A Conversational Agent for Providing Personalized Preexposure Prophylaxis (PrEP) Support: Protocol for Chatbot Implementation and Evaluation
Abstract
Background: Chatbots have the potential to reduce barriers to preexposure prophylaxis (PrEP), including lack of awareness, misconceptions, and stigma, by providing anonymous and continuous support. However, in the context of PrEP, chatbots are still nascent; they lack personalized informational expertise, peer experiential expertise, and human-like emotional support to facilitate future PrEP uptake. These personalized and relatable forms of support are crucial for increasing engagement, influencing health decisions, and fostering resilience and well-being.
Objective: In this paper, we describe the iterative development and evaluation plans of a retrieval-augmented generation (RAG) chatbot for providing personalized information, peer experiential expertise, and human-like emotional support to PrEP candidates.
Methods: We used an iterative design process consisting of 2 phases: prototype conceptualization and iterative chatbot development. In the conceptualization phase, we identified real-world PrEP needs and designed a functional dialogue flow diagram for PrEP support. Chatbot development included developing 2 components: a query preprocessor and a RAG module. The preprocessor uses the Segment Any Text (SAT) tool (developed by Markus Frohmann, Igor Sterner, Ivan Vulić, Benjamin Minixhofer, and Markus Schedl) for query segmentation and a Gemma 2 fine-tuned support classifier to identify informational, emotional, and contextual data from real-world queries. To implement the…
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