AI-Enabled Bioresponsive Clinical Decision Support Systems for Chronic Pain: User-Centered Approach

Abstract

The advancement of eye-tracking technologies has enabled the development of systems capable of detecting attention and cognitive states objectively and in real time. Biometric technologies that capture psychological measures, such as eye movements (EMs), have allowed user experience (UX) research to expand toward building smart bioresponsive tools. One area that may benefit from these advancements is chronic pain, where self-report methods are often limited in capturing the complex phenomenon of chronic pain experience in both research and practice. This has established a need for objective biomarkers that can support pain assessment. Pain literature suggests the use of EMs as potential biomarkers, as they reflect pain-related attentional patterns. This dissertation adopts a bioresponsive, UX research approach to explore the efficacy of using EMs to detect pain experience in individuals with and without chronic pain. A proof-of-concept AI tool was developed to detect chronic pain using only EMs from individuals with and without chronic pain, achieving an accuracy of 81%, thereby demonstrating the robustness of EMs as a potential biomarker for pain. To successfully evolve this proof of concept into a fully developed and effective Clinical Decision Support System (CDSS) for chronic pain treatment and management, it is essential to understand the needs of the healthcare professionals who will use the system. As a first step, traditional UX research methods were employed to conduct interviews with healthcare professionals involved in the treatment and management of chronic pain. Based on this research, six user personas, four representing doctors and two representing nurses, were developed to serve as a foundational guideline for the design of an initial CDSS prototype. The findings of this dissertation contribute to both UX research and pain science by presenting a comprehensive methodology for using eye movements (EMs) as input signals to an AI tool capable of detecting differences in attentional patterns toward pain-related stimuli. It also contributes to clinical practice by outlining design guidelines for developing an initial prototype of such an AI-based CDSS, grounded in the needs and workflows of healthcare professionals.