Rachele Cattano

BACKGROUND: The increasing integration of chatbots across various sectors marks a significant shift in digital communication, and their role in healthcare makes no exception. This scoping review aims to systematically examine the role of chatbots in the perspective of organizational vaccine literacy, particularly in enhancing vaccine literacy and facilitating the dissemination of vaccine-related information, evaluating the potential of chatbots to transform vaccination communication strategies and improve health education outcomes. METHODS: This scoping review adhered to the Joanna Briggs Institute methodology and the PRISMA-ScR checklist. A systematic search of MEDLINE, Embase, Scopus, and PsycInfo was conducted from January 2020 to October 30, 2024, using keywords related to "chatbots" and "vaccination." Study selection involved a two-stage screening process, focusing on studies reporting the use of chatbots to improve vaccine literacy and uptake. Data were thematically analyzed and presented in a narrative format. RESULTS: Twenty-two studies were included in the review: these studies demonstrate the effectiveness of chatbots in enhancing vaccine literacy and acceptance, mainly focusing on COVID-19 but also addressing HPV and childhood vaccinations. They highlight chatbots' role in improving the vaccine-literate environment through countering misinformation and improving communication with healthcare professionals, showcasing their potential to significantly influence public health outcomes and their adaptability to diverse populations and geographic regions. CONCLUSIONS: These digital assistants could provide personalized and up-to-date information, improving not only knowledge but also attitudes and intentions towards vaccinations.

OBJECTIVES: Informed consent forms are important for assisting patients in making informed choices regarding medical procedures. Because of their lengthy nature, complexity and specialised terminology, consent forms usually prove challenging for the general public to comprehend. This pilot study aims to use Chat Generative Pretrained Transformer (ChatGPT), a large language model (LLM), to improve the readability and understandability of a consent form for vaccination. METHODS: The study was conducted in Italy, within the Central Tuscany Local Health Unit. Three different consent forms were selected and approved: the standard consent form currently in use (A), a new form totally generated by ChatGPT (B) and a modified version of the standard form created by ChatGPT (C). Healthcare professionals in the vaccination unit were asked to evaluate the consent forms regarding adequacy, comprehensibility and completeness and to give an overall judgement. The Kruskal-Wallis test and Dunn's test were used to evaluate the median scores of the consent forms across these variables. RESULTS: Consent forms A and C achieved the top scores in every category; consent form B obtained the lowest score. The median scores were 4.0 for adequacy on consent forms A and C and 3.0 on consent form B. Consent forms A and C received high overall judgement ratings with median scores of 4.0, whereas consent form B received a median score of 3.0. CONCLUSIONS: The findings indicate that LLM tools such as ChatGPT could enhance healthcare communication by improving the clarity and accessibility of consent forms, but the best results are seen when these tools are combined with human knowledge and supervision.