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Priming intelligent split menus with text corpora for computerized patient record data-entry

K Canfield1

  • 1Department of Information Systems, University of Maryland, Baltimore 21228, USA.

International Journal of Bio-Medical Computing
|May 1, 1995
PubMed
Summary
This summary is machine-generated.

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Intelligent split menus, using healthcare text data, significantly reduce provider data-entry effort. This method optimizes menu selection by prioritizing frequent choices, saving time and clicks.

Area of Science:

  • Health informatics
  • Human-computer interaction
  • Natural language processing

Background:

  • Provider data-entry is time-consuming.
  • Current menu systems are often inefficient.
  • Optimizing user interfaces can improve healthcare efficiency.

Purpose of the Study:

  • To develop a methodology for priming intelligent split menus using existing healthcare text corpora.
  • To reduce the effort required for provider data-entry.
  • To enhance the efficiency of electronic health record systems.

Main Methods:

  • Utilizing a corpus from echocardiography to develop a simulation.
  • Employing statistical associations between word categories (e.g., 'anatomy', 'pathology') to prime menu frequency ordering.

Related Experiment Videos

  • Using a dictionary of terms for categorical information.
  • Updating menu frequencies based on actual user selections for adaptive learning.

    • Main Results:

    • Simulations indicate that intelligent split menus require 2-5 times less effort than traditional alphabetical menus.
    • The methodology effectively uses corpus knowledge to organize menu selections.
    • The system adapts to individual provider data-entry patterns over time.

    Conclusions:

    • Intelligent split menus offer a significant improvement in data-entry efficiency for healthcare providers.
    • This methodology provides a scalable approach to optimizing user interfaces in healthcare.
    • Further research can explore broader applications in clinical data management.