Occupant Transportation Decision Algorithm: an Advanced Automatic Crash Notification Algorithm Developed and Evaluated for Motor Vehicle Crash Occupant Triage

The objective was to develop an Advanced Automatic Crash Notification (AACN) algorithm and evaluate its performance in making optimal occupant triage decisions. The developed AACN algorithm known as the Occupant Transportation Decision Algorithm (OTDA) uses measurements obtainable from vehicle telemetry to predict risk of overall occupant injury and recommend a transportation decision for the occupant following a motor vehicle crash (MVC), particularly whether transport to a Level I/II trauma center is recommended. A list of injuries necessitating treatment at a Level I/II trauma center (TC) was determined using an injury-based approach based on three facets (severity, time sensitivity, and predictability). These three facets were quantified for each injury from expert physician and emergency medical services (EMS) professional opinion and database analyses of the National Trauma Data Bank and National Inpatient Sample. Severity, Time Sensitivity, and Predictability Scores were summed for each injury to compute an Injury Score. Injuries with an Injury Score exceeding a particular threshold were included on the Master Target Injury List, which is a list of injuries more likely to require Level I/II TC treatment. OTDA inputs for development include the Master Target Injury List and 38,970 National Automotive Sampling System-Crashworthiness Data System (NASS-CDS) 2000-2011 occupants. The OTDA uses multivariate logistic regression to predict an occupant's risk of sustaining an injury on the Master Target Injury List from the following model variables: longitudinal/lateral delta-v, number of quarter turns (in rollover only), belt status, multiple impacts, and airbag deployment. A parametric OTDA was developed with five tunable parameters allowing for extensive optimization. The OTDA was optimized with a genetic algorithm that compared the OTDA transportation decision for each NASS-CDS occupant to a dichotomous representation of their Injury Severity Score (ISS). Occupants with ISS 16+ should be transported to a Level I/II TC. OTDA optimization minimized under triage (UT) and over triage (OT) rates with the goal of producing UT rates < 5% and OT rates < 50% as recommended by the American College of Surgeons (ACS). For the optimized OTDA, UT rates by crash mode were 5.9% (frontal), 4.6% (near side), 2.9% (far side), 7.0% (rear), and 16.0% (rollover). OT rates by crash mode for the optimized OTDA were 49.7% (frontal), 47.9% (near side), 49.7% (far side), 44.0% (rear), and 49.7% (rollover). The OTDA was developed with an injury-based approach that examined three injury facets to identify injuries necessitating treatment at a Level I/II TC. Large hospital and survey datasets containing information on injuries, mortality risk, treatment urgency, and hospital transfers were used in conjunction with large crash datasets with crash, vehicle, occupant, and injury data. The OTDA has been rigorously optimized and has demonstrated improved UT rates compared to other AACN algorithms in the literature and OT rates meeting ACS recommendations. Since the OTDA uses only vehicle telemetry measurements specified in Part 563 regulation, this AACN algorithm could be readily incorporated into new vehicles to inform emergency personnel of recommended triage decisions for MVC occupants. The overall societal purpose of this AACN algorithm is to reduce response times, increase triage efficiency, and improve overall patient outcome.