Conversational agents are increasingly being used for health interventions but indexing stories for these agents remains a challenge. The authors propose a method for automatically indexing health stories used in conversational agents with three key elements - concepts, emotions, and behaviors. Their method uses natural language processing techniques like keyword extraction and sentiment analysis to analyze stories and produce indexes with these three elements in a standardized format that can benefit future health conversational agents.