Virtual Patients to Train Communication Skills of Healthcare Providers

Abstract

Virtual patients (VPs) offer an affordable and feasible method to train individuals when compared to patient-actors. They can provide training on communication skills, such as motivational interviewing and conflict resolution, and often facilitate a change in patients’ thinking and emotional states. However, few studies have focused on VPs with cognitive and emotional states and internal schema or rules that govern them. In this research, we aim to design and empirically study a VP training system with a mental model to enrich the interactions and training. With such a model, the learning environment has the potential to generate valuable feedback and guidance for the learner based on the states of the VP. We started by examining systems aimed at training individuals with a virtual agent that simulates a person in a social situation (e.g., a virtual customer to train salespersons). We developed an architecture for these systems, defined the current interaction approaches, and will discuss the main aspects of the training system architecture and various technological approaches. Here, we consider the potential solutions proposed in adjacent virtual-agent domains. Based on our findings, we will model the VP’s reasoning to improve trainees’ communication skills and investigate how helpful feedback and guidance could be generated from these mental models at run time. The main contribution of our work will be the build of an empirically grounded virtual patient with a mental model as a training system.