response in real time

Balancing Ambulance Crew Workloads via a Tiered Dispatch Policy

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