Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: May 6, 2020
Date Accepted: Oct 2, 2020
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Utilization of Self-Diagnosis Health Chatbots in the Wild: A Case Study
ABSTRACT
Background:
Artificial intelligence (AI)-empowered chatbots are increasingly being used in healthcare, but most chatbots are designed for a specific population and evaluated in controlled settings. There is little research documenting how health consumers (e.g., patients and their caregivers) use chatbots for self-diagnosis in real world scenarios.
Objective:
This research aims to understand how health chatbots are used in the wild, what issues and barriers exist in the usage, as well as how to improve the user experience of this novel technology.
Methods:
In this study, we employed a data-driven approach to analyze the system log of a widely deployed self-diagnosis chatbot in China. Our dataset consists of 47,684 consultation sessions initiated by 16,519 users over a six-month period. The log data includes a variety of information, including users’ non-identifiable demographic information, consultation details, diagnostic reports, and user feedback. We conducted both statistical analysis and content analysis to analyze this heterogenous dataset.
Results:
The chatbot users span all age groups, including middle-aged and older adults. They consulted the chatbot on a wide range of medical conditions, including those that often entail considerable privacy and social stigma issues. Furthermore, we distilled two prominent issues in the chatbot use: 1) a considerable number of users dropped out at a certain point of their consultation sessions, and 2) some users pretended to have health concerns and used the chatbot for non-therapeutic purpose. Finally, we identified a set of user concerns regarding the use of the chatbot, including insufficient actionable information and perceived inaccurate diagnostic suggestion.
Conclusions:
Although health chatbots are considered as a convenient tool for enhancing patient-centered care, there are issues and barriers that impede the optimal use of this novel technology. Designers and developers should employ user-centered approaches to address issues and user concerns in order to achieve the best uptake and utilization. We concluded this paper by discussing several design implications, including making the chatbots more informative, easy-to-use, and trustworthy, as well as improving the onboarding experience to enhance user engagement.
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