Dynamic Decision Making and Training for Disaster

Cleotilde Gonzalez, Carnegie Mellon University
Angela Brunstein

A revised version appears as "Training for Emergencies" The Journal of Trauma: Injury, Infection, and Critical Care: August 2009 - Volume 67 - Issue 2 - pp S100-S105

Abstract or Description

Background
Disaster triage embodies all key features of dynamic decision making: Multiple decisions have to be made under time pressure and workload. Situations are often unpredictable requiring trainees to apply learned routines to novel conditions. Up to this point, psychological theories of learning can provide only little support on how to train disaster responders for these challenging situations.
Methods
We summarize and illustrate several examples of dynamic decision making research using simulations and microworlds as a starting point for a new theory of learning and skill acquisition in disaster triage. We describe MEDIC, a microworld in the context of medical diagnosis, and other simple tasks designed to gather people's understanding of accumulation, a basic component of dynamic tasks.
Results
Using a microworld called MEDIC, we demonstrate the difficulties of learning to be effective at medical decision making and present a set of theoretical constructs which help explain those difficulties. Implications for how to overcome them are also discussed. Based on this kind of research and our instance-based learning theory, we develop principles for the design of effective disaster training and for building a theoretical framework that can systematically predict how to best train for successful performance in disaster situations. Finally, we also demonstrate the difficulty of understanding dynamic systems; educated adults with medical expertise have trouble understanding even simple dynamic medical problems.
Conclusions
Dynamic decision making research can be used as a theoretical and empirical reference for advancing pediatric triage training to prepare trainees for disaster triage. Recommendations for effective learning derived from dynamic decision making research are presented.