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Reconfigurable Gamification Platform for the Autonomous Learning of Low Value Medical Practices
- Source :
- Signal & Image Processing Trends.
- Publication Year :
- 2022
- Publisher :
- Academy and Industry Research Collaboration Center (AIRCC), 2022.
-
Abstract
- Failure to follow do-not-do recommendations (also known as low-value practices) is one of the causes of the lack of quality care in all health systems in all countries. Healthcare professionals must be provided with information about these low-value practices that are still frequently performed and their implications for patients and the healthcare system. Continuous education is a key factor in this scenario, so that health students, health professionals, and even patients are kept updated with the main do-not-do recommendations. Gamified platforms are one of the most valuable options for continuous education, as they combine learning efficiency with a high level of engagement for the students. Besides, the effectiveness of gamification platforms can be improved by adding artificial intelligence techniques. In this paper, a novel gamified platform focused on improving knowledge about low-value practices is proposed. AI techniques, as well as NLP tools are used to optimize the effectiveness of learning by adapting the platform to each user, at an individual level. Besides, the engagement of students is encouraged by their participation in a common project, namely the creation of a specialized dictionary for do-not-do terms. Hardware development is currently in progress. A basic gamification platform has already been developed for the two main mobile operating systems. Developing IA and NLP techniques to analyse the training outputs and make the platform adaptable to each student is progressing. The proposed learning tool can significantly improve healthcare quality and be applied to many other learning fields, particularly when continuous training is required.
Details
- Database :
- OpenAIRE
- Journal :
- Signal & Image Processing Trends
- Accession number :
- edsair.doi...........bdfb67f50162e1d717093e239ccc53af
- Full Text :
- https://doi.org/10.5121/csit.2022.121716