Understanding Communications in Medical Emergency Situations

Abstract

Good communication is essential within teams dealing with emergency situations. In this paper we look at communications within a resuscitation team performing cardio-pulmonary resuscitation. Communication underpins efficient collaboration, joint coordination of work, and helps to construct a mutual awareness of the situation. Poor communication wastes valuable time and can ultimately lead to lifethreatening mistakes. Although training sessions frequently focus on medical knowledge and procedures, soft skills, such as communication receive less attention. This paper analyses communication problems in the case of CPR and proposes an architecture that merges a situation awareness model and the belief-desire-intention (BDI) approach in multi-agent systems. The architecture forms the basis of an agent-based simulator used to assess communication protocols in CPR teams. Communications and MSAMany studies identify the solid link between communication, and SA and MSA that strongly affects a team's performance [15].Communication failures maybe classified along several dimensions: the content (messages are unclear or unstructured) [7] the entities themselves (members of the team do not always share the same communication code [3], and the intended purpose of the communication (members do not always correctly interpret the message). Other reasons for communication failures arise from an excessively noisy environment, stress, and the lack of accurate communication strategies in a particular critical situation, etc. [26].The Agency for Healthcare Research and Quality (AHRQ) in the United States recommends several information exchange standards for effective communication that will increase MSA [2] (Table 1)

A Highly Effective Deep Learning Based Escape Route Recognition Module for Autonomous Robots in Crisis and Emergency Situations

“…A significant practical weakness of most software agents is that they lack level 1 capabilities, so that they are not aware or only partially aware of their environment. However, these level 1 capabilities are necessary for sophisticated artificial agents' decision making [13,16].…”

“…2) Our module architecture is 78 times smaller than the MCIndoor20000 benchmark model and needs a significantly smaller amount of computational power [25], making it suitable for embedding in artificial drones and robots [26]. 3) We extend the work by Dugdale's group on socially relevant multi-agent systems in crisis and emergency situations by giving the artificial agent the ability to precisely recognize objects [16]. 4) We enhance the level 1 capabilities of Endsley's Situational Awareness theory [14].…”

“…4) We enhance the level 1 capabilities of Endsley's Situational Awareness theory [14]. 5) Our approach closes the gap in visual level 1 Situational Awareness capabilities for multi-agent systems, showing the potential of artificial intelligence in crisis and emergency management [16]. The paper is organized as follows: Next we present an overview of related work before providing the research methodology, including the applied deep learning methods and the data used for evaluation.…”

“…(p. 36). The combination of BDI architecture and Situational Awareness theory by Dugdale's group leads to better agent decisions [15,16]. Research has shown that the implementation of sophisticated behavioral models (BDI, Situational Awareness theory, etc.)…”

Introduction to the Minitrack on ICT and Artificial Intelligence for Crisis and Emergency Management

“…The paper by Ricardo Buettner and Hermann Baumgarti concerns "A Highly Effective Deep Learning Based Escape Route Recognition Module for Autonomous Robots in Crisis and Emergency Situations". The underlying rationale for the work comes from merging situation awareness and socially relevant agent-based systems (Mancheva and Dugdale, 2016). The paper shows how artificial agents can precisely recognise escape signs, doors and stairs for evacuation route planning.…”