Beyond Self-diagnosis: How a Chatbot-based Symptom Checker Should Respond

Abstract

Chatbot-based symptom checker (CSC) apps have become increasingly popular in healthcare. These apps engage users in human-like conversations and offer possible medical diagnoses. The conversational design of these apps can significantly impact user perceptions and experiences, and may influence medical decisions users make and the medical care they receive. However, the effects of the conversational design of CSCs remain understudied, and there is a need to investigate and enhance users’ interactions with CSCs. In this paper, we conducted a two-stage exploratory study using a human-centered design methodology. We first conducted a qualitative interview study to identify key user needs in engaging with CSCs. We then performed an experimental study to investigate potential CSC conversational design solutions based on the results from the interview study. We identified that emotional support, explanations of medical information, and efficiency were important factors for users in their interactions with CSCs. We also demonstrated that emotional support and explanations could affect user perceptions and experiences, and they are context-dependent. Based on these findings, we offer design implications for CSC conversations to improve the user experience and health-related decision-making.

Evaluation of the Current State of Chatbots for Digital Health: Scoping Review

“…Addressing this, Hwang et al [ 37 ] emphasized the need for chatbots to transparently convey data types used in model training and the generation of diagnostic recommendations. Recent research by You et al [ 38 ] aligns with this, advocating for chatbots to provide explanatory insights into diagnostic outcomes. Although diagnostic chatbots hold promise as decision-support mechanisms for health care experts, further research is essential to gauge users’ preferences, especially when trust has been established through interactions with diagnostic chatbots.…”

Critical review of self‐diagnosis of mental health conditions using artificial intelligence

“…The increasing prevalence of AI-assisted self-diagnosis in mental health reflects broader societal changes in how individuals engage with their emotional wellbeing (Stuart, 2016;Torous et al, 2021). The internet offers a plethora of easily accessible resources such as online assessments, symptom checkers, and AI-powered chatbots, making self-diagnosis an appealing option for those seeking quick insights (You et al, 2023;You & Gui, 2020).…”

An Overview of Chatbot-Based Mobile Mental Health Apps: Insights From App Description and User Reviews (Preprint)

“…Chatbot interventions are effective in increasing physical activity, achieving relevant weight loss, and improving diet [38][39][40] by sending daily check-in reminders [41] and offering relevant resources [40]. They are also sufficiently sophisticated to interact with users through daily adaptive little chats and show progress toward goals using analytics and graphs to encourage self-reflection [42].…”