An Injury Severity-, Time Sensitivity-, and Predictability-Based Advanced Automatic Crash Notification Algorithm...

Area of Science:

• Traumatology
• Emergency Medicine
• Automotive Safety Engineering

Background:

• Advanced Automatic Crash Notification (AACN) algorithms utilize vehicle telemetry to predict serious injury risk.
• Current triage efficiency and patient outcomes can be improved by minimizing undertriage (<5%) and overtriage (<50%).

Purpose of the Study:

• To develop an AACN algorithm to enhance motor vehicle crash response.
• Improve triage efficiency and patient outcomes by reducing undertriage and overtriage rates.
• Align triage decisions with American College of Surgeons recommendations.

Main Methods:

• Developed a Target Injury List based on injury severity, time sensitivity, and predictability.
• Utilized multivariable logistic regression with National Automotive Sampling System - Crashworthiness Data System data (2000-2011).
• Optimized and evaluated the AACN algorithm to minimize triage deviations.

Main Results:

• Achieved <50% overtriage and <5% undertriage for side impacts.
• Attained 6% to 16% undertriage for other crash modes.
• Estimated nationwide implementation could improve triage for 44% of undertriaged and 38% of overtriaged occupants.

Conclusions:

• The developed algorithm can guide emergency personnel in motor vehicle crash triage decisions.
• Nationwide implementation is projected to benefit an estimated 165,000 occupants annually through improved triage.
• The algorithm successfully lowers both undertriage and overtriage rates, enhancing care appropriateness.