Paper Info
Title
Exploring and Promoting Diagnostic Transparency and Explainability in Online Symptom Checkers
Abstract
ABSTRACT Online symptom checkers (OSC) are widely used intelligent systems in health contexts such as primary care, remote healthcare, and epidemic control. OSCs use algorithms such as machine learning to facilitate self-diagnosis and triage based on symptoms input by healthcare consumers. However, intelligent systems’ lack of transparency and comprehensibility could lead to unintended consequences such as misleading users, especially in high-stakes areas such as healthcare. In this paper, we attempt to enhance diagnostic transparency by augmenting OSCs with explanations. We first conducted an interview study (N=25) to specify user needs for explanations from users of existing OSCs. Then, we designed a COVID-19 OSC that was enhanced with three types of explanations. Our lab-controlled user study (N=20) found that explanations can significantly improve user experience in multiple aspects. We discuss how explanations are interwoven into conversation flow and present implications for future OSC designs.
Year
DOI
Venue
2021
10.1145/3411764.3445101
Conference on Human Factors in Computing Systems
Keywords
DocType
Citations 
Symptom Checker, COVID-19, Explanation, Health, Transparency
Conference
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
CH Tsai100.34
Youping Li221.39
X Gui301.01
Yubo Kou44415.42
John M. Carroll549501233.96