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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. 

Almost half of the preventable deaths in emergency care can be associated with a medical delay. Understanding how clinicians experience delays can lead to improved alert designs to increase delay awareness and mitigation. In this paper, we present the findings from an iterative user-centered design process involving 48 clinicians to develop a prototype alert system for supporting delay awareness in complex medical teamwork such as trauma resuscitation. We used semi-structured interviews and card-sorting workshops to identify the most common delays and elicit design requirements for the prototype alert system. We then conducted a survey to refine the alert designs, followed by near-live, video-guided simulations to investigate clinicians' reactions to the alerts. We contribute to CSCW by designing a prototype alert system to support delay awareness in time-critical, complex teamwork and identifying four mechanisms through which teams mitigate delays.