MedNgage: A Dataset for Understanding Engagement in Patient-Nurse Conversations
This work addresses the need for AI systems to better understand patient engagement in healthcare conversations, which is crucial for improving patient care, but it is incremental as it primarily focuses on dataset creation and baseline modeling.
The paper tackles the problem of understanding patient engagement in healthcare conversations by introducing MedNgage, a dataset of patient-nurse conversations annotated with socio-affective and cognitive engagement categories, showing a positive correlation between engagement and symptom management outcomes and demonstrating that fine-tuned transformer models can reliably predict engagement classes.
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 spans). Through statistical analysis of the data that is annotated using our framework, we show a positive correlation between patient symptom management outcomes and their engagement in conversations. Additionally, we demonstrate that pre-trained transformer models fine-tuned on our dataset can reliably predict engagement classes in patient-nurse conversations. Lastly, we use LIME (Ribeiro et al., 2016) to analyze the underlying challenges of the tasks that state-of-the-art transformer models encounter. The de-identified data is available for research purposes upon request.