CAR-EM: A Synthesis-Based Clinically Assistive Robot System for Emergency Medicine

Emergency departments (EDs) are fast-paced, dynamic, safety-critical spaces where clinicians are overworked and underpaid. To support clinicians, researchers are exploring the contextualization and development of clinically assistive robots (CARs) that can assume non-critical tasks to reduce clinician overload. In this paper, we introduce CAR-EM (Clinically Assistive Robot System for Emergency Medicine), collaboratively developed with ED clinicians. CAR-EM includes an autonomous robot and a task specification interface. It completes tasks by leveraging control synthesis, a framework that automatically transforms high-level tasks into control while providing guarantees and feedback. We conducted a feasibility study across two different hospital EDs, where interprofessional clinicians tasked the robot to perform patient assessments and item deliveries. Clinicians found the system easy to use, and particularly helpful to offload busywork. This work demonstrates control synthesis as a feasible tool to develop autonomy for robots in safety-critical spaces, and identifies considerations for failure interventions. We also discuss ethical considerations for deploying robots in hospitals, including healthcare worker displacement and work disruption. Thus, our work: 1) highlights the unique requirements of situating robots in real world hospital EDs, and 2) demonstrates a novel approach leveraging guarantees and feedback from control synthesis methods to successfully implement context-specific CAR behaviors. Through this work, we aim to further research for safer and more reliable robots in real world, uncertain environments.