Social Navigation for Mobile Robots in the Emergency Department

The emergency department (ED) is a safety-critical environment in which healthcare workers (HCWs) are overburdened, overworked, and have limited resources, especially during the COVID-19 pandemic. One way to address this problem is to explore the use of robots that can support clinical teams, e.g., to deliver materials or restock supplies. However, due to EDs being overcrowded, and the cognitive overload HCWs experience, robots need to understand various levels of patient acuity so they avoid disrupting care delivery. In this paper, we introduce the Safety-Critical Deep Q-Network (SafeDQN) system, a new acuity-aware navigation system for mobile robots. SafeDQN is based on two insights about care in EDs: high-acuity patients tend to have more HCWs in attendance and those HCWs tend to move more quickly. We compared SafeDQN to three classic navigation methods, and show that it generates the safest, quickest path for mobile robots when navigating in a simulated ED environment. We hope this work encourages future exploration of social robots that work in safety-critical, human-centered environments, and ultimately help to improve patient outcomes and save lives.

Iris: A Low-Cost Telemedicine Robot to Support Healthcare Safety and Equity During a Pandemic

The COVID-19 pandemic exacerbated problems of already overwhelmed healthcare ecosystems. The pandemic worsened long-standing health disparities and increased stress and risk of infection for frontline healthcare workers (HCWs). Telemedical robots offer great potential to both improve HCW safety and patient access to high-quality care, however, most of these systems are prohibitively expensive for under-resourced healthcare organizations, and difficult to use. In this paper, we introduce Iris, a low-cost, open hardware/open software telemedical robot platform. We co-designed Iris with front-line HCWs to be usable, accessible, robust, and well-situated within the emergency medicine (EM) ecosystem. We tested Iris with 15 EM physicians, who reported high usability, and provided detailed feedback critical to situating the robot within a range of EM care delivery contexts, including under-resourced ones. Based on these findings, we present a series of concrete design suggestions for those interested in building and deploying similar systems. We hope this will inspire future work both in the current pandemic and beyond. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.