Classification of Failures in the Perception of Conversational Agents (CAs) and their Implications on Patient Safety

Haris Aftab, Syed Hammad Hussain Shah, Ibrahim Habli

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

Abstract

The use of Conversational agents (CAs) in healthcare is an emerging field. These CAs seem to be effective in accomplishing administrative tasks, e.g. providing locations of care facilities and scheduling appointments. Modern CAs use machine learning (ML) to recognize, understand and generate a response. Given the criticality of many healthcare settings, ML and other component errors may result in CA failures and may cause adverse effects on patients. Therefore, in-depth assurance is required before the deployment of ML in critical clinical applications, e.g. management of medication dose or medical diagnosis. CA safety issues could arise due to diverse causes, e.g. related to user interactions, environmental factors and ML errors. In this paper, we classify failures of perception (recognition and understanding) of CAs and their sources. We also present a case study of a CA used for calculating insulin dose for gestational diabetes mellitus (GDM) patients. We then correlate identified perception failures of CAs to potential scenarios that might compromise patient safety.
Original languageEnglish
Title of host publicationPublic Health and Informatics
Subtitle of host publicationProceedings of MIE 2021
EditorsJohn Mantas, Lăcrămioara Stoicu-Tivadar, Catherine Chronaki Chronaki, Arie Hasman, Patrick Weber, Parisis Gallos, Mihaela Crişan-Vida, Emmanouil Zoulias, Oana Sorina Chirila
PublisherIOS Press
Pages659-663
Number of pages5
Volume281
ISBN (Electronic)9781643681856
ISBN (Print)9781643681849
DOIs
Publication statusPublished - 29 May 2021

Publication series

NameStudies in Health Technology and Informatics
PublisherIOS Press
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Bibliographical note

© 2021 European Federation for Medical Informatics (EFMI) and IOS Press

Keywords

  • conversational agents
  • Healthcare
  • Machine learning
  • PATIENT SAFETY

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