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AI-enabled decision-support systems aim to help medical providers rapidly make decisions with limited information during medical emergencies. A critical challenge in developing these systems is supporting providers in interpreting the system output to make optimal treatment decisions. In this study, we designed and evaluated an AI-enabled decision-support system to aid providers in treating patients with traumatic injuries. We first conducted user research with physicians to identify and design information types and AI outputs for a decision-support display. We then conducted an online experiment with 35 medical providers from six health systems to evaluate two human-AI interaction strategies: (1) AI information synthesis and (2) AI information and recommendations. We found that providers were more likely to make correct decisions when AI information and recommendations were provided compared to receiving no AI support. We also identified two socio-technical barriers to providing AI recommendations during time-critical medical events: (1) an accuracy-time trade-off in providing recommendations and (2) polarizing perceptions of recommendations between providers. We discuss three implications for developing AI-enabled decision support used in time-critical events, contributing to the limited research on human-AI interaction in this context. 

Decision support alerts have the potential to assist clinicians in determining appropriate interventions for critically injured patients. The design of these alerts is critical because it can impact their adoption and effectiveness. In this late-breaking work, we explore how decision support alerts should be designed for cognitive aids used in time- and safety-critical medical events. We conducted interviews with 11 trauma team leaders to elicit their thoughts and reactions to potential alert designs. From the findings, we contribute three implications for designing alerts for cognitive aids that support team-based, time-critical decision making and discuss how these implications can be further explored in future work.