An Artificial Intelligence Framework for the Detection of Emotion Transitions in Telehealth Services

Recent advancements in Artificial intelligence (AI) have led to its widespread adoption in healthcare applications and services. The global pandemic has further hastened the integration of AI into telehealth services, such as service quality improvement and new models of care. This paper focuses on the service quality improvement of telehealth, specifically, cancer information and telephone support services, in terms of its human system interaction. We present an AI framework for the detection of patient emotions and emotion transitions based on call recordings of telehealth services. The call recordings are typically a conversation between the caller (patient or carer) and the healthcare practitioner (nurse or counsellor). All primary emotions are expressed across diverse topics during these calls. It is anticipated that the caller emotion state improves during the call due to the information and support received. The proposed AI framework is designed to detect emotions expressed at all stages of the call and based on these expressions, formulate the emotion transition during the call. We have evaluated the proposed AI framework on a large real-world dataset of 60,000 call recordings of cancer information and telephone support services provided by Cancer Council Victoria, Australia. The results confirm the effectiveness of this AI framework in detection of emotions and emotion transitions during the provision of telehealth services.