Integrating Process Mining and Cognitive Analysis to Study EHR Workflow

Stephanie K. Furniss, Matthew M. Burton, Adela Grando, David Larson, David R. Kaufman

Research output: Contribution to journalArticle

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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