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Related Experiment Videos

Pick from thousands: a collaborative processing model for coded data entry.

S M Huff1, T A Pryor, R D Tebbs

  • 1Department of Medical Informatics, LDS Hospital/University of Utah, Salt Lake City.

Proceedings. Symposium on Computer Applications in Medical Care
|January 1, 1992
PubMed
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We developed Pick From Thousands (PFT), a new data entry method for clinical systems. PFT enhances efficiency and accuracy by combining existing techniques with a novel string matching algorithm.

Area of Science:

  • Health Informatics
  • Clinical System Design
  • Data Entry Optimization

Background:

  • Optimizing coded data entry in clinical systems is crucial for efficient healthcare operations.
  • Existing methods like menu selection and keyword entry have limitations in speed and selectivity.
  • System design frequently addresses challenges in coded data input.

Purpose of the Study:

  • To introduce and evaluate a novel data entry mechanism called "Pick From Thousands" (PFT).
  • To improve the efficiency and accuracy of coded data entry in clinical information systems.
  • To present a solution that enhances user interaction and data integrity.

Main Methods:

  • Developed the "Pick From Thousands" (PFT) data entry method.
  • Integrated established techniques: menu selection, keyword entry, and initial character matching.

Related Experiment Videos

  • Introduced a new string matching algorithm for enhanced selectivity.
  • Utilized collaborative processing between PC workstations and a central HELP system computer.

    • Main Results:

    • The PFT method demonstrates superior selectivity compared to initial character matching for multi-word entries with equivalent keystrokes.
    • The system design facilitates ease of maintenance through collaborative processing.
    • Enhanced flexibility and performance of the clinical data entry system were achieved.

    Conclusions:

    • The "Pick From Thousands" (PFT) method offers a significant advancement in optimizing coded data entry.
    • This approach improves data accuracy and user efficiency in clinical settings.
    • Collaborative processing enhances system maintainability and overall performance.