Exploring and promoting diagnostic transparency and explainability in online symptom checkers

Chun Hua Tsai, Yue You, Xinning Gui, Yubo Kou, John M. Carroll

Research output: Chapter in Book/Report/Conference proceedingConference contribution

53 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationCHI 2021 - Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Subtitle of host publicationMaking Waves, Combining Strengths
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450380966
DOIs
StatePublished - May 6 2021
Event2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021 - Virtual, Online, Japan
Duration: May 8 2021May 13 2021

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021
Country/TerritoryJapan
CityVirtual, Online
Period5/8/215/13/21

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design

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