Characterization of the occult nature of injury for frequently occurring motor vehicle crash injuries

•Timely triage is key to reduce the morbidity and mortality of occult injuries.•An Occult Score was derived using EMS professional survey data.•AIS 2–4 abdominal injuries were perceived as the most occult injuries.•The Occult Score is part of an Advanced Automatic Crash Notification algorithm.•The algorithm’s goal is to reduce response times and improve patient outcomes. Occult injuries are not easily detected and can be potentially life-threatening. The purpose of this study was to quantify the perceived occultness of the most frequent motor vehicle crash injuries according to emergency medical services (EMS) professionals. An electronic survey was distributed to 1,125 EMS professionals who were asked to quantify the likelihood that first responders would miss symptoms related to a particular injury on a 5-point Likert scale. The Occult Score for each injury was computed from the average of all the survey responses and normalized to be a continuous metric ranging from 0 to 1 where 0 is a non-occult (highly apparent on initial presentation) injury and 1 is an occult (unapparent on initial presentation) injury. Overall, 110,671 survey responses were collected. The Occult Score ranged from 0 to 1 with a mean, median, and standard deviation of 0.443, 0.450, and 0.233, respectively. When comparing the Occult Score of an injury to its corresponding AIS severity, there was no relationship between the metrics. When stratifying by body region, injury type, and AIS severity, it was evident that AIS 2–4 abdominal injuries with lacerations, hemorrhage, or contusions were perceived as the most occult injuries. Timely triage is key to reduce the morbidity and mortality associated with occult injuries. The Occult Score developed in this study to describe the predictability of an injury in a motor vehicle crash will be used as part of a larger effort, including incorporation into an advanced automatic crash notification (AACN) algorithm to detect crash conditions associated with a patient’s need for prompt treatment at a trauma center.