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Designing AI-enabled decision support for fast-paced medical teams remains underexplored. We use Emergency Medical Services (EMS) as a critical use case to examine where AI can, and should not, support time-critical decision-making. We conducted participatory design workshops and formative user evaluations with EMS providers to elicit, prototype, and critique candidate AI application areas for EMS work. Providers identified several promising uses of AI: (1) AI-enabled information retrieval to accelerate access to protocols and medication references; (2) speech-based documentation support to reduce charting burden and generate draft records during care; (3) AI-generated patient “snapshots” that summarize relevant history from prior encounters; and (4) AI-based medication recognition to identify home medications and surface key safety information. In contrast, participants were cautious about clinical reasoning support that could be interpreted as diagnostic assertions, ambient “always-on” monitoring to flag errors or workflow deviations, and voice-based exchanges with AI in front of patients. Across various concepts, providers emphasized key factors critical for AI adoption in EMS, including seamless workflow fit, robustness to noisy environments, transparency, appropriate trust calibration, and the preservation of professional autonomy. We conclude with design implications for responsibly advancing the use of AI technology for EMS decision support. 

We study operads with trivial A-actions and prove an equivalence between the category of A-trivial operads and that of pseudo-graded-Perm associative algebras. As a consequence, we show that finitely generated A-trivial operads are right noetherian of integral Gelfand-Kirillov dimension and that every element in a prime A-trivial operad is central. 

Situational awareness (SA) is critical for Emergency Medical Services (EMS) providers as they operate in high-stakes, dynamic environments requiring rapid information processing and decision-making. While prior research has explored SA challenges in EMS, little is known about how visual attention patterns influence SA and clinical performance. This study employs eye-tracking technology to objectively assess how EMS providers allocate their visual attention during simulated pediatric emergency scenarios in urban and rural settings. We investigate variations in visual attention across experience levels, team structures, and task roles and examine differences between highand low-performing teams. Results reveal that high-performing teams demonstrate more frequent and evenly distributed visual scanning, whereas lower-performing teams exhibit a narrowed focus, increasing the risk of missing critical cues. Our findings underscore the need for training interventions and technology solutions to enhance SA and optimize EMS performance.