How Researchers Access Clinical Data: Resources, Pathways, and Challenges

KIT –The Research University in the Helmholtz Association [email protected] Employer hospital Open data Other hospital Employment allows access via hospital network •High data quality •Moderate time and minimal additional financial resources needed •Data limited to hospital data base •Limited computing resources •External data sets for model validation still needed Methods & Results Data freely available, registration or ethical training might be required Access mode might vary strongly (VPN, anonymised data, onsite use, federated learning setup) DIC acts as a fiduciary and provides researchers with pseudonymised datasets avia data sharing agreement bvia Data Integration Centre (DIC) •provides comprehensive internal data at moderate cost but is limited in scalability across institutions •allows rapid, low-cost research while ensuring privacy compliance but is constrained by scope, specificity, and dataset quality •enables external validation and access to larger datasets but access might be inconvenient and data not interoperable •holds significant potential for researchers, but is restricted by communication flow and available personnel capacity •To advance data-driven biomedical research, future efforts should focus on simplifying and accelerating ethical and legal procedures •DIC capacities should be strengthened through adequate institutional and governmental support •Promoting interoperability via harmonised data formats enables integration across hospitals and datasets, improving data reuse and analysis This work was supported by the Leibniz ScienceCampus Digital Transformation of Research (DiTraRe). How Researchers Access Clinical Data: Resources, Pathways, and Challenges S. Becker1,2, L. Singson3, E. Silaban3, A. Krishna2, T. Keller2,4, D. Westermann2, T. Arentz2, F. Boehm3, M. Eichenlaub2, A. Loewe1 Introduction Conclusion & Outlook •In the health sector, digitalisation has led to the rapid growth of digital health data •This work aims to provide an overview of the various sources of clinical data used in research, their access pathways and bottlenecks encountered throughout the data acquisition and utilisation process by researchers and their institutions •Fast access •Low-cost •Might not meet highly specific data or metadata needs •Limited data quality •Limited data availability due to confidentiality (e.g. DNA data) 1Karlsruhe Institute of Technology, Karlsruhe, Germany; 2University of Freiburg, Bad Krozingen; 3FIZ Karlsruhe –Leibniz Institute for Information Infrastructure, Eggenstein-Leopoldshafen; 4Department of Cardiology, Justus Liebig University Giessen, Giessen •High data quality •Data suited for external model validation •Ethics approval needed •Often inefficient and inconvenient data access •Limited interoperability between systems •High data quality •Data suited for external model validation •Data gets exported by DIC in an organised and pseudonymised way •Secure access to larger datasets •Interoperability •Ethics approval needed •Process might be slowed down by communication flow and availability of personnel at DIC •Resource-intensive a b