Abstract: A computer-based system that includes one or more computing devices configured to receive user input comprising a plurality of parameters indicating a condition of a trauma patient, the plurality of parameters comprising at least one patient reported outcome; apply the plurality of parameters to a predictive model trained to determine, based on the plurality of parameters, a quality of life designation for the patient; and transmit the quality of life designation to a user computing device for display on the user computing device.

Abstract: A computer-based, trauma-patient-triage system includes one or more computing devices configured to: receive, from a mobile computing device, user input comprising a plurality of parameters indicating a condition of a trauma patient; apply the plurality of parameters to one or more machine-learning algorithms trained to determine, based on the plurality of parameters, a trauma-triage category for the patient, wherein the trauma-triage category for the patient indicates an NEI-6 designation for the patient; and transmit the trauma-triage category to the mobile computing device for display on the mobile computing device.

Abstract: In some examples, a computing system includes a data repository configured to store a plurality of treatments and a corresponding plurality of clinically relevant phrases; and a computing system comprising processing circuitry configured to receive prehospital data; determine at least one clinically relevant phrase of the plurality of clinically relevant phrases present in the EMS prehospital data; and determine at least one recommended treatment associated with the at least one clinically relevant phrase.

Abstract: An elderly-mortality prediction system includes a computing system configured to apply a first analytical model to a subset of a set of patient data parameters for computing a quick elderly mortality after trauma (qEMAT) score indicative of a likelihood of mortality of the patient, wherein the subset of the data parameters includes patient data available at admission of the patient, and further configured to apply a second analytical model to compute a full elderly mortality after trauma (fEMAT) score as an updated score indicative of a likelihood of mortality of the patient, using a full set of patient demographic data parameters including the subset of the data parameters available at admission of the patient and additional data parameters based upon diagnostic results and medical history of the patient.