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Modeling Uncertain and Dynamic Casualty Health in Optimization-Based Decision Support for Mass Casualty Incident Response
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Author(s): Duncan T. Wilson (School of Engineering and Computing Sciences, Durham University, UK), Glenn I. Hawe (School of Engineering and Computing Sciences, Durham University, UK), Graham Coates (School of Engineering and Computing Sciences, Durham University, UK)and Roger S. Crouch (School of Engineering and Computing Sciences, Durham University, UK)
Copyright: 2015
Pages: 13
Source title:
Healthcare Administration: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-6339-8.ch021
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Abstract
When designing a decision support program for use in coordinating the response to Mass Casualty Incidents, the modelling of the health of casualties presents a significant challenge. In this paper we propose one such health model, capable of acknowledging both the uncertain and dynamic nature of casualty health. Incorporating this into a larger optimisation model capable of use in real-time and in an online manner, computational experiments examining the effect of errors in health assessment, regular updates of health and delays in communication are reported. Results demonstrate the often significant impact of these factors.
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