Virtual Enumerator's Training for Time and Motion Studies Using AI Scenarios.

Journal: Studies in health technology and informatics
Published Date:

Abstract

This paper presents the implementation of a virtual training program for enumerators conducting Time and Motion Studies using the WOMBAT methodology, as part of a project assessing healthcare workflows before the adoption of an Electronic Health Record system. The training employed digital tools, including Zoom meetings, Google Forms, the WOMBAT App, and AI-generated scenario images, to simulate clinical settings. The program successfully prepared observers for data collection, with high agreement among them. Even though its limitations, this study shares a remote training approach for TMS in geographically dispersed contexts, with potential for further integration of AI tools.

Authors

  • Daniel A Rizzato Lede
    IECS, Buenos Aires, Argentina.
  • Martín M Díaz Maffini
    IECS, Buenos Aires, Argentina.
  • Juan M Ortiz
    IECS, Buenos Aires, Argentina.
  • Kern Rocke
    IECS, Buenos Aires, Argentina.
  • Jermaine Whyte
    IECS, Buenos Aires, Argentina.
  • Adrian Santoro
    IECS, Buenos Aires, Argentina.
  • Juan P Alonso
    IECS, Buenos Aires, Argentina.
  • Analía López
    IECS, Buenos Aires, Argentina.
  • Cintia A Cejas
    IECS, Buenos Aires, Argentina.
  • Adolfo Rubinstein
    IECS, Buenos Aires, Argentina.