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InterAgent on FHIR – Lessons Learned from Implementing an Intelligent Tutoring System with the Help of HL7 FHIR
Nicolas Frey, Nina Haffer, Lennart Vogelsang, Julian Saß, Margaux Gatrio, Sylvia Thun, Felix Balzer, Martin Möckel, Philipp Landgraf
152 - 159
10.3233/SHTI240851
Research Article
Studies in Health Technology and Informatics
Volume 317: German Medical Data Sciences 2024
Abstract

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.

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