Combat medics care for battlefield casualties at point of injury (PoI), and stabilize and transport them to field care facilities. Casualty care requires complex decisions under austere and frequently dangerous conditions. Future operations are not expected to have immediate evacuation available, requiring more complex care and potential for complications including circulatory shock, which is a life-threatening state that can lead to organ failure and death if not detected and treated. We report on a project to develop decision support for combat medics (Role 1) and Battalion Aid Station/Field Hospital clinicians (Role 2) by combining resources of two proven intensive care unit (ICU) decision support systems. Machine learning algorithms are under development that will be used to detect the probability of shock in a casualty. Algorithms will be compared with actual clinical decisions in a 'silent test.' A parallel effort is developing a software program for use with a vital signs sensor that can be installed on a DoD-approved version of the Samsung smart phone and tablet, then evaluated in the field by medics and clinicians. Faster, more accurate decisions can improve care for trauma patients, in circumstances where delays can increase morbidity and mortality.