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VERSION:2.0
PRODID:Linklings LLC
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TZID:America/Chicago
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DTSTART:19700308T020000
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DTSTART:19701101T020000
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BEGIN:VEVENT
DTSTAMP:20251016T135136Z
LOCATION:Grand Hall I
DTSTART;TZID=America/Chicago:20251016T171000
DTEND;TZID=America/Chicago:20251016T173000
UID:HFESAM_ASPIRE 2025_sess279_LECT159@linklings.com
SUMMARY:Enhancing Patient Safety Event Reporting with Machine Learning: A 
 Usability Study
DESCRIPTION:Deenar Virani, Victoria Yeung, and Dr. Myrtede Alfred (Univers
 ity of Toronto)\n\nThis study aims to assess the usability of a previously
  developed patient safety event (PSE) reporting interface by Chen et al (C
 hen et al, 2023), which integrates a machine learning (ML) classifier and 
 the Local Interpretable Model-Agnostic Explanations (LIME) technique (Ribe
 iro MT et al, 2016) to automatically classify the event type of PSE report
 s. The objective of the usability testing is to evaluate the accuracy and 
 effectiveness of the ML classifier for PSE reporting systems, particularly
  its impact on users’ decision-making, interpretation of classifications, 
 trust, and overall usability of the interface.\n\nTrack: Usability and Sys
 tem Evaluation\n\nSession Chair: Amelia Warden (University of Michigan)\n\
 n
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