Integrating Process Mining and Cognitive Analysis to Study EHR Workflow

Stephanie K. Furniss, Matthew Burton, Maria Grando, David W. Larson, David Kaufman

    Research output: Contribution to journalArticlepeer-review

    3 Scopus citations

    Abstract

    There are numerous methods to study workflow. However, few produce the kinds of in-depth analyses needed to understand EHR-mediated workflow. Here we investigated variations in clinicians' EHR workflow by integrating quantitative analysis of patterns of users' EHR-interactions with in-depth qualitative analysis of user performance. We characterized 6 clinicians' patterns of information-gathering using a sequential process-mining approach. The analysis revealed 519 different screen transition patterns performed across 1569 patient cases. No one pattern was followed for more than 10% of patient cases, the 15 most frequent patterns accounted for over half ofpatient cases (53%), and 27% of cases exhibited unique patterns. By triangulating quantitative and qualitative analyses, we found that participants' EHR-interactive behavior was associated with their routine processes, patient case complexity, and EHR default settings. The proposed approach has significant potential to inform resource allocation for observation and training. In-depth observations helped us to explain variation across users.

    Original languageEnglish (US)
    Pages (from-to)580-589
    Number of pages10
    JournalAMIA ... Annual Symposium proceedings. AMIA Symposium
    Volume2016
    StatePublished - 2016

    ASJC Scopus subject areas

    • Medicine(all)

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