R-CAST-MED: Applying Intelligent Agents to Support Emergency Medical Decision-Making Teams

  • Authors:

  • Shizhuo Zhu;Joanna Abraham;Sharoda A. Paul;Madhu Reddy;John Yen;Mark Pfaff;Christopher Deflitch

  • Affiliations:

  • College of Information Sciences and Technology, The Pennsylvania State University, Unversity Park, PA 16802, USA;College of Information Sciences and Technology, The Pennsylvania State University, Unversity Park, PA 16802, USA;College of Information Sciences and Technology, The Pennsylvania State University, Unversity Park, PA 16802, USA;College of Information Sciences and Technology, The Pennsylvania State University, Unversity Park, PA 16802, USA;College of Information Sciences and Technology, The Pennsylvania State University, Unversity Park, PA 16802, USA;College of Information Sciences and Technology, The Pennsylvania State University, Unversity Park, PA 16802, USA;Department of Emergency Medicine, Penn State Milton S. Hershey Medical Center, Penn State College of Medicine, Hershey, PA 17033, USA

  • Venue:
  • AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
  • Year:
  • 2007

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Abstract

Decision-making is a crucial aspect of emergency response during mass casualty incidents (MCIs). MCIs require rapid decisions to be taken by geographically-dispersed teams in an environment characterized by insufficient information, ineffective collaboration and inadequate resources. Despite the increasing adoption of decision support systems in healthcare, there is limited evidence of their value in large-scale disasters. We conducted focus groups with emergency medical services and emergency department personnel who revealed that one of the main challenges in emergency response during MCIs is information management. Therefore, to alleviate the issues arising from ineffective information management, we propose R-CAST-MED, an intelligent agent architecture built on Recognition-Primed Decision-making (RPD) and Shared Mental Models (SMMs). A simulation of R-CAST-MED showed that this tool enabled efficient information management by identifying relevant information, inferring missing information and sharing information with other agents, which led to effective collaboration and coordination of tasks across teams.