Secondary use of patient data within decentralized studies using the example of rare diseases in Germany: A data scientist's exploration of process and lessons learned.

Autor:	Zoch M; Institute for Medical Informatics and Biometry, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany., Gierschner C; Institute for Medical Informatics and Biometry, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany., Andreeff AK; Institute for Medical Informatics and Biometry, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany., Henke E; Institute for Medical Informatics and Biometry, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany., Sedlmayr M; Institute for Medical Informatics and Biometry, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany., Müller G; Center for Evidence-based Healthcare, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany., Tippmann J; Thiem-Research GmbH, Cottbus, Germany., Hebestreit H; Center for Rare Diseases - Reference Center Northern Bavaria, University Hospital, Julius-Maximilians University, Würzburg, Germany., Choukair D; Center for Rare Diseases, University Hospital Heidelberg, Heidelberg, Germany., Hoffmann GF; Center for Rare Diseases, University Hospital Heidelberg, Heidelberg, Germany., Fritz-Kebede F; Center for Rare Diseases, University Hospital Heidelberg, Heidelberg, Germany., Toepfner N; University Centre for Rare Diseases and Department of Pediatrics, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany., Berner R; University Centre for Rare Diseases and Department of Pediatrics, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany., Biergans S; Medical Data Integration Center (meDIC), University Hospital Tübingen, Tübingen, Germany., Verbücheln R; Medical Data Integration Center (meDIC), University Hospital Tübingen, Tübingen, Germany., Schaaf J; Institute of Medical Informatics, Goethe University Frankfurt, University Hospital, Frankfurt, Germany., Fleck J; Center for Rare Diseases, RWTH Aachen University Hospital, Aachen, Germany., Wirth FN; Berlin Institute of Health (BIH) at Charité Universitätsmedizin Berlin, Berlin, Germany., Schepers J; Berlin Institute of Health (BIH) at Charité Universitätsmedizin Berlin, Berlin, Germany., Prasser F; Berlin Institute of Health (BIH) at Charité Universitätsmedizin Berlin, Berlin, Germany.
Jazyk:	angličtina
Zdroj:	Digital health [Digit Health] 2024 Aug 10; Vol. 10, pp. 20552076241265219. Date of Electronic Publication: 2024 Aug 10 (Print Publication: 2024).
DOI:	10.1177/20552076241265219
Abstrakt:	Objective: Unlocking the potential of routine medical data for clinical research requires the analysis of data from multiple healthcare institutions. However, according to German data protection regulations, data can often not leave the individual institutions and decentralized approaches are needed. Decentralized studies face challenges regarding coordination, technical infrastructure, interoperability and regulatory compliance. Rare diseases are an important prototype research focus for decentralized data analyses, as patients are rare by definition and adequate cohort sizes can only be reached if data from multiple sites is combined. Methods: Within the project "Collaboration on Rare Diseases", decentralized studies focusing on four rare diseases (cystic fibrosis, phenylketonuria, Kawasaki disease, multisystem inflammatory syndrome in children) were conducted at 17 German university hospitals. Therefore, a data management process for decentralized studies was developed by an interdisciplinary team of experts from medicine, public health and data science. Along the process, lessons learned were formulated and discussed. Results: The process consists of eight steps and includes sub-processes for the definition of medical use cases, script development and data management. The lessons learned include on the one hand the organization and administration of the studies (collaboration of experts, use of standardized forms and publication of project information), and on the other hand the development of scripts and analysis (dependency on the database, use of standards and open source tools, feedback loops, anonymization). Conclusions: This work captures central challenges and describes possible solutions and can hence serve as a solid basis for the implementation and conduction of similar decentralized studies. Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. (© The Author(s) 2024.)
Databáze:	MEDLINE
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