Self-Diagnosis through AI-enabled Chatbot-based Symptom Checkers: User Experiences and Design Considerations
This work addresses usability and functionality gaps in healthcare apps for users seeking self-diagnosis, but it is incremental as it identifies issues without proposing novel solutions.
The study investigated AI-enabled chatbot-based symptom checker apps, finding that they lack functions to support the full diagnostic process of an offline medical visit and that users perceive deficiencies in medical history support, symptom input flexibility, question comprehensibility, and disease/user group diversity.
Recently, there has been a growing interest in developing AI-enabled chatbot-based symptom checker (CSC) apps in the healthcare market. CSC apps provide potential diagnoses for users and assist them with self-triaging based on Artificial Intelligence (AI) techniques using human-like conversations. Despite the popularity of such CSC apps, little research has been done to investigate their functionalities and user experiences. To do so, we conducted a feature review, a user review analysis, and an interview study. We found that the existing CSC apps lack the functions to support the whole diagnostic process of an offline medical visit. We also found that users perceive the current CSC apps to lack support for a comprehensive medical history, flexible symptom input, comprehensible questions, and diverse diseases and user groups. Based on these results, we derived implications for the future features and conversational design of CSC apps.