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Evaluation of a Scalable Information Analytics System for Enhanced Situational Awareness in Mass Casualty Events
We investigate the utility of DIORAMA-II system which provides enhanced situational awareness within a disaster scene by using real-time visual analytics tools and a collaboration platform between the incident commander and the emergency responders. Our trials were conducted in different geographica...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4940543/ https://www.ncbi.nlm.nih.gov/pubmed/27433161 http://dx.doi.org/10.1155/2016/9362067 |
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author | Ganz, Aura Schafer, James M. Yang, Zhuorui Yi, Jun Lord, Graydon Ciottone, Gregory |
author_facet | Ganz, Aura Schafer, James M. Yang, Zhuorui Yi, Jun Lord, Graydon Ciottone, Gregory |
author_sort | Ganz, Aura |
collection | PubMed |
description | We investigate the utility of DIORAMA-II system which provides enhanced situational awareness within a disaster scene by using real-time visual analytics tools and a collaboration platform between the incident commander and the emergency responders. Our trials were conducted in different geographical areas (feature-rich and featureless regions) and in different lighting conditions (daytime and nighttime). DIORAMA-II obtained considerable time gain in efficiency compared to conventional paper based systems. DIORAMA-II time gain was reflected in reduction of both average triage time per patient (up to 34.3% average triage time reduction per patient) and average transport time per patient (up to 76.3% average transport time reduction per red patient and up to 66.3% average transport time reduction per yellow patient). In addition, DIORAMA-II ensured that no patients were left behind or transported in the incorrect order compared to the conventional method which resulted in patients being left behind and transported in the incorrect order. |
format | Online Article Text |
id | pubmed-4940543 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-49405432016-07-18 Evaluation of a Scalable Information Analytics System for Enhanced Situational Awareness in Mass Casualty Events Ganz, Aura Schafer, James M. Yang, Zhuorui Yi, Jun Lord, Graydon Ciottone, Gregory Int J Telemed Appl Research Article We investigate the utility of DIORAMA-II system which provides enhanced situational awareness within a disaster scene by using real-time visual analytics tools and a collaboration platform between the incident commander and the emergency responders. Our trials were conducted in different geographical areas (feature-rich and featureless regions) and in different lighting conditions (daytime and nighttime). DIORAMA-II obtained considerable time gain in efficiency compared to conventional paper based systems. DIORAMA-II time gain was reflected in reduction of both average triage time per patient (up to 34.3% average triage time reduction per patient) and average transport time per patient (up to 76.3% average transport time reduction per red patient and up to 66.3% average transport time reduction per yellow patient). In addition, DIORAMA-II ensured that no patients were left behind or transported in the incorrect order compared to the conventional method which resulted in patients being left behind and transported in the incorrect order. Hindawi Publishing Corporation 2016 2016-06-28 /pmc/articles/PMC4940543/ /pubmed/27433161 http://dx.doi.org/10.1155/2016/9362067 Text en Copyright © 2016 Aura Ganz et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Ganz, Aura Schafer, James M. Yang, Zhuorui Yi, Jun Lord, Graydon Ciottone, Gregory Evaluation of a Scalable Information Analytics System for Enhanced Situational Awareness in Mass Casualty Events |
title | Evaluation of a Scalable Information Analytics System for Enhanced Situational Awareness in Mass Casualty Events |
title_full | Evaluation of a Scalable Information Analytics System for Enhanced Situational Awareness in Mass Casualty Events |
title_fullStr | Evaluation of a Scalable Information Analytics System for Enhanced Situational Awareness in Mass Casualty Events |
title_full_unstemmed | Evaluation of a Scalable Information Analytics System for Enhanced Situational Awareness in Mass Casualty Events |
title_short | Evaluation of a Scalable Information Analytics System for Enhanced Situational Awareness in Mass Casualty Events |
title_sort | evaluation of a scalable information analytics system for enhanced situational awareness in mass casualty events |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4940543/ https://www.ncbi.nlm.nih.gov/pubmed/27433161 http://dx.doi.org/10.1155/2016/9362067 |
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