Virtual Enumerator’s Training for Time and Motion Studies Using AI Scenarios
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 (ORCID: https://orcid.org/0000-0003-4670-3046)
- Jennifer Whyte (ORCID: https://orcid.org/0000-0003-4640-2913)
- Adrián Santoro (ORCID: https://orcid.org/0000-0003-3789-2332)
- Juan Pedro Alonso (ORCID: https://orcid.org/0000-0002-8366-7200)
- Juan Manuel Ortiz (ORCID: https://orcid.org/0000-0002-0945-0003)
- Analía López (ORCID: https://orcid.org/0000-0002-6732-6449)
- Fernando Rubinstein (ORCID: https://orcid.org/0000-0001-5653-5766)
- Cintia Cejas (ORCID: https://orcid.org/0000-0001-5591-7615)
- Martín M. Díaz Maffini
- Kern Rocke
Institutions
- Instituto de Efectividad Clínica y Sanitaria (AR)
- University of the West Indies (JM)
Publication Details
- Journal
- Studies in health technology and informatics
- Published
- 2025-05-15
- DOI
- https://doi.org/10.3233/shti250546
- Primary Topic
- Electronic Health Records Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00