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A Sequential Data Analysis Approach to Electronic Health Record Workflow.

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This summary is machine-generated.

Understanding clinical workflow in electronic health record (EHR) tasks is key to improving usability. Sequential data analysis revealed common clinician information-gathering patterns, highlighting potential inefficiencies.

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Area of Science:

  • Clinical Informatics
  • Human-Computer Interaction
  • Health Data Science

Background:

  • Usability issues in electronic health records (EHRs) often stem from a lack of understanding of clinical workflows.
  • Efficient information gathering by clinicians is crucial for effective patient care and EHR system performance.

Purpose of the Study:

  • To characterize clinician information-gathering patterns within EHR systems using sequential data analysis.
  • To identify common screen transition sequences and their frequencies during clinical tasks.
  • To provide insights for improving EHR design to better support clinical workflows.

Main Methods:

  • Employed sequential data analysis on interaction data from 5 clinicians across 66 patients.
  • Conducted two analyses: characterizing common screen transition patterns and quantifying sequence occurrence frequency.
  • Identified 27 distinct screen-transition patterns used multiple times.

Main Results:

  • Observed 27 screen-transition patterns utilized 2 to 7 times among clinicians.
  • Documents/Images and Intake/Output screens were accessed for nearly all patients.
  • Repeated viewing of certain screens suggests potential inefficiencies in the information-gathering process.

Conclusions:

  • Sequential data analysis offers valuable insights into clinical workflow patterns within EHRs.
  • Understanding these patterns can inform the development of more robust and user-centered EHR designs.
  • Quantitative analysis of interaction data is essential for optimizing clinical workflow support.