Human Factors in Healthcare
Human factors engineering has been identified by the Institute of Medicine and the National Academy of Engineering as an important tool for designing better healthcare systems to improve patient safety and quality of care. In this line of research, we examine how to optimize human performance through better understanding the behaviors of healthcare professional, their interactions with each other and with their environments.
Yang, X.J., Wickens, C.D., Park, T., Fong, L. & Siah, K.T.H. (2015). The effects of information access cost and overconfidence bias on medical residents’ pre-handover performance. Human factors, 57(8), 1459-1471.
Yang, X.J., Park, T., Siah, K.T.H., Ang, S.B.L., & Donchin, Y. (2015). One size fits all? Challenges faced by physicians during shift handovers in a hospital with high sender/recipient ratio. Singapore Medical Journal, 56(2), 109-115.
Siah, K.T.H., Yang, X.J., Yoshida, N., Ogiso, K., Hölttä-Otto, K., Naito, Y. (2015) Effects of experience, withdrawal speed and monitor size on colonoscopists’ visual detection of polyps. Proceedings of the 59th Human Factors and Ergonomics Society Annual Meeting, 471-475.
Trust-driven Human-Autonomy Interaction
The use of automated decision aids (ADAs) to assist human performance is growing at an unprecedented pace. As the capabilities of the automation advances, there is an increasing possibility that it might function actively to perceive and analyze information, make decisions, and execute actions. Ideally, with the assistance of automation, task performance of a human operator should increase. Unfortunately, performance gains are not always achieved, one of the reasons being the human operator’s inappropriate trust and dependence on automated technologies. The goal of this research is to examine how trust-reliability miscalibration affects human-automation team performance.
Yang, X.J., Unhelkar, V. V., Shah, J. A. (2017). Evaluating effects of user experience and system transparency on trust in automation. The 12th ACM/IEEE International Conference on Human-Robot Interaction.
Yang, X.J., Wickens, C.D., Hölttä-Otto, K. (2016). How users adjust trust in automation: Contrast effect and hindsight bias. Proceedings of the 60th Human Factors and Ergonomics Society Annual Meeting, 196-200.
Gombolay, M., Yang, X.J., Hayes, B., Seo, N., Wadhwania, S., Liu, Z., Yu, T., Shah, N., Golen, T. & Shah, J. A. (2016). Robotic Assistance in Coordination of Patient Care. Proceedings of Robotics: Science and Systems (RSS), Michigan, USA, June 18-22.
User Experience Design
Good user experience of a product or a service is a necessary component to ensure a product/service success. In this line of research, we develop quantitative methods to uncover latent user needs, and examine how to fulfill such needs through affective design.
Xu, Q., Jiao, R.J., Yang, X., Helander, M.G., Khalid, H.M., & Opperud, A. (2009). An Analytical Kano Model for Customer Need Analysis. Design Studies, 30(1), 87-110.
Yang, X., Wu, D., Zhou, F., & Jiao, J. (2008). Associate rule mining for affective product design. Proceedings of IEEE Conference on Industrial Engineering and Engineering Management 2008, 748-752.