Explainable Clinical Decision Support: Towards Patient-Facing Explanations for Education and Long-Term Behavior Change

Abstract

There is an increasing shift towards the self-management of long-term chronic illness by patients in a home setting, supported by personal health electronic equipment. Among others, self-management requires comprehensive education on the illness, i.e., understanding the effects of nutritional, fitness, and medication choices on personal health; and long-term health behavior change, i.e., modifying unhealthy lifestyles that contribute to chronic illness. Smart health recommendations, generated using AI-based Clinical Decision Support (CDS), can guide patients towards positive nutritional, fitness, and health behavioral choices. Moreover, we posit that explaining these recommendations to patients, using Explainable AI (XAI) techniques, will effect education and positive behavior change. We present our work towards an explanation framework for rule-based CDS, called EXPLAIN (EXPLanations of AI In N3), which aims to generate human-readable, patient-facing explanations.