Ophélie Morand, Stéphane Safin, Robert Larribau, & Caroline Rizza. (2023). Understanding and Improving Collaboration in Emergency Simulations with a Local Chain of Survival. In V. L. Thomas J. Huggins (Ed.), Proceedings of the ISCRAM Asia Pacific Conference 2022 (pp. 117–129). Palmerston North, New Zealand: Massey Unversity.
Abstract: Out-of-hospital cardiac arrest (OHCA) and choking are two emergencies where the rapid action of a bystander can increase the victim's chances of survival. Few bystanders act because they are not aware of their role as the first link in the chain of survival. Working on collaboration among a local chain of survival and using applications to improve communication and provide tutorials of actions to perform can be used to overcome this issue. We investigate these elements in the context of the Geneva Chain of Survival using simulations. The results show that an optimal collaboration means a lead’s handover between the intervening parties. Collaboration can be degraded by problems of communication, panic , and confusion. Applications constitute a valuable addition to enhance the dispatcher's awareness and to help guide the CPR while not extending the intervention time. Finally, the debriefing that follows enables the acquisition of competencies through experiential learning that relies on emotions.
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Tobias Andersson Granberg, Sara Erlander, David Fredman, Lovisa Olovsson, & Emma Persson. (2022). Predicting Volunteer Travel Time to Emergencies. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 44–54). Tarbes, France.
Abstract: A model is developed, which can predict the travel time for volunteers that are dispatched as first responders to emergencies. Specifically, the case of lay responders to out of hospital cardiac arrest is studied. Positions from historical responses is used to estimate the real response times, which are used to train and evaluate the new travel time model. The new model considers the road network and the transport mode most likely used by the volunteers. The results for the new model are compared to a model used in an existing volunteer initiative. They show that the new model can make better predictions in 59.7% of the cases. This can be used directly as a base for improving the travel time estimates in existing volunteer initiatives, and to improve the input data to the continuously evolving volunteer resource management systems.
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Lennart Landsberg, David Ganske, Christopher Munschauer, & Ompe Aimé Mudimu. (2020). Using Existing Data to Support Operational Emergency Response in Germany – Current Use Cases, Opportunities and Challenges. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 406–415). Blacksburg, VA (USA): Virginia Tech.
Abstract: The availability of resources in the fire and ambulance services in Germany is facing a radical change. Demographic and social transition is reducing the availability of volunteer personnel, and increasing traffic congestion in cities is resulting in longer travel times for emergency vehicles. This paper presents the findings of the definition phase of a research project that addresses these changes. It shows the basic idea of how resilience of fire and ambulance services can be improved by analyzing operational data from past incidents using artificial intelligence (AI). The primary objective is the development of a decision support system for control center dispatchers, which ensures optimal use of available resources. As the result of the definition phase, this paper gives an overview of existing data, current as well as future use cases and also highlights risks and challenges that have to be considered.
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Jess Kropczynski, Rob Grace, Shane Halse, Doina Caragea, Cornelia Caragea, & Andrea Tapia. (2019). Refining a Coding Scheme to Identify Actionable Information on Social Media. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: This paper describes the use of a previously established qualitative coding scheme developed through a design workshop with public safety professionals, and applied the schema to social media data collecting during crises. The intention of applying this scheme to existing crisis datasets was to acquire training data for machine learning. Applying the coding scheme to social media data revealed that additional subcategories of the coding scheme are necessary to satisfy information requirements necessary to dispatch first responders to an incident. The coding scheme was refined and adapted into a set of instructions for qualitative coders on Amazon Mechanical Turk. The contribution of this work is a coding scheme that is more directly related to the information needs of public safety professionals. Implications of early results using the refined coding scheme are discussed in terms of proposed automated methods to identify actionable information for dispatch of first responders during emergency incidents.
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Rob Grace, Shane Halse, Jess Kropczynski, Andrea Tapia, & Fred Fonseca. (2019). Integrating Social Media in Emergency Dispatch via Distributed Sensemaking. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: Emergency dispatchers typically answer 911 calls and relay information to first responders; however, new workflows arise when social media analysts are included in emergency dispatch work. In this study we examine emergency dispatch workflows as distributed sensemaking processes performed among 911 call takers, dispatchers, and social media analysts during simulated emergency dispatch operations. In active shooter and water rescue scenarios, emergency dispatch teams including call takers, dispatchers, and social media analysts make sense of unfolding events by analyzing, aggregating, and synthesizing information provided by 911 callers and social media users during each scenario. Findings from the simulations inform design requirements for social media analysis tools that can help analysts detect, seek, and analyze information posted on social media during a crisis, and protocols for coordinating analysts? sensemaking activities with those of 911 call takers and dispatchers in reconfigured emergency dispatch workflows.
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