Artikel
InterAgent on FHIR – Lessons Learned from Implementing an Intelligent Tutoring System with the Help of HL7 FHIR
Suche in Medline nach
Autoren
-
Nicolas Frey
- Charité – Universitätsmedizin Berlin, Institute of Medical informatics, Berlin, Germany
-
Nina Haffer
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
-
Lennart Vogelsang
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
-
Julian Saß
- Berliner Institut für Gesundheitsforschung in der Charité – Universitätsmedizin Berlin, Berlin, Germany
-
Margaux Gatrio
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
-
Sylvia Thun
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
-
Felix Balzer
- Charité – Universitätsmedizin Berlin, Institute of Medical informatics, Berlin, Germany
-
Martin Möckel
- Charité - Universitätsmedizin Berlin, Berlin, Germany
-
Philipp Landgraf
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| Veröffentlicht: | 6. September 2024 |
|---|
Gliederung
Text
Introduction: For an interoperable Intelligent Tutoring System (ITS), we used resources from Fast Healthcare Interoperability Resources (FHIR) and mapped learning content with Unified Medical Language System (UMLS) codes to enhance healthcare education. This study addresses the need to enhance the interoperability and effectiveness of ITS in healthcare education.
State of the art: The current state of the art in ITS involves advanced personalized learning and adaptability techniques, integrating technologies such as machine learning to personalize the learning experience and to create systems that dynamically respond to individual learner needs. However, existing ITS architectures face challenges related to interoperability and integration with healthcare systems.
Concept: Our system maps learning content with UMLS codes, each scored for similarity, ensuring consistency and extensibility. FHIR is used to standardize the exchange of medical information and learning content.
Implementation: Implemented as a microservice architecture, the system uses a recommender to request FHIR resources, provide questions, and measure learner progress.
Lessons learned: Using international standards, our ITS ensures reproducibility and extensibility, enhancing interoperability and integration with existing platforms.
The authors declare that they have no competing interests.
The authors declare that an ethics committee vote is not required.
