Accurate Yet Privacy-Preserving Determination of Case Numbers Across German University Hospital Health Data

Overton P, Kiel A, Semler SC, Prasser F, Kussel T, Ganslandt T, Gründner J (2026)

Publication Type: Book chapter / Article in edited volumes

Publication year: 2026

Journal

Studies in Health Technology and Informatics IOS Press

Publisher: IOS Press BV

Edited Volumes: Die persönliche Verbindung zwischen Technologie und Gesundheitswesen öffnen

Series: Studien im Bereich Gesundheitstechnologie und Informatik

Book Volume: 336

Pages Range: 1425-1429

Conference Proceedings Title: Studies in Health Technology and Informatics

Event location: Genoa, ITA

ISBN: 9781643686615

DOI: 10.3233/SHTI260444

Abstract

Protecting privacy is essential when handling personal medical records. Even in pseudonymized health data, identifying individual patients remains a persistent risk in large medical datasets. The German Research Data Portal for Health (FDPG) provides access to extensive patient-derived routine data, including diagnoses, procedures, laboratory results, oncological findings, images, and genomic information. These data are stored locally at participating sites and harmonized through a common HL7 FHIR-based model. The portal’s feasibility query function allows users to define complex cohorts using diverse search criteria and retrieves real-time patient counts from all German university hospitals and beyond. Despite the high aggregation level, re-identification risks persist due to potential prior knowledge or repeated (“tracker”) queries. The FDPG therefore aims to minimize re-identification risk while preserving sufficient data utility for meaningful data discovery. Here, we present the data protection approach implemented in the distributed FDPG infrastructure and share insights from its first two years of operation, offering a blueprint for the upcoming European Health Data Space (EHDS).

Authors with CRIS profile

Thomas Ganslandt Professur für Medical Data Science Julian Gründner Professur für Medical Data Science

Involved external institutions

TMF – Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V. DE Germany (DE) Universität Leipzig DE Germany (DE) Berliner Institut für Gesundheitsforschung in der Charité / Berlin Institute of Health at Charité (BIH) DE Germany (DE) Deutsches Krebsforschungszentrum (DKFZ) DE Germany (DE)

How to cite

APA:

Overton, P., Kiel, A., Semler, S.C., Prasser, F., Kussel, T., Ganslandt, T., & Gründner, J. (2026). Accurate Yet Privacy-Preserving Determination of Case Numbers Across German University Hospital Health Data. In Maria Hagglund, Lars Lindskold, Lenka Lhotska, Sara Marceglia, Enea Parimbelli, Lucia Sacchi, Paolo Soda, Lacramioara Stoicu-Tivadar, Pierangelo Veltri, Patrizia Vizza, Mauro Giacomini, Jaime Delgado, Theodoros N. Arvanitis, Elisavet Andrikopoulou, Arriel Benis, Gabriella Balestra, Riccardo Bellazzi, Parisis G. Gallos, Roberto Gatta, Daniele Roberto Giacobbe, Noemi Giordano (Eds.), Die persönliche Verbindung zwischen Technologie und Gesundheitswesen öffnen. (pp. 1425-1429). IOS Press BV.

MLA:

Overton, Philip, et al. "Accurate Yet Privacy-Preserving Determination of Case Numbers Across German University Hospital Health Data." Die persönliche Verbindung zwischen Technologie und Gesundheitswesen öffnen. Ed. Maria Hagglund, Lars Lindskold, Lenka Lhotska, Sara Marceglia, Enea Parimbelli, Lucia Sacchi, Paolo Soda, Lacramioara Stoicu-Tivadar, Pierangelo Veltri, Patrizia Vizza, Mauro Giacomini, Jaime Delgado, Theodoros N. Arvanitis, Elisavet Andrikopoulou, Arriel Benis, Gabriella Balestra, Riccardo Bellazzi, Parisis G. Gallos, Roberto Gatta, Daniele Roberto Giacobbe, Noemi Giordano, IOS Press BV, 2026. 1425-1429.

BibTeX: Download