Characterizing the macrostructure of electronic health record work using raw audit logs: An unsupervised action embeddings approach

Sunny Lou, Hanyang Liu, Derek Harford, Chenyang Lu, Thomas Kannampallil

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Raw audit logs provide a comprehensive record of clinicians' activities on an electronic health record (EHR) and have considerable potential for studying clinician behaviors. However, research using raw audit logs is limited because they lack context for clinical tasks, leading to difficulties in interpretation. We describe a novel unsupervised approach using the comparison and visualization of EHR action embeddings to learn context and structure from raw audit log activities. Using a dataset of 15 767 634 raw audit log actions performed by 88 intern physicians over 6 months of EHR use across inpatient and outpatient settings, we demonstrated that embeddings can be used to learn the situated context for EHR-based work activities, identify discrete clinical workflows, and discern activities typically performed across diverse contexts. Our approach represents an important methodological advance in raw audit log research, facilitating the future development of metrics and predictive models to measure clinician behaviors at the macroscale.

Original languageEnglish
Pages (from-to)539-544
Number of pages6
JournalJournal of the American Medical Informatics Association
Volume30
Issue number3
DOIs
StatePublished - Mar 1 2023

Keywords

  • clinical workflow
  • raw audit logs
  • unsupervised learning

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