Ambulance allocation for maximal survival with heterogeneous outcome measures

Knight, Vincent A.; Harper, Paul Robert; Smith, Leanne

doi:10.1016/j.omega.2012.02.003

Cited by 123 publications

(95 citation statements)

References 25 publications

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“…Knight et al [41] addressed this limitation by incorporating a cardiac arrest survival function with step functions representing response time targets for other incident types. The heterogeneous objective function was a sum of the individual survival functions weighted 3 by their relative priorities; eectively a multi-objective optimization.…”

Section: Introductionmentioning

confidence: 99%

A simulation model to enable the optimization of ambulance fleet allocation and base station location for increased patient survival

McCormack

Coates

2015

European Journal of Operational Research

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(2015) 'A simulation model to enable the optimization of ambulance eet allocation and base station location for increased patient survival.', European journal of operational research., 247 (1). pp. 294-309. Further information on publisher's website:http://dx.doi.org/10.1016/j.ejor.2015.05.040Publisher's copyright statement: NOTICE: this is the author's version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reected in this document. Changes may have been made to this work since it was submitted for publication. A denitive version was subsequently published in European Journal of Operational Research, 247, 1, 2015Research, 247, 1, , 10.1016Research, 247, 1, /j.ejor.2015 Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. further improved survival when its location and resourcing were optimized for key periods of service. Also, the removal of a base station from the system was found to have minimal impact on survival probability when the selected station and resourcing were optimized simultaneously.

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Section: Introductionmentioning

confidence: 99%

A simulation model to enable the optimization of ambulance fleet allocation and base station location for increased patient survival

McCormack

Coates

2015

European Journal of Operational Research

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“…After all, there is only very little discrimination between different response times as an ambulance is either on time or too late. Possibly, it is better to use 'survival' as measure for the EMS system performance, as done by Knight et al [14] , Mayorga et al [21] and Van Barneveld [30] . However, it is difficult to quantify 'survival', as it depends on more factors than the response time solely.…”

Section: Discussionmentioning

confidence: 99%

“…Now, the ILP is given by the objective function of Eq. (9) subject to constraints (10) - (14) and the following integer and binary constraints:…”

Section: Mexclp2 For Compliance Tablesmentioning

confidence: 99%

Compliance tables for an EMS system with two types of medical response units

Barneveld

Mei

Bhulai

2017

Computers & Operations Research

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a b s t r a c tIn this paper, we consider an Emergency Medical Services (EMS) system with two types of medical response units: Rapid Responder Ambulances (RRAs) and Regular Transport Ambulances (RTAs). The key difference between both is that RRAs are faster, but they lack the ability to transport a patient to the hospital. To maintain the ability to respond to emergency requests timely when ambulances get busy, we consider compliance tables, which indicate the desired locations of the available ambulances. Our system brings forth additional complexity to the problem of computing optimal compliance tables, as we have two kinds of ambulances. We propose an Integer Linear Program (ILP) computing compliance tables for such a system, which uses outcomes of a Hypercube model as input parameters. Moreover, we include nestedness constraints and we set bounds on the relocation times in the ILP. To obtain more credible results, we simulate the computed compliance tables for different input parameters. Results show that bounding the time a relocation may last is beneficial in certain settings. Besides, including the nestedness constraints ensures that the number of relocations and the relocation time is reduced, while the performance stays unaffected.

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“…Many research works have contributed in the medical resource allocation problem, such as ambulance, bed, operating room and so on (Knight et al, 2012;Paul and Jotshi, 2013;Bai and Zhang, 2014). However, due to the key distinct characteristics of large-scale disasters, a salient point is the number of emergency medical resources on demand is much higher than supply.…”

Section: Previous Studies Of Emergency Resource Allocation Problemmentioning

confidence: 99%

A Markov decision process model of allocating emergency medical resource among multi-priority injuries

Wang

Zhao

2017

IJMOR

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Abstract:This article studies how to allocate a scare emergency medical resource at the beginning of large-scale disaster. One salient problem in emergency rescue is how to coordinate the resource among injuries of different degrees in different periods. The injuries are classified into three levels and given different priorities. Waiting cost with resources shortage, deteriorated cost with delay and transferring cost to other hospitals are introduced into the decision-making process. Basing on incorporating the costs into revenue function, a Markov decision process (MDP) model is proposed to establish the optimal action policies in different periods. A dynamic algorithm is proposed to derive the optimal solution. With a numerical experiment, the improvement of MDP is displayed and some managerial suggestions are also provided.Keywords: emergency management; medical resource allocation; Markov decision process; multi-priority injuries; operational research.

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Ambulance allocation for maximal survival with heterogeneous outcome measures

Cited by 123 publications

References 25 publications

A simulation model to enable the optimization of ambulance fleet allocation and base station location for increased patient survival

A simulation model to enable the optimization of ambulance fleet allocation and base station location for increased patient survival

Compliance tables for an EMS system with two types of medical response units

A Markov decision process model of allocating emergency medical resource among multi-priority injuries

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