scholarly journals Modeling Uncertain and Dynamic Casualty Health in Optimization-Based Decision Support for Mass Casualty Incident Response

2013 ◽  
Vol 5 (2) ◽  
pp. 32-44
Author(s):  
Duncan T. Wilson ◽  
Glenn I. Hawe ◽  
Graham Coates ◽  
Roger S. Crouch
Keyword(s):  
Decision Support ◽  
Health Assessment ◽  
Mass Casualty Incident ◽  
Mass Casualty ◽  
Support Program ◽  
Incident Response ◽  
Dynamic Nature ◽  
Significant Challenge ◽  
Optimisation Model ◽  
Health Model

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.

scholarly journals Modeling Uncertain and Dynamic Casualty Health in Optimization-Based Decision Support for Mass Casualty Incident Response

2015 ◽  
pp. 411-423
Author(s):  
Duncan T. Wilson ◽  
Glenn I. Hawe ◽  
Graham Coates ◽  
Roger S. Crouch
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Health Assessment ◽  
Mass Casualty Incident ◽  
Mass Casualty ◽  
Mass Casualty Incidents ◽  
Support Program ◽  
Dynamic Nature ◽  
Significant Challenge ◽  
Health Model

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.

Utilization of computer simulation to enhance mass casualty incident response skills

1985 ◽  
Vol 14 (5) ◽  
pp. 517 ◽  
Author(s):  
RD Kelley ◽  
KC Harrison ◽  
SM Lyon ◽  
LC Baldwin ◽  
CR Hansen
Keyword(s):  
Computer Simulation ◽  
Mass Casualty Incident ◽  
Mass Casualty ◽  
Incident Response ◽  
Enhance Mass

scholarly journals An armed assailant in our hospital: Are we prepared?

2021 ◽  
Vol 50 (9) ◽  
pp. 712-716
Author(s):  
Sohil Pothiawala ◽  
Rabind Charles ◽  
Wai Kein Chow ◽  
Kheng Wee Ang ◽  
Karen Hsien Ling Tan ◽  
...  
Keyword(s):  
Emergency Preparedness ◽  
Hospital Setting ◽  
Mass Casualty Incident ◽  
Mass Casualty ◽  
Incident Response ◽  
Potential Threat ◽  
Healthcare Facilities ◽  
Response Strategies ◽  
Hospital Preparedness ◽  
Response Plan

ABSTRACT While armed assailant attacks are rare in the hospital setting, they pose a potential risk to healthcare staff, patients, visitors and the infrastructure. Singapore hospitals have well-developed disaster plans to respond to a mass casualty incident occurring outside the hospital. However, lack of an armed assailant incident response plan can significantly reduce the hospital’s ability to appropriately respond to such an incident. The authors describe various strategies that can be adopted in the development of an armed assailant incident response plan. Regular staff training will increase staff resilience and capability to respond to a potential threat in the future. The aim of this article is to highlight the need for the emergency preparedness units of all hospitals to work together with various stakeholders to develop an armed assailant incident response plan. This will be of great benefit for keeping healthcare facilities safe, both for staff as well as for the community. Keywords: Armed assailant, hospital, preparedness, response, strategies

Mass Casualty Incident Response: Flexing PACU Muscles

2019 ◽  
Vol 34 (4) ◽  
pp. e14-e15
Author(s):  
Elizabeth Resweber ◽  
Alya Nadji ◽  
Aviva Mandel
Keyword(s):  
Mass Casualty Incident ◽  
Mass Casualty ◽  
Incident Response

scholarly journals Optimal service order for mass-casualty incident response

2017 ◽  
Vol 261 (1) ◽  
pp. 355-367 ◽  
Author(s):  
Behrooz Kamali ◽  
Douglas Bish ◽  
Roger Glick
Keyword(s):  
Mass Casualty Incident ◽  
Mass Casualty ◽  
Incident Response ◽  
Optimal Service

scholarly journals Mobile Decision Support Tool for Emergency Departments and Mass Casualty Incidents (EDIT): Initial Study (Preprint)

2018 ◽  
Author(s):  
Nicholas Boltin ◽  
Diego Valdes ◽  
Joan M. Culley ◽  
Homayoun Valafar
Keyword(s):  
Emergency Department ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Mass Casualty Incident ◽  
Mass Casualty ◽  
Patient Data ◽  
Chemical Exposures ◽  
System P ◽  
Collect Patient Data

BACKGROUND Chemical exposures pose a significant threat to life. A rapid assessment by first responders and emergency nurses is required to reduce death and disability. Currently, no informatics tools exist to process victims of chemical exposures efficiently. The surge of patients into a hospital emergency department during a mass casualty incident creates additional stress on an already overburdened system, potentially placing patients at risk and challenging staff to process patients for appropriate care and treatment efficacy. Traditional emergency department triage models are oversimplified during highly stressed mass casualty incident scenarios in which there is little margin for error. Emerging mobile technology could alleviate the burden placed on nurses by allowing the freedom to move about the emergency department and stay connected to a decision support system. OBJECTIVE This study aims to present and evaluate a new mobile tool for assisting emergency department personnel in patient management and triage during a chemical mass casualty incident. METHODS Over 500 volunteer nurses, students, and first responders were recruited for a study involving a simulated chemical mass casualty incident. During the exercise, a mobile application was used to collect patient data through a kiosk system. Nurses also received tablets where they could review patient information and choose recommendations from a decision support system. Data collected was analyzed on the efficiency of the app to obtain patient data and on nurse agreement with the decision support system. RESULTS Of the 296 participants, 96.3% (288/296) of the patients completed the kiosk system with an average time of 3 minutes, 22 seconds. Average time to complete the entire triage process was 5 minutes, 34 seconds. Analysis of the data also showed strong agreement among nurses regarding the app’s decision support system. Overall, nurses agreed with the system 91.6% (262/286) of the time when it came to choose an exposure level and 84.3% (241/286) of the time when selecting an action. CONCLUSIONS The app reliably demonstrated the ability to collect patient data through a self-service kiosk system thus reducing the burden on hospital resources. Also, the mobile technology allowed nurses the freedom to triage patients on the go while staying connected to a decision support system in which they felt would give reliable recommendations.

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