Towards Standardizing Ophthalmic Data for Seamless Interoperability in Eye Care.

Autor:	Hoffmann K; Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany., Peng Y; Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany., Schlosser T; Junior Professorship of Media Computing, Chemnitz University of Technology, Chemnitz, Germany., Stolze G; Department of Ophthalmology, Klinikum Chemnitz gGmbH, Chemnitz, Germany., Langner H; Professorship of Media Informatics, University of Applied Sciences Mittweida, Mittweida, Germany., Susky M; Research Group Digital Health, Faculty of Business and Economics, Technical University of Dresden, Dresden, Germany., Meyer T; Junior Professorship of Media Computing, Chemnitz University of Technology, Chemnitz, Germany., Ritter M; Professorship of Media Informatics, University of Applied Sciences Mittweida, Mittweida, Germany., Kowerko D; Junior Professorship of Media Computing, Chemnitz University of Technology, Chemnitz, Germany., Kakkassery V; Department of Ophthalmology, Klinikum Chemnitz gGmbH, Chemnitz, Germany., Wolfien M; Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.; Center for Scalable Data Analytics and Artificial Intelligence, Dresden/Leipzig, Germany., Sedlmayr M; Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
Jazyk:	angličtina
Zdroj:	Studies in health technology and informatics [Stud Health Technol Inform] 2024 Aug 30; Vol. 317, pp. 139-145.
DOI:	10.3233/SHTI240849
Abstrakt:	Introduction: Seamless interoperability of ophthalmic clinical data is beneficial for improving patient care and advancing research through the integration of data from various sources. Such consolidation increases the amount of data available, leading to more robust statistical analyses, and improving the accuracy and reliability of artificial intelligence models. However, the lack of consistent, harmonized data formats and meanings (syntactic and semantic interoperability) poses a significant challenge in sharing ophthalmic data. Methods: The Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR), a standard for the exchange of healthcare data, emerges as a promising solution. To facilitate cross-site data exchange in research, the German Medical Informatics Initiative (MII) has developed a core data set (CDS) based on FHIR. Results: This work investigates the suitability of the MII CDS specifications for exchanging ophthalmic clinical data necessary to train and validate a specific machine learning model designed for predicting visual acuity. In interdisciplinary collaborations, we identified and categorized the required ophthalmic clinical data and explored the possibility of its mapping to FHIR using the MII CDS specifications. Discussion: We found that the current FHIR MII CDS specifications do not completely accommodate the ophthalmic clinical data we investigated, indicating that the creation of an extension module is essential.
Databáze:	MEDLINE
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