Mass-casualty triage: Distribution of victims to multiple hospitals using the SAVE model

Dean, Matthew D.; Nair, Suresh K.

doi:10.1016/j.ejor.2014.03.028

Cited by 68 publications

(51 citation statements)

References 25 publications

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“…The relevant quantitative studies referred in Sect. 2 (Jacobson et al 2012;Dean and Nair 2014;Mills 2016;Repoussis et al 2016) also exclude this factor. Identifying the parameters for the change in evacuation rate remains a challenge.…”

Section: Resultsmentioning

confidence: 99%

“…However, there are few studies that specifically focus on managing mass-casualty traffic from the incident site to the medical facilities. Recently, Jacobson et al (2012), Dean and Nair (2014), Mills (2016), and Repoussis et al (2016) have presented optimization models related to this specific issue. Jacobson et al (2012) formulated a priority assignment model using stochastic dynamic programming to ration the emergency healthcare resources (for example, ambulances and operating rooms).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Mills (2016), stressing the importance of simple decision making in an MCI scenario, proposed a heuristic-based approach for patient prioritization to determine the order in which to transport the casualties. Considering the available resources and casualty survival probabilities, Dean and Nair (2014) formulated an optimization model to determine the optimal distribution of MCI casualties between multiple hospitals. Repoussis et al (2016) proposed a mixed integer program through which they aimed to optimize ambulance dispatching and treatment order, besides patient-to-hospital assignment.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It can, however, be argued that the assessment and classifications of casualties using a mathematical model at the incident site may not be realistic. Dean and Nair (2014) and Mills (2016) recognized that solving a mathematical program in real time can be a barrier to implementation in an urgent situation. Aylwin et al (2006) even suggested that simplifying casualty classification at the incident site and systematic casualty reassessment at different stages can be a better approach.…”

Section: Literature Reviewmentioning

confidence: 99%

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Mass-Casualty Distribution for Emergency Healthcare: A Simulation Analysis

Jat

Rafique

2020

Int J Disaster Risk Sci

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This study focuses on the casualty-load distribution problem that arises when a mass casualty incident (MCI) necessitates the engagement of multiple medical facilities. Employing discrete event simulations, the study analyzed different MCI response regimes in Lahore, Pakistan, that vary in terms of the level of casualty-load distribution and the required coordination between the incident site and the responding hospitals. Past terrorist attacks in this major metropolitan area were considered to set up experiments for comparing delays in treatment under the modeled regimes. The analysis highlights that the number of casualties that are allowed to queue up at the nearest hospital before diverting the casualty traffic to an alternate hospital can be an important factor in reducing the overall treatment delays. Prematurely diverting the casualty traffic from the incident site to an alternate hospital can increase the travel time, while a delay in diversion can overload the nearest hospital, which can lead to overall longer waiting times in the queue. The casualty distribution mechanisms based only on the responding hospitals' available capacity and current load can perform inefficiently because they overlook the trade-off between the times casualties spend in traveling and in queues.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Mass-Casualty Distribution for Emergency Healthcare: A Simulation Analysis

Jat

Rafique

2020

Int J Disaster Risk Sci

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“…Given the discussed points and the consequences of the poor management of patient distribution and in order to avoid the geographic effect and overload a single hospital, a systematic method of patient distribution is required to assign the casualties to the different hospitals available [4]. …”

Section: Introductionmentioning

confidence: 99%

Criteria and models for the distribution of casualties in trauma-related mass casualty incidents: a systematic literature review protocol

et al. 2017

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BackgroundOne of the most critical practices in mass casualty incident management is vacating the victims from scene of the incident and transporting them to proper healthcare facilities. Decision on distribution of casualties needs to be taken on pre-developed policies and structured decision support mechanisms. While many studies tried to present models for the distribution of casualties, no systematic review has yet been conducted to evaluate the existing models on casualty distribution following mass casualty incidents. A systematic review is therefore needed to examine the existing models of patient distribution and to provide a summary of the models. This systematic review protocol is aimed to examine the existing models and extracting rules and principles of mass casualty distribution.MethodsThis study will comprehensively investigate existing papers with search phrases and terms including “mass casualty incident”, distribution, evacuation, and Mesh terms directly corresponding to search phrases. No limitations on the type of studies, date of publication, or language of the relevant documents will be imposed. PubMed, Web of Science, Scopus, and Google Scholar will be searched to access the relevant documents. Included papers will be critically appraised by two independent reviewers. The data including incidents type, scene characteristics, patient features, pre-hospital resources, and hospital resources will be categorized. Subgroup analysis will be conducted when possible.DiscussionTo the best of our knowledge, no study has yet addressed the effects and interaction of contributing factors on the decision-making processes for casualty’s distribution. This is the first study that comprehensively assesses and critically appraises the current models of casualty distribution. This study will provide evidences about models and criteria for casualty distribution following mass casualty incidents.Systematic review registrationPROSPERO Registration Number: CRD42016049115 Electronic supplementary materialThe online version of this article (doi:10.1186/s13643-017-0538-z) contains supplementary material, which is available to authorized users.

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A Meta Algorithm for Reinforcement Learning: Emergency Medical Service Resource Prioritization Problem in an MCI as an Example

Shin

Lee

2020

Springer Proceedings in Mathematics &Amp; Statistics

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Mass-casualty triage: Distribution of victims to multiple hospitals using the SAVE model

Cited by 68 publications

References 25 publications

Mass-Casualty Distribution for Emergency Healthcare: A Simulation Analysis

Mass-Casualty Distribution for Emergency Healthcare: A Simulation Analysis

Criteria and models for the distribution of casualties in trauma-related mass casualty incidents: a systematic literature review protocol

A Meta Algorithm for Reinforcement Learning: Emergency Medical Service Resource Prioritization Problem in an MCI as an Example

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