Collaborative Research: FW-HTF-R: The Future of Robot-Assisted Nursing: Interactive AI Frameworks for Upskilling Nurses and Customizing Robot Assistance

The specific objectives of the Future of Work at the Human-Technology Frontier program are (1) to facilitate convergent research that employs the joint perspectives, methods, and knowledge of computer science, engineering, learning sciences, research on education and workforce training, and social, behavioral, and economic sciences; (2) to encourage the development of a research community dedicated to designing intelligent technologies and work organization and modes inspired by their positive impact on individual workers, the work at hand, the way people learn and adapt to technological change, creative and supportive workplaces (including remote locations, homes, classrooms, or virtual spaces), and benefits for social, economic, and environmental systems at different scales; (3) to promote deeper basic understanding of the interdependent human-technology partnership to advance societal needs by advancing design of intelligent work technologies that operate in harmony with human workers, including consideration of how adults learn the new skills needed to interact with these technologies in the workplace, and by enabling broad workforce participation, including improving accessibility for those challenged by physical or cognitive impairment; and (4) to understand, anticipate, and explore ways of mitigating potential risks arising from future work at the human-technology frontier.

Nursing is a discipline of knowledge and practice focused on delivering patient-centered care. As the healthcare providers who are with hospitalized patients 24 hours a day, 7 days a week, registered nurses are crucial for ensuring patient safety and delivering patient-centered care. Alarmingly, however, the U.S. is experiencing a dire nursing shortage, which is projected to significantly worsen in the next decade.

As a result, nurses have limited time for patient-centered care. To continue providing high-quality patient care, healthcare leaders are scrambling for solutions, often turning to technological aids such as artificial intelligence (AI) and robots. On one hand, AI-enabled robotic assistants hold the potential to support nurses in some routine tasks, allowing them to spend more time on patient care and improving patient outcomes.

On the other hand, the introduction of robots also brings forth several areas of concerns such as increase in nursing workload due to required training and maintenance to use these complex systems. This planning project will develop a multi-disciplinary research agenda to systematically introduce nurses to AI-enabled robotics technology, with the goal of ensuring that the integration of robots in the future of nursing brings long-term positive impact.

Seamless integration of robotic assistants in the nursing workflow requires (a) careful scientific study of the impact of robots on nursing workload, (b) design of nurse-centered frameworks for customizing robotic assistance, and (c) continued development of nurse training content and practices. To develop a research agenda that expands on these thrusts, the project will involve three main planning activities.

First, the project will bring together technologists, healthcare professionals, social scientists, and educators through workshops and stakeholder meetings. These meetings will facilitate cross-disciplinary collaborations and formation of a convergent research team. Second, through participatory design, the project team will create a testbed for robot-assisted nursing, where multiple disciplines can brainstorm, prototype, and evaluate solutions for robot-assisted nursing.

Third, the project team will use the testbed for generating preliminary data to assess the impact of robotic assistants on nursing workload. The data collection will be conducted with the help of nurse volunteers of varied experience, and in a combination of simulated and physical environments.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.