Automatic feedback through natural language processing using a chatbot-based simulated patient (PEPE) for the training of mental health professionals

Abstract

Mental, neurological, and substance use disorders are highly prevalent worldwide; however, it is estimated that between 75% and 95% of affected individuals lack access to treatment. Therefore, it is essential to promote the development of effective strategies for the training and evaluation of mental health professionals. There is evidence supporting the effectiveness of using standardized simulated patients to train healthcare professionals. One of the main challenges is the extent to which these simulations can authentically and validly represent real patients. The use of Artificial Intelligence has recently been explored to enhance the ecological validity of simulations through Natural Language Processing (NLP). NLP enables various applications, including chatbots and virtual assistants that can engage in natural, human-like conversations. In healthcare, chatbots can serve as valuable tools for training and providing feedback to professionals on a wide range of topics. This project aims to evaluate the impact of automated feedback—delivered through a simulated patient chatbot named PEPE—on the training of healthcare professionals at the Addiction Prevention Center, Faculty of Psychology, UNAM, in areas such as depression, anxiety, and substance abuse.