Exploring Task Patterns in EHR Workflows Using Action Sequence Embedding and Graph-Based Analysis

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Abstract

This study investigates clinician workflows in electronic health records (EHR) using a novel combination of context-based embedding and graph-based dimensionality reduction techniques to EHR-based audit log sequences. We identified distinct clinical task groups, suggesting the potential for semi-automated, unsupervised methods for characterizing EHR-based workflow patterns.

Original languageEnglish
Title of host publicationMEDINFO 2025 - Healthcare Smart x Medicine Deep
Subtitle of host publicationProceedings of the 20th World Congress on Medical and Health Informatics
EditorsMowafa S. Househ, Mowafa S. Househ, Zain Ul Abideen Tariq, Mahmood Al-Zubaidi, Uzair Shah, Elaine Huesing
PublisherIOS Press BV
Pages1281-1285
Number of pages5
ISBN (Electronic)9781643686080
DOIs
StatePublished - Aug 7 2025
Event20th World Congress on Medical and Health Informatics, MEDINFO 2025 - Taipei, Taiwan, Province of China
Duration: Aug 9 2025Aug 13 2025

Publication series

NameStudies in Health Technology and Informatics
Volume329
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference20th World Congress on Medical and Health Informatics, MEDINFO 2025
Country/TerritoryTaiwan, Province of China
CityTaipei
Period08/9/2508/13/25

Keywords

  • Electronic health records
  • action sequence
  • audit logs
  • clinical workflow
  • context embedding
  • task identification
  • unsupervised learning

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