PDF herunterladen
Rofo 2025; 197(05): 518-525
DOI: 10.1055/a-2462-2351
Review

FHIR – Overdue Standard for Radiology Data Warehouses

Artikel in mehreren Sprachen: English | deutsch
Philipp Arnold
1   Department of Radiology, Medical Center – University of Freiburg Department of Radiology, Freiburg, Germany (Ringgold ID: RIN537501)
,
Daniel Pinto dos Santos 
2   Institute of Diagnostic and Interventional Radiology, University Hospital Cologne Institute of Diagnostic and Interventional Radiology, Cologne, Germany (Ringgold ID: RIN647356)
3   Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz Department of Diagnostic and Interventional Radiology, Mainz, Germany (Ringgold ID: RIN155981)
,
Fabian Bamberg
1   Department of Radiology, Medical Center – University of Freiburg Department of Radiology, Freiburg, Germany (Ringgold ID: RIN537501)
,
Elmar Kotter
1   Department of Radiology, Medical Center – University of Freiburg Department of Radiology, Freiburg, Germany (Ringgold ID: RIN537501)
› InstitutsangabenGefördert durch: Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) 499552394 – SFB 1597
Gefördert durch: Medizinische Fakultät der Albert-Ludwigs-Universität Freiburg Hans A. Krebs Medical Scientist Program
› Weitere Informationen
Auch verfügbar auf
   Lizenzen und Reprints

Abstract

Background

In radiology, technological progress has led to an enormous increase in data volumes. To effectively use these data during diagnostics or subsequent clinical evaluations, they have to be aggregated at a central location and be meaningfully retrievable in context. Radiology data warehouses undertake this task: they integrate diverse data sources, enable patient-specific and examination-specific evaluations, and thus offer numerous benefits in patient care, education, and clinical research.

Method

The international standard Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) is particularly suitable for the implementation of such a data warehouse. FHIR allows for easy and fast data access, supports modern web-based frontends, and offers high interoperability due to the integration of medical ontologies such as SNOMED-CT or RadLex. Furthermore, FHIR has a robust data security concept. Because of these properties, FHIR has been selected by the Medical Informatics Initiative (MII) as the data standard for the core data set and is intended to be promoted as an international standard in the European Health Data Space (EHDS).

Conclusion

Implementing the FHIR standard in radiology data warehouses is therefore a logical and sensible step towards data-driven medicine.

Key Points

  • A data warehouse is essential for data-driven medicine, clinical care, and research purposes.

  • Data warehouses enable efficient integration of AI results and structured report templates.

  • Fast Healthcare Interoperability Resources (FHIR) is a suitable standard for a data warehouse.

  • FHIR provides an interoperable data standard, supported by proven web technologies.

  • FHIR improves semantic consistency and facilitates secure data exchange.

Citation Format

  • Arnold P, Pinto dos Santos D, Bamberg F et al. FHIR – Overdue Standard for Radiology Data Warehouses. Rofo 2025; 197: 518–524

Keywords

PACS - Data Warehouse - diagnostic radiology - Artificial Intelligence - Fast Healthcare Interoperability Resources (FHIR)


Publikationsverlauf

Eingereicht: 08. Mai 2024

Angenommen nach Revision: 27. Oktober 2024

Artikel online veröffentlicht:
06. Dezember 2024

© 2024. Thieme. All rights reserved.

Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

 

  • References

  • 1 Prather JC, Lobach DF, Goodwin LK. et al. Medical data mining: knowledge discovery in a clinical data warehouse. Proc AMIA Annu Fall Symp 1997; 4: 101
    Suche in Google Scholar
  • 2 Kawamoto K, Houlihan CA, Balas EA. et al. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 2005; 330: 765
    Suche in Google Scholar
  • 3 McDonald RJ, Schwartz KM, Eckel LJ. et al. The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload. Acad Radiol 2015; 22: 1191-1198
    Suche in Google Scholar
  • 4 Xu L, Busch F, Adams LC. et al. Artificial intelligence in radiology and radiotherapy. Onkologie 2024; 1-7
    Suche in Google Scholar
  • 5 Gagalova KK, Angelica Leon Elizalde M, Portales-Casamar E. et al. What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions. JMIR Form Res 2020; 4
    Suche in Google Scholar
  • 6 Nemati HR, Steiger DM, Iyer LS. et al. Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing. Decis Support Syst 2002; 33: 143-161
    Suche in Google Scholar
  • 7 Murphy SN, Dubey A, Embi PJ. et al. Current State of Information Technologies for the Clinical Research Enterprise across Academic Medical Centers. Clin Transl Sci 2012; 5: 281-284
    Suche in Google Scholar
  • 8 MacKenzie SL, Wyatt MC, Schuff R. et al. Practices and perspectives on building integrated data repositories: results from a 2010 CTSA survey. J Am Med Informatics Assoc 2012; 19: e119-e124
    Suche in Google Scholar
  • 9 Der Kerndatensatz der Medizininformatik-Initiative. Zugriff am 29. April 2024 unter: https://www.medizininformatik-initiative.de/de/der-kerndatensatz-der-medizininformatik-initiative
  • 10 Ayaz M, Pasha MF, Alzahrani MY. et al. The Fast Health Interoperability Resources (FHIR) Standard: Systematic Literature Review of Implementations, Applications, Challenges and Opportunities. JMIR Med Inf 2021; 9 (07) e21929 https://medinform.jmir.org/2021/7/e21929
    Suche in Google Scholar
  • 11 Taylor & Francis; 2006
  • 12 Noumeir R. Benefits of the DICOM structured report. J Digit Imaging 2006; 19: 295-306
    Suche in Google Scholar
  • 13 Clunie DA. DICOM structured reporting. PixelMed publishing 2000;
    Suche in Google Scholar
  • 14 Mustra M, Delac K, Grgic M. Overview of the DICOM standard. In: 2008 50th International Symposium ELMAR. 2008
    Suche in Google Scholar
  • 15 Benson T. HL7 Version 2 BT – Principles of Health Interoperability HL7 and SNOMED. In: Benson T. , ed. . London: Springer London; 2012: 101-119
    Suche in Google Scholar
  • 16 Viangteeravat T, Anyanwu MN, Nagisetty VR. et al. Clinical data integration of distributed data sources using Health Level Seven (HL7) v3-RIM mapping. J Clin Bioinforma 2011; 1: 1-10
    Suche in Google Scholar
  • 17 Lee D, de Keizer N, Lau F. et al. Literature review of SNOMED CT use. J Am Med Informatics Assoc 2014; 21: e11-e19
    Suche in Google Scholar
  • 18 Marwede D, Daumke P, Marko K. et al. RadLex – Deutsche version: Ein radiologisches lexikon zur indexierung von bild- und befunddaten. Fortschr Röntgenstr 2009; 181: 38-44
    Suche in Google Scholar
  • 19 McDonald CJ, Huff SM, Suico JG. et al. LOINC, a universal standard for identifying laboratory observations: a 5-year update. Clin Chem 2003; 49: 624-633
    Suche in Google Scholar
  • 20 Vreeman DJ, Abhyankar S, Wang KC. et al. The LOINC RSNA radiology playbook – a unified terminology for radiology procedures. J Am Med Informatics Assoc 2018; 25: 885-893
    Suche in Google Scholar
  • 21 Ivanović M, Budimac Z. An overview of ontologies and data resources in medical domains. Expert Syst Appl 2014; 41: 5158-5166
    Suche in Google Scholar
  • 22 Cimino JJ. Desiderata for Controlled Medical Vocabularies in the Twenty-First Century. Methods Inf Med 1998; 37: 394
    Suche in Google Scholar
  • 23 Gruber TR. Toward principles for the design of ontologies used for knowledge sharing?. Int J Hum Comput Stud 1995; 43: 907-928
    Suche in Google Scholar
  • 24 Mildenberger P. The Essence of HL7, DICOM, and IHE BT – Basic Knowledge of Medical Imaging Informatics: Undergraduate Level and Level I. In: van Ooijen PMA. , ed. . Cham: Springer International Publishing; 2021: 15-23
    Suche in Google Scholar
  • 25 IHE. IHR Category:FHIR. Zugriff am 01. Juli 2024 unter: https://wiki.ihe.net/index.php/Category:FHIR
  • 26 HL7 Messaging Standard Version 2.9. Zugriff am 29. April 2024 unter: https://www.hl7.org/implement/standards/product_brief.cfm?product_id=516
  • 27 Sharma M, Aggarwal H. HL-7 Based Middleware Standard for Healthcare Information System: FHIR. Lect Notes Networks Syst 2019; 46: 889-899
    Suche in Google Scholar
  • 28 Zhu S-H, Rao N-N. Design and Implementation of HL7 V3 Gateway. J Electron Sci Technol China 2005; 3
    Suche in Google Scholar
  • 29 Smith B, Ceusters W. HL7 RIM: An Incoherent Standard. Stud Health Technol Inform 2006; 124: 133-138
    Suche in Google Scholar
  • 30 Bender D, Sartipi K. HL7 FHIR: An agile and RESTful approach to healthcare information exchange. Proc CBMS 2013 – 26th IEEE Int Symp Comput Med Syst. 2013
    Suche in Google Scholar
  • 31 Marcus JS, Martens B, Carugati C. et al. The European Health Data Space. SSRN Electron J 2022;
    Suche in Google Scholar
  • 32 Open Source Implementations – FHIR – Confluence. Zugriff am 29. April 2024 unter: https://confluence.hl7.org/display/FHIR/Open+Source+Implementations
  • 33 GitHub – hapifhir/hapi-fhir-jpaserver-starter. Zugriff am 29. April 2024 unter: https://github.com/hapifhir/hapi-fhir-jpaserver-starter
  • 34 Overview of Docker Desktop | Docker Docs. Zugriff am 29. April 2024 unter: https://docs.docker.com/desktop/
  • 35 GitHub – microsoft/FHIR-Converter: Conversion utility to translate legacy data formats into FHIR. Zugriff am 29. April 2024 unter: https://github.com/microsoft/FHIR-Converter
  • 36 GitHub – LinuxForHealth/hl7v2-fhir-converter: Converts HL7 v2 Messages to FHIR Resources. Zugriff am 29. April 2024 unter: https://github.com/LinuxForHealth/hl7v2-fhir-converter
  • 37 Resource Description Framework (RDF) Model and Syntax Specification. Zugriff am 27. März 2024 unter: https://www.w3.org/TR/1999/REC-rdf-syntax-19990222/
  • 38 Berners-Lee T, Hendler J, Lassila O. (PDF) The Semantic Web: A New Form of Web Content That is Meaningful to Computers Will Unleash a Revolution of New Possibilities. Sci Am 2001;
    Suche in Google Scholar
  • 39 Fensel D, Facca FM, Simperl E. et al. Semantic Web. Semant Web Serv 2011; 87-104
    Suche in Google Scholar
  • 40 Prud’hommeaux E, Collins J, Booth D. et al. Development of a FHIR RDF data transformation and validation framework and its evaluation. J Biomed Inform 2021; 117: 103755
    Suche in Google Scholar
  • 41 Lehmann J, Bühmann L. AutoSPARQL: Let users query your knowledge base. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 2011; 6643: 63-79
    Suche in Google Scholar
  • 42 Perevalov A, Yan X, Kovriguina L. et al. Knowledge Graph Question Answering Leaderboard: A Community Resource to Prevent a Replication Crisis. Lang Resour Eval Conf Lr 2022; 2998-3007
    Suche in Google Scholar
  • 43 Xiao G, Pfaff E, Prud’hommeaux E. et al. FHIR-Ontop-OMOP: Building clinical knowledge graphs in FHIR RDF with the OMOP Common data Model. J Biomed Inform 2022; 134: 1-23
    Suche in Google Scholar