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User interface optimization for an electronic medical record system.

Kai Zheng1, Rema Padman, Michael P Johnson

  • 1School of Public Health and School of Information, University of Michigan, Ann Arbor 48109-2029, USA. kzheng@umich.edu

Studies in Health Technology and Informatics
|October 4, 2007
PubMed
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Improving healthcare application usability is crucial. This study uses sequential pattern analysis on electronic medical record data to understand clinician navigation, enhancing user interfaces and potentially improving practice behaviors.

Area of Science:

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

Background:

  • Information technology in healthcare often fails due to poor user interface design.
  • There is a lack of focus on creating usable and acceptable interfaces for healthcare professionals in health informatics.
  • Effective user interface design is critical for the successful adoption of electronic medical record systems.

Purpose of the Study:

  • To present a method for enhancing application interface usability in healthcare.
  • To analyze temporal event sequences (clickstreams) from electronic medical record systems.
  • To align software interfaces with clinicians' mental models and improve workflow efficiency.

Main Methods:

  • Sequential pattern analysis applied to clinician navigation data (clickstreams).

Related Experiment Videos

  • Analysis of temporal event sequences recorded in an electronic medical record system.
  • Utilizing identified patterns to calibrate software interfaces and workflows.

    • Main Results:

    • Identified common clinician navigation patterns within the electronic medical record system.
    • Software developers used these patterns to refine the user interface.
    • The recalibrated interface better matched clinicians' problem-solving mental models.

    Conclusions:

    • Sequential pattern analysis is an effective method for improving health IT interface usability.
    • Aligning interfaces with clinician mental models enhances workflow and system adoption.
    • Analysis of usage patterns can inform improvements in clinical practice and system interaction.