skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


This content will become publicly available on June 21, 2026

Title: ASTRID: A Robotic Tutor for Nurse Training to Reduce Healthcare-Associated Infections
The central line dressing change is a life-critical procedure performed by nurses to provide patients with rapid infusion of fluids, such as blood and medications. Due to their complexity and the heavy workloads nurses face, dressing changes are prone to preventable errors that can result in central line-associated bloodstream infections (CLABSIs), leading to serious health complications or, in the worst cases, patient death. In the post-COVID-19 era, CLABSI rates have increased, partly due to the heightened nursing workload caused by shortages of both registered nurses and nurse educators. To address this challenge, healthcare facilities are seeking innovative nurse training solutions to complement expert nurse educators. In response, we present the design, development and evaluation of a robotic tutoring system, ASTRID: the Automated Sterile Technique Review and Instruction Device. ASTRID, which is the outcome of a two-year participatory design process, is designed to aid in the training of nursing skills essential for CLABSI prevention. First, we describe insights gained from interviews with nurse educators and nurses, which revealed the gaps of current training methods and requirements for new training tools. Based on these findings, we outline the development of our robotic tutor, which interacts with nursing students, providing real-time interventions and summary feedback to support skill acquisition. Finally, we present evaluations of the system's performance and perceived usefulness, conducted in a simulated clinical setting with nurse participants. These evaluations demonstrate the potential of our robotic tutor in nursing education. Our work highlights the importance of participatory design for robotics systems, and motivates new avenues for foundational research in robotics.  more » « less
Award ID(s):
2326390
PAR ID:
10638062
Author(s) / Creator(s):
; ; ; ; ; ;
Editor(s):
Carlone, Luca; Kulic, Dana; Venture, Gentiane; Strader, Jared
Publisher / Repository:
Robotics: Science and Systems
Date Published:
ISBN:
979-8-9902848-1-4
Subject(s) / Keyword(s):
Robotics in Healthcare Human-Centered Robotics Intelligent Tutors Participatory Design
Format(s):
Medium: X
Location:
Los Angeles, CA, USA
Sponsoring Org:
National Science Foundation
More Like this
  1. An ongoing nurse labor shortage has the potential to impact patient care well-being in the entire healthcare system. Moreover, more complex and sophisticated nursing care is required today for patients in hospitals forcing hospital-based nurses to carry out frequent training and assessment procedures, both to onboard new nurses and to validate skills of existing staff that guarantees best practices and safety. In this paper, we recognize an opportunity for the development and integration of intelligent robot tutoring technology into nursing education to tackle the growing challenges of nurse deficit. To this end, we identify specific research problems in the area of human-robot interaction that will need to be addressed to enable robot tutors for nurse training. 
    more » « less
  2. Fernández-Alcántara, Manuel (Ed.)
    Background Nurse identification of patient deterioration is critical, particularly during the COVID-19 pandemic, as patients can deteriorate quickly. While the literature has shown that nurses rely on intuition to make decisions, there is limited information on what sources of data experienced nurses utilize to inform their intuition. The objectives of this study were to identify sources of data that inform nurse decision-making related to recognition of deteriorating patients, and explore how COVID-19 has impacted nurse decision-making. Methods In this qualitative study, experienced nurses voluntarily participated in focused interviews. During focused interviews, expert nurses were asked to share descriptions of memorable patient encounters, and questions were posed to facilitate reflections on thoughts and actions that hindered or helped their decision-making. They were also asked to consider the impact of COVID-19 on nursing and decision-making. Interviews were transcribed verbatim, study team members reviewed transcripts and coded responses, and organized key findings into themes. Results Several themes related to decision-making were identified by the research team, including: identifying patient care needs, workload management, and reflecting on missed care opportunities to inform learning. Participants (n = 10) also indicated that COVID-19 presented a number of unique barriers to nurse decision-making. Conclusions Findings from this study indicate that experienced nurses utilize several sources of information to inform their intuition. It is apparent that the demands on nurses in response to pandemics are heightened. Decision-making themes drawn from participants’ experiences can to assist nurse educators for training nursing students on decision-making for deteriorating patients and how to manage the potential barriers (e.g., resource constraints, lack of family) associated with caring for patients during these challenging times prior to encountering these issues in the clinical environment. Nurse practice can utilize these findings to increase awareness among experienced nurses on recognizing how pandemic situations can impact to their decision-making capability. 
    more » « less
  3. null (Ed.)
    The COVID-19 pandemic has placed an overwhelming strain on our Nation's ability to treat patients; the number of patients who need to be tested continues to rise. With nurses also becoming infected, the number of trained professionals who can perform tasks such as testing of patients along with providing care involving hooking up patients to ventilators continues to decrease as well. There is a need to explore the adoption of virtual computer based training mediums which will enable new nurses and others to be trained in safe and efficient procedures involving patients during this pandemic period. In this paper, the design of a VR based simulator based on Human Centered Computing (HCC) principles is discussed. The role of HCC factors such as affordance and cognitive load on the comprehension and scene understanding of nurses during training and the acquisition of knowledge of safety procedures and detailed steps (pertaining to nasal sample collection and use of ventilators on patients) has been studied with the involvement of nurse and nurse trainee participants. Adoption of a participatory design approach involving experts (nurses, doctors involved in covid-19 testing and treatment) has provided a foundational basis for design of the training environments and assessment activities. Formal information centric process models of the nasal swabbing procedures and ventilator hookup tasks were created using the engineering Enterprise Modeling Language (eEML). The preliminary results from the assessment activities indicate the positive impact of such HCC based 3Dsimulators in such training of first responders. 
    more » « less
  4. Abstract IntroductionIn order to be positioned to address the increasing strain of burnout and worsening nurse shortage, a better understanding of factors that contribute to nursing workload is required. This study aims to examine the difference between order‐based and clinically perceived nursing workloads and to quantify factors that contribute to a higher clinically perceived workload. DesignA retrospective cohort study was used on an observational dataset. MethodsWe combined patient flow, nurse staffing and assignment, and workload intensity data and used multivariate linear regression to analyze how various shift, patient, and nurse‐level factors, beyond order‐based workload, affect nurses' clinically perceived workload. ResultsAmong 53% of our samples, the clinically perceived workload is higher than the order‐based workload. Factors associated with a higher clinically perceived workload include weekend or night shifts, shifts with a higher census, patients within the first 24 h of admission, and male patients. ConclusionsThe order‐based workload measures tended to underestimate nurses' clinically perceived workload. We identified and quantified factors that contribute to a higher clinically perceived workload, discussed the potential mechanisms as to how these factors affect the clinically perceived workload, and proposed targeted interventions to better manage nursing workload. Clinical RelevanceBy identifying factors associated with a high clinically perceived workload, the nurse manager can provide appropriate interventions to lighten nursing workload, which may further reduce the risk of nurse burnout and shortage. 
    more » « less
  5. null (Ed.)
    Background According to the US Bureau of Labor Statistics, nurses will be the largest labor pool in the United States by 2022, and more than 1.1 million nursing positions have to be filled by then in order to avoid a nursing shortage. In addition, the incidence rate of musculoskeletal disorders in nurses is above average in comparison with other occupations. Robot-assisted health care has the potential to alleviate the nursing shortage by automating mundane and routine nursing tasks. Furthermore, robots in health care environments may assist with safe patient mobility and handling and may thereby reduce the likelihood of musculoskeletal disorders. Objective This pilot study investigates the perceived ease of use and perceived usefulness (acceptability) of a customized service robot as determined by nursing students (as proxies for nursing staff in health care environments). This service robot, referred to as the Adaptive Robotic Nurse Assistant (ARNA), was developed to enhance the productivity of nurses through cooperation during physical tasks (eg, patient walking, item fetching, object delivery) as well as nonphysical tasks (eg, patient observation and feedback). This pilot study evaluated the acceptability of ARNA to provide ambulatory assistance to patients. Methods We conducted a trial with 24 participants to collect data and address the following research question: Is the use of ARNA as an ambulatory assistive device for patients acceptable to nurses? The experiments were conducted in a simulated hospital environment. Nursing students (as proxies for nursing staff) were grouped in dyads, with one participant serving as a nurse and the other acting as a patient. Two questionnaires were developed and administrated to the participants based on the Technology Acceptance Model with respect to the two subscales of perceived usefulness and perceived ease of use metrics. In order to evaluate the internal consistency/reliability of the questionnaires, we calculated Cronbach alpha coefficients. Furthermore, statistical analyses were conducted to evaluate the relation of each variable in the questionnaires with the overall perceived usefulness and perceived ease of use metrics. Results Both Cronbach alpha values were acceptably high (.93 and .82 for perceived usefulness and perceived ease of use questionnaires, respectively), indicating high internal consistency of the questionnaires. The correlation between the variables and the overall perceived usefulness and perceived ease of use metrics was moderate. The average perceived usefulness and perceived ease of use metrics among the participants were 4.13 and 5.42, respectively, out of possible score of 7, indicating a higher-than-average acceptability of this service robot. Conclusions The results served to identify factors that could affect nurses’ acceptance of ARNA and aspects needing improvement (eg, flexibility, ease of operation, and autonomy level). 
    more » « less