FW-HTF-R: Biometrics and AI to Support Expert Nurse Decision-Making

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Healthcare workers routinely make fast life-saving decisions. Failures in decision-making can threaten the welfare of the patient and the worker. High-performing, expert nurses utilize decision-making strategies such as prioritizing important information, maintaining a big-picture view of the situation, and executing effective clinical skills to provide safe patient care. New or inexperienced nurses often lack the experiences to support expert decision-making. This project is developing sensing technology and algorithms that can be used in real-time decision-support for training nursing students. The technology could improve the skills and real-time decision making of these students, especially in distracting and risky situations. The project is also creating teaching modules for high school students and inter-disciplinary university courses that integrate fields such as computer science, human factors engineering, and nursing. The project vision is to augment the nursing workforce with technology that will support nurse decision-making in real time while nurses actively care for patients, work with other caregivers, and carry out their diverse responsibilities. To achieve this goal, the project is developing real-time biometrics that can assess the cognitive state of workers during their complex, dynamic healthcare work. The project will also study expert nurse behaviors to learn efficient decision-making patterns that could be used to guide novice nurses. Imitation (computer) learning of expert behavior will be used to train the guides. In the future, the developed system could also support rapid recognition of patient risk by integrating user sensing and decision-support behaviors with just-in-time wearables designed for healthcare workplaces.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.