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Towards Collaborative Crash Cart Robots that Support Clinical Teamwork

Healthcare workers (HCWs) face many challenges during bedside care that impede team collaboration and often lead to poor patient outcomes. Robots have the potential to support medical decision0making, help identify medical errors, and deliver supplies to clinical teams in a timely manner. However, there is a lack of knowledge about using robots to support clinical team dynamics despite being used in surgery, healthcare operations, and other applications. To address this gap, we engaged in a co-design process of robots that support clinical teamwork. We collaboratively explore how robots can support clinical teamwork with HCWs.This collaborative processincludes understanding the challenges they face during bedside care and envisioning robots that can help mitigate these issues. Our study shows that robots can act as a shared mental model for clinical teams, help close communication gaps, and provide procedural steps to assist HCWs with limited in-hospital experience. This research highlights new ways HRI researchers can deploy robots in acute care settings, as well as defne appropriate levels of autonomy to maintain human control in safety-critical settings. [HRI]




Robot Navigation in Risky, Crowded Environments: Understanding Human Preferences

The effective deployment of robots in risky and crowded environments (RCE) requires the specification of robot plans that are consistent with humans’ behaviors. As is well known, humans perceive uncertainty and risk in a biased way, which can lead to a diversity of actions and expectations when interacting with others. To gain a better understanding of these behaviors, this work presents new data that aims to verify how these biases translate into a human navigational setting. More precisely, we conduct a novel study that recreates a COVID-19 pandemic grocery shopping scenario and asks participants to select among various paths with different levels of} time-risk tradeoffs. The data shows that participants exhibit a variety of path preferences: from risky and urgent to safe and relaxed. To model users’ decision making, we evaluate three popular risk models and found that CPT captures people’s decisions more accurately, corroborating previous theoretical results that CPT is more expressive and inclusive. We also find that people’s self assessments of risk and time-urgency do not correlate with their path preferences in RCEs. Finally, we conduct thematic analysis of custom open-ended questions to gauge interest and preferences of navigational Explainable AI (XAI) in robots. A large majority also showed interest in understanding robot’s intention (path plans and decisions) through various modalities like speech, touchscreen and gestures. Our work provides crucial XAI design insights for deployment of robots in RCEs.



Hospitals of the Future: Designing Interactive Robotic Systems for Resilient Emergency Departments

The Emergency Department (ED) is a stressful, safety-critical environment, which is overcrowded, noisy, chaotic, and understaffed. The built environment plays a key role in patient outcomes, experiences, and the mental health of healthcare workers (HCWs). However, once a space is built it is difficult to change it; so the modularity and adaptability of new technologies such as robots could potentially help stakeholders mitigate some of these challenges; yet, there is a lack of research in this area, particularly in the ED. In this paper, we address this gap by engaging HCWs in a research-through-design process, utilizing design fiction, to envision a future resilient ED. We hope our work inspires further exploration into using new technologies to reimagine and reconfigure the built environment to support resilient hospitals. [PDF][Design Catalog]



REGROUP: A Robot-Centric Group Detection and Tracking System

To facilitate HRI’s transition from dyadic to group interaction, new methods are needed for robots to sense and understand team behavior. We introduce the Robot-Centric Group Detection and Tracking System (REGROUP), a new method that enables robots to detect and track groups of people from an ego-centric perspective using a crowd-aware, tracking-by-detection approach. Our system employs a novel technique that leverages person re-identification deep learning features to address the group data association problem. We show that REGROUP outperformed three group detection methods by up to 40% in terms of precision and up to 18% in terms of recall. Also, we show that REGROUP’s group tracking method outperformed three state-of-the-art methods by up to 66% in terms of tracking accuracy and 20% in terms of tracking precision. [PDF][Video][GitHub]



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.  In this paper, we introduce the Safety-Critical Deep Q-Network (SafeDQN) system, a new acuity-aware navigation system for mobile robots. 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. [PDF]


Contextualizing Robots for Emergency Medicine

The emergency department (ED) is a safety-critical environment in which mistakes can be deadly and providers are overburdened. Well-designed and contextualized robots could be an asset in the ED by relieving providers of non-value added tasks and enabling them to spend more time on patient care. To support future work in this application domain, in this paper, we characterize ED staff workflow and patient experience, and identify key considerations for robots in the ED, including safety, physical and behavioral attributes, usability, and training. Then, we discuss the task representation and data needed to situate the robot in the ED, based on this domain knowledge. [PDF]



Coordinating Clinical Teams: Using Robots to Empower nurses to Stop the line

Patient safety errors account for over 400,000 preventable deaths annually in US hospitals alone, 70% of which are caused by team communication breakdowns, stemming from hierarchical structures and asymmetrical power dynamics between physicians, nurses, patients, and others. Nurses are uniquely positioned to identify and prevent these errors, but they are often penalized for speaking up, particularly when physicians are responsible. Nevertheless, empowering nurses and building strong interdisciplinary teams can lead to improved patient safety and outcomes. Thus, our group has been developing a series of intelligent systems that support teaming in safety-critical settings, Robot-Centric Team Support System (RoboTSS), and recently developed a group detection and tracking system for collaborative robots. In this paper, we explore how RoboTSS can be used to empower nurses in interprofessional team settings, through a three month-long, collaborative design process with nurses across five US-based hospitals. [PDF]