@Article{info:doi/10.2196/65681,
author="Ziegler, Jasmin
and Erpenbeck, Marcel Pascal
and Fuchs, Timo
and Saibold, Anna
and Volkmer, Paul-Christian
and Schmidt, Guenter
and Eicher, Johanna
and Pallaoro, Peter
and De Souza Falguera, Renata
and Aubele, Fabio
and Hagedorn, Marlien
and Vansovich, Ekaterina
and Raffler, Johannes
and Ringshandl, Stephan
and Kerscher, Alexander
and Maurer, Julia Karolin
and K{\"u}hnel, Brigitte
and Schenkirsch, Gerhard
and Kampf, Marvin
and Kapsner, Lorenz A
and Ghanbarian, Hadieh
and Spengler, Helmut
and Soto-Rey, I{\~{n}}aki
and Albashiti, Fady
and Hellwig, Dirk
and Ertl, Maximilian
and Fette, Georg
and Kraska, Detlef
and Boeker, Martin
and Prokosch, Hans-Ulrich
and Gulden, Christian",
title="Bridging Data Silos in Oncology with Modular Software for Federated Analysis on Fast Healthcare Interoperability Resources: Multisite Implementation Study",
journal="J Med Internet Res",
year="2025",
month="Apr",
day="15",
volume="27",
pages="e65681",
keywords="real-world data; real-world evidence; oncology; electronic health records; federated analysis; HL7 FHIR; cancer registries; interoperability; observational research network",
abstract="Background: Real-world data (RWD) from sources like administrative claims, electronic health records, and cancer registries offer insights into patient populations beyond the tightly regulated environment of randomized controlled trials. To leverage this and to advance cancer research, 6 university hospitals in Bavaria have established a joint research IT infrastructure. Objective: This study aimed to outline the design, implementation, and deployment of a modular data transformation pipeline that transforms oncological RWD into a Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) format and then into a tabular format in preparation for a federated analysis (FA) across the 6 Bavarian Cancer Research Center university hospitals. Methods: To harness RWD effectively, we designed a pipeline to convert the oncological basic dataset (oBDS) into HL7 FHIR format and prepare it for FA. The pipeline handles diverse IT infrastructures and systems while maintaining privacy by keeping data decentralized for analysis. To assess the functionality and validity of our implementation, we defined a cohort to address two specific medical research questions. We evaluated our findings by comparing the results of the FA with reports from the Bavarian Cancer Registry and the original data from local tumor documentation systems. Results: We conducted an FA of 17,885 cancer cases from 2021/2022. Breast cancer was the most common diagnosis at 3 sites, prostate cancer ranked in the top 2 at 4 sites, and malignant melanoma was notably prevalent. Gender-specific trends showed larynx and esophagus cancers were more common in males, while breast and thyroid cancers were more frequent in females. Discrepancies between the Bavarian Cancer Registry and our data, such as higher rates of malignant melanoma (3400/63,771, 5.3{\%} vs 1921/17,885, 10.7{\%}) and lower representation of colorectal cancers (8100/63,771, 12.7{\%} vs 1187/17,885, 6.6{\%}) likely result from differences in the time periods analyzed (2019 vs 2021/2022) and the scope of data sources used. The Bavarian Cancer Registry reports approximately 3 times more cancer cases than the 6 university hospitals alone. Conclusions: The modular pipeline successfully transformed oncological RWD across 6 hospitals, and the federated approach preserved privacy while enabling comprehensive analysis. Future work will add support for recent oBDS versions, automate data quality checks, and integrate additional clinical data. Our findings highlight the potential of federated health data networks and lay the groundwork for future research that can leverage high-quality RWD, aiming to contribute valuable knowledge to the field of cancer research. ",
issn="1438-8871",
doi="10.2196/65681",
url="https://www.jmir.org/2025/1/e65681",
url="https://doi.org/10.2196/65681"
}