Promise and Caution: Mapping Opportunities for AI Decision Support in Emergency Medical Services.

Abstract

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.