Problem
Research questions and friction points this paper is trying to address.
AI healthcare chatbots
user-reported breakdowns
information infrastructure
privacy and security
digital health systems
AI Healthcare Chatbots as Information Infrastructure: A Large-Scale Study of User-Reported Breakdowns
🤖 AI Summary
This study addresses the challenges undermining user trust and health information accessibility in real-world AI-powered medical chatbots, which frequently suffer from access barriers, poor interaction experiences, billing and customer service issues, and privacy and security risks. Treating these systems as information infrastructures, the research analyzes over 15,000 user reviews across 59 applications through thematic modeling and interpretive analysis. It systematically identifies three critical failure modes: unreliable service delivery, suboptimal user interaction, and deficiencies in billing and customer support. Notably, privacy and security concerns are strongly associated with the most negative user experiences. By framing AI medical chatbots as socio-technical infrastructures, this work offers novel empirical insights and a conceptual foundation for enhancing the usability, accessibility, and trustworthiness of digital health systems.
📝 Abstract
AI healthcare chatbots are increasingly used to support health information seeking and self-management, yet their performance and impact on users remains to be studied. This study examines over 15,000 user reviews from 59 AI healthcare chatbot apps to explore how these systems function in everyday informational and emotional contexts. Topic modeling and interpretive analysis identify three recurring breakdowns: access barriers and service unreliability, user experience and interaction quality, and billing and customer support issues. Privacy and security concerns are associated with the most negative experiences. By framing AI healthcare chatbots as information infrastructures, our findings highlight how failures in access, usability, and trust affect users, offering actionable insights for designers, policymakers, and information professionals aiming to improve digital health systems.
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI healthcare chatbots
information infrastructure
user-reported breakdowns
privacy and security
topic modeling
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