Chronic Pain and Eye Movements: A NeuroIS Approach to Designing Smart Clinical Decision Support Systems

Abstract

AbstractThe pressing need for objective measures in the evaluation of chronic pain both in research and practice highlights the role that neuro information systems (NeuorIS) research plays in designing smart clinical decision support systems. A first step in such a research agenda is identifying practical stimuli-task paradigms that can reliably detect chronic pain from physiological measures such as eye movements. In this study, we propose and test a new stimuli-task paradigm. Our results show that our proposed stimuli-task paradigm can detect differences in information processing behavior of people with and without chronic pain. The results also show that our proposed stimuli-task paradigm can reliably predict a person’s reported subjective pain experience from his/her eye movements. These findings provide support for our proposed stimuli-task paradigm. They also show that the eye-tracking variables that we selected to test our proposed paradigm are effective in capturing the impact of chronic pain on visual attention and suggest that eye movements have the potential to serve as reliable biomarkers of chronic pain. In other words, our results provide support for the potential of eye movements to facilitate the development of smart information systems that can detect the presence and/or the severity of chronic pain from an individual’s ocular behavior.