Assuring End-to-End Data Quality for Analytics on FHIR
Future work will expand the system to address additional data quality dimensions, such as correctness and plausibility, improving the overall robustness of data analytics in federated environments.
Authors
- Clara Fischer (ORCID: https://orcid.org/0000-0001-6109-6315)
- Jasmin Ziegler (ORCID: https://orcid.org/0009-0005-5362-5228)
- Jonathan M. Mang (ORCID: https://orcid.org/0000-0003-0518-4710)
- Christian Gülden (ORCID: https://orcid.org/0000-0003-1261-3691)
- Thomas Ganslandt (ORCID: https://orcid.org/0000-0001-6864-8936)
- Marcel Erpenbeck (ORCID: https://orcid.org/0009-0007-5468-6510)
- Hans‐Ulrich Prokosch
- Paul-Christian Volkmer
Institutions
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- University Hospital Regensburg (DE)
- Universitätsklinikum Erlangen (DE)
- Comprehensive Cancer Center Mainfranken (DE)
- Cancer Research Center (US)
- Centro de Investigación del Cáncer (ES)
Publication Details
- Journal
- Studies in health technology and informatics
- Published
- 2025-04-24
- DOI
- https://doi.org/10.3233/shti250161
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00