From 911 to Hospital: Challenges and Opportunities for AI Integration in Emergency Medical Services
2026-06-15 • Human-Computer Interaction
Human-Computer Interaction
AI summaryⓘ
The authors studied how AI could help or disrupt Emergency Medical Services (EMS) workers during different stages of their fast-paced jobs. They interviewed 25 EMS clinicians to learn about current technology use and their hopes and worries about AI. The clinicians worried that AI might cause problems with teamwork, legal issues, and job control. Based on these findings, the authors suggest five ways to design AI that supports EMS teams without interfering with their important awareness and coordination.
Emergency Medical ServicesArtificial IntelligenceDistributed CognitionSituational AwarenessWorkflowTeam CoordinationTechnical ReliabilityProfessional AutonomyPrivacyHuman-AI Interaction
Authors
Emily Hou, Marelyn Gonzalez, Andrew L. Kun, Osnat Mokryn, Orit Shaer
Abstract
Artificial Intelligence (AI) is increasingly introduced into healthcare settings, yet its integration into fast-paced, high-pressure domains such as Emergency Medical Services (EMS) remains limited. EMS work unfolds across distinct stages, each characterized by different information needs, constraints, and forms of collaboration. Designing effective AI support requires understanding how AI interventions align with, or disrupt, EMS work across its different stages. We conducted semi-structured interviews with 25 EMS clinicians across the United States to examine how existing technologies currently support emergency services workflows and how they envision opportunities for, and concerns about, future AI-based support across different stages of emergency response. Our analysis reveals the cognitive, social, and procedural factors that enable EMS team coordination, which is grounded in situational awareness across distributed roles. EMS clinicians expressed significant concerns about how AI integration threatens this coordination mechanism across multiple dimensions: legal and privacy issues, technical reliability, contextual sensitivity, professional autonomy, and workflow friction. We propose five design principles for AI systems that augment distributed cognition and situational awareness, enabling EMS teams to deliver effective care under extreme constraints.