Geoffrey Hoare, Mary Beth Russell, Aaron Kite-Powell, & Rick France. (2010). Developing H1N1 hospital surge dashboard indicators: A demonstration. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Developing key state-wide indicators of Florida's health care system's public health capacity during the H1N1 Pandemic has been challenging. This demonstration outlines work to develop a key indicator of patient surge caused by the H1N1 outbreak. Further work to calibrate this measure and relate it to surge in other health care organizations is outlined.
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Jonas Rybing, Johan Larsson, Carl-Oscar Jonson, & Erik Prytz. (2016). Preliminary Validation Results of DigEmergo for Surge Capacity Management. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: This paper presents preliminary analysis from a validation study of a novel emergency medicine command and control training and evaluation simulator: DIGEMERGO®. The simulated emergency scenario was a surge capacity event at a generic emergency department, in which the participants took on a management role as the emergency department?s coordinating head nurse. A between group validation design with medical expert and novice participants was used. Initial analysis examined three triage measures associated with surge capacity management performance: time to triage, amount of patients triaged, and triage accuracy. The results show that experts were significantly more accurate at triaging in-hospital patients, but not incoming trauma patients. No significant differences in time or number of patients triaged was found. These initial results partially indicate simulator validity, but trauma patient triage accuracy suffered from a confounding variable in the triage system used. Analysis of additional measures is undergoing to further investigate validity claims.
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Ke Wang, Yongsheng Yang, Genserik Reniers, Jian Li, & Quanyi Huang. (2021). An Attribute-based Model to Retrieve Storm Surge Disaster Cases. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 567–580). Blacksburg, VA (USA): Virginia Tech.
Abstract: In China, storm surge disasters cause severe damages in coastal regions. One of the most important tasks is to predict affected regions and their relative damage levels to support decision-making. This study develops a two-stage retrieval model to search the most similar past disaster case to complete prediction. Based on spatial attributes of cases, the top-ranking past cases with a similar location to the target case are selected. Among these past cases, the most similar past case is selected by disaster attribute similarities. Three typical storm surge case studies have been used and implemented into this proposed model and the results show that all the most affected regions can be predicted. The proposed model simplifies the prediction process and updates results quickly. This study provides useful information for the government to make real-time response plans.
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