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Integrating Process Mining and Cognitive Analysis to Study EHR Workflow.

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Summary
This summary is machine-generated.

Understanding electronic health record (EHR) workflow variations is crucial. This study combined quantitative EHR interaction analysis with qualitative observations, revealing diverse clinician workflows and informing training needs.

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Area of Science:

  • Health Informatics
  • Human-Computer Interaction
  • Clinical Workflow Analysis

Background:

  • Studying electronic health record (EHR) mediated workflows requires in-depth analysis.
  • Existing methods often lack the granularity to capture complex clinician interactions within EHR systems.

Purpose of the Study:

  • To investigate variations in clinicians' EHR workflow by integrating quantitative and qualitative analyses.
  • To characterize information-gathering patterns within EHR systems.

Main Methods:

  • Employed sequential process-mining to analyze EHR interaction patterns from 6 clinicians across 1569 patient cases.
  • Integrated quantitative screen transition data with qualitative user performance observations.
  • Triangulated data to associate EHR-interactive behavior with routine processes, case complexity, and system defaults.

Main Results:

  • Identified 519 distinct screen transition patterns, with no single pattern dominating more than 10% of cases.
  • The 15 most frequent patterns covered 53% of patient cases, while 27% of cases showed unique patterns.
  • Clinician EHR behavior correlated with established routines, patient case complexity, and EHR default settings.

Conclusions:

  • The integrated quantitative and qualitative approach provides deep insights into EHR workflow variability.
  • Findings highlight the impact of individual routines, case complexity, and system defaults on EHR use.
  • This methodology can inform resource allocation for clinical observation and targeted EHR training programs.