Minimizing Errors in the Nursing Profession with Technology-Enhanced Education and Training

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Our research aims to address two gaps in the current state of the art and practice in nursing education. First, while there are some studies on the association between medical errors and the underlying cognitive mechanisms, the knowledge on the linkage between the cognitive mechanisms and different types of medical errors under various conditions is far from adequate, and even less so on how to reduce medical errors by better equipping future nurses with cognitive capabilities and by using technical interventions when possible. Second, while human patient simulation (HPS) has been widely adopted in nursing programs in the US, its effectiveness is severely handicapped by the lack of reliable and efficient methods to provide objective assessment and feedback to students. To address the first gap, we propose to systematically identify common types of medical errors and discover the underlying cognitive mechanisms based on gaze tracking for patient safety related activities during HPS under a set of carefully crafted conditions. To address the second gap, we propose to develop an automated method to objectively assess the performance of students during HPS using computer vision and to provide realtime and on-demand offline feedback to students based on the data collected during HPS and predefined rules.