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CC BY 4.0 · Methods Inf Med
DOI: 10.1055/a-2521-4250
Original Article for a Focus Theme

Large-Scale Integration of DICOM Metadata into HL7-FHIR for Medical Research

Alexa Iancu
1   Friedrich-Alexander-Universität Erlangen-Nürnberg, Medical Informatics, Erlangen, Germany
2   Bavarian Cancer Research Center, Erlangen, Bayern, Germany
,
Johannes Bauer
3   Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Bayern, Germany
,
Matthias S. May
4   Institute of Radiology, Universitätsklinikum Erlangen, Erlangen, Bayern, Germany
,
Hans-Ulrich Prokosch
1   Friedrich-Alexander-Universität Erlangen-Nürnberg, Medical Informatics, Erlangen, Germany
,
Arnd Dörfler
5   Department of Neuroradiology, Universitätsklinikum Erlangen, Erlangen, Bayern, Germany
,
Michael Uder
4   Institute of Radiology, Universitätsklinikum Erlangen, Erlangen, Bayern, Germany
,
Lorenz A. Kapsner
1   Friedrich-Alexander-Universität Erlangen-Nürnberg, Medical Informatics, Erlangen, Germany
4   Institute of Radiology, Universitätsklinikum Erlangen, Erlangen, Bayern, Germany
› Author Affiliations Funding This work was funded by the German Federal Ministry of Education and Research (BMBF) within the Medical Informatics Initiative (MIRACUM Consortium) under the Funding Number FKZ: 01ZZ1801A and by the Bavarian Cancer Research Center (BZKF).
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Abstract

Background The current gap between the availability of routine imaging data and its provisioning for medical research hinders the utilization of radiological information for secondary purposes. To address this, the German Medical Informatics Initiative (MII) has established frameworks for harmonizing and integrating clinical data across institutions, including the integration of imaging data into research repositories, which can be expanded to routine imaging data.

Objectives This project aims to address this gap by developing a large-scale data processing pipeline to extract, convert, and pseudonymize DICOM (Digital Imaging and Communications in Medicine) metadata into “ImagingStudy” Fast Healthcare Interoperability Resources (FHIR) and integrate them into research repositories for secondary use.

Methods The data processing pipeline was developed, implemented, and tested at the Data Integration Center of the University Hospital Erlangen. It leverages existing open-source solutions and integrates seamlessly into the hospital's research IT infrastructure. The pipeline automates the extraction, conversion, and pseudonymization processes, ensuring compliance with both local and MII data protection standards. A large-scale evaluation was conducted using the imaging studies acquired by two departments at University Hospital Erlangen within 1 year. Attributes such as modality, examined body region, laterality, and the number of series and instances were analyzed to assess the quality and availability of the metadata.

Results Once established, the pipeline processed a substantial dataset comprising over 150,000 DICOM studies within an operational period of 26 days. Data analysis revealed significant heterogeneity and incompleteness in certain attributes, particularly the DICOM tag “Body Part Examined.” Despite these challenges, the pipeline successfully generated valid and standardized FHIR, providing a robust basis for future research.

Conclusion We demonstrated the setup and test of a large-scale end-to-end data processing pipeline that transforms DICOM imaging metadata directly from clinical routine into the Health Level 7-FHIR format, pseudonymizes the resources, and stores them in an FHIR server. We showcased that the derived FHIRs offer numerous research opportunities, for example, feasibility assessments within Bavarian and Germany-wide research infrastructures. Insights from this study highlight the need to extend the “ImagingStudy” FHIR with additional attributes and refine their use within the German MII.

Keywords

radiology information system - health information interoperability - medical informatics applications - radiology department - hospital

Data Availability Statement

The DICOM orchestration API (https://github.com/alexa-ian/DICOM-Interval-Scheduler) and the fork of the DICOM-FHIR converter including adaptions for the local DIC at the UHE (https://github.com/alexa-ian/dicom-fhir-converter) are both available on GitHub.




Publication History

Received: 15 July 2024

Accepted: 15 January 2025

Article published online:
15 April 2025

© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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