Learning Objective 1: describe how artificial intelligence can be used to develop a clinical decision rule.
Learning Objective 2: recognize the development of a trauma triage decision rule as one step in the translation of evidence to the clinical care of injured persons.
Methods: This study used classification tree analysis to examine 74,626 NASS CDS database records of adults who were involved in vehicular crashes. Thirteen predictors were examined for their ability to classify (predict) patients having severe injury, an indicator of the need for TC care. Using the classification tree data, a triage decision rule was developed to identify patients with severe injuries who should be transported to a TC.
Results: Police-estimated injury severity, manner of collision, number of persons injured, and age were the best predictors of severe injury and became the decision points for a triage decision rule. Sensitivity and specificity of the rule in statistical modeling were 99.18% and 73.96%, respectively.
Conclusion: Artificial intelligence methods classified injury data and identified patients for TC transport. These classification data were used to develop a triage decision rule. Prospective validation of the rule is needed.