Improving Trauma Triage Models for Motor Vehicle Crashes
Trauma results in 30 million ED visits and 180,000 deaths annually. For motor-vehicle crashes, the decision to transport an occupant to a trauma center is based on a limited triage algorithm. Advanced Automated Collision Notification (AACN) systems (e.g., OnStar) have the potential to substantially improve resource allocation, trauma response and patient outcomes while reducing cost. However, current AACN-based triage algorithms only predict need for transport to a trauma center based on the probability of severe injury and do not consider cost/benefit or predict the need for costly services like helicopter transport. Existing databases will be analyzed to define the benefits of rapid transport as a function of injury type and severity and establish local cost models. This information will be used to develop improved AACN-based triage models that consider multiple outcomes including the need for helicopter transport, EMS, and firefighters. This research should lead to future CDC or NIH funding.
$35,000 grant from the University of Michigan Transportation Center