Rapidly Built Medical Crash Cart! Lessons Learned and Impacts on High-Stakes Team Collaboration in the Emergency Room
Not on the map. Why?
Abstract (arXiv)
Designing robots to support high-stakes teamwork in emergency settings presents unique challenges, including seamless integration into fast-paced environments, facilitating effective communication among team members, and adapting to rapidly changing situations. While teleoperated robots have been successfully used in high-stakes domains such as firefighting and space exploration, autonomous robots that aid highs-takes teamwork remain underexplored. To address this gap, we conducted a rapid prototyping process to develop a series of seemingly autonomous robot designed to assist clinical teams in the Emergency Room. We transformed a standard crash cart--which stores medical equipment and emergency supplies into a medical robotic crash cart (MCCR). The MCCR was evaluated through field deployments to assess its impact on team workload and usability, identified taxonomies of failure, and refined the MCCR in collaboration with healthcare professionals. Our work advances the understanding of robot design for high-stakes, time-sensitive settings, providing insights into useful MCCR capabilities and considerations for effective human-robot collaboration. By publicly disseminating our MCCR tutorial, we hope to encourage HRI researchers to explore the design of robots for high-stakes teamwork.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Feb 2025.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2025: 4.
4 papers.
- 2025From Virtual Agents to Robot Teams: A Multi-Robot Framework Evaluation in High-Stakes Healthcare Contextcited by 3
- 2025Skill-Aligned Fairness in Multi-Agent Learning for Collaboration in Healthcarecited by 3
- 2025Simulating Teams with LLM Agents: Interactive 2D Environments for Studying Human-AI Dynamicscited by 0
- 2025AdaFair-MARL: Enforcing Adaptive Fairness Constraints in Multi-Agent Reinforcement Learningcited by 0
- 2025AdaFair-MARL: Enforcing Adaptive Fairness Constraints in Multi-Agent Reinforcement Learning18 Nov 2025
- 2025Simulating Teams with LLM Agents: Interactive 2D Environments for Studying Human-AI Dynamics9 Oct 2025
- 2025Skill-Aligned Fairness in Multi-Agent Learning for Collaboration in Healthcare26 Aug 2025
- 2025From Virtual Agents to Robot Teams: A Multi-Robot Framework Evaluation in High-Stakes Healthcare Context4 Jun 2025