MedNgage: A Dataset for Understanding Engagement in Patient-Nurse Conversations

Patients who effectively manage their symptoms often demonstrate higher levels of engagement in conversations and interventions with healthcare practitioners. This engagement is multifaceted, encompassing cognitive and socio-affective dimensions. Consequently, it is crucial for AI systems to understand the engagement in natural conversations between patients and practitioners to better contribute toward patient care. In this paper, we present a novel dataset (MedNgage), which consists of patient-nurse conversations about cancer symptom management. We manually annotate the dataset with a novel framework of categories of patient engagement from two different angles, namely: i) socio-affective (3.1K spans), and ii) cognitive use of language (1.8K Figure 1: Our dataset contains patient-nurse conversations spans). Through statistical analysis of the annotated with engagement derived from socioaffective data that is annotated using our framework, and cognitive use of language. We hypothesize we show a positive correlation between patient that patients who have high engagement tend to symptom management outcomes and their engagement have better symptom control. in conversations.