(P1-43) Standardized Full-Scale Exercises using Indicators Related to Patient Outcome: A Method to Increase Knowledge Coping with Aircrafts Incidents

Abstract

Introduction

Disaster medicine is a young discipline and there is a need for the development of methods for evaluation and research. This includes full-scale disaster exercises that are quite costly. Within each organization these exercises are seldom conducted. If there was a standardized concept on how to conduct as well as evaluate these exercises, this could lead to better knowledge and cost effectiveness. The aim of this study was to increase awareness of the possibility to develop and conduct full-scale exercises in different settings using performance indicators combined with indicators related to patient outcome serving as a basis for comparison and evaluation process.

Methods

Two full-scale exercises in different organizations were studied. Identical panorama with the same number and type of casualties was used. Sets of performance indicators combined with indicators for unfavorable patient outcome, according to the Emergo Train System®, were recorded as well as all transportation times and the patient distribution to selected hospitals. Qualified observers scored the results on predetermined locations; on the scene, hospital and strategic command and control.

Results

The lowest scored performance indicators were “first report to dispatch”, “second report from scene” and “first patient evacuated”. Due to insufficient response and evacuation times of victims to the receiving hospitals the unfavourable patient outcome, regarding preventable deaths and preventable complications were 28% (n = 18) and 41% (n = 17), respectively.

Conclusions

Standardized full-scale exercises where the same type of results is recorded can be conducted. This combination of performance indicators and Emergo Train System® leads to probabilities of development and better command and control response. Future use of the same concept may demonstrate important results that will lead to new and better knowledge that can be applied during real incidents.