Linkage of Data from Diverse Data Sources (LDS): A Data Combination Model Provides Clinical Data of Corresponding Specimens in Biobanking Information System

  • Authors:
  • Okyaz Eminaga;Enver Özgür;Axel Semjonow;Jan Herden;Ilgar Akbarov;Ali Tok;Udo Engelmann;Sebastian Wille

  • Affiliations:
  • Department of Urology, University Hospital Cologne, Cologne, Germany 50937 and Department of Urology/Prostate Center, University Hospital Cologne, Cologne, Germany 50937;Department of Urology, University Hospital Cologne, Cologne, Germany 50937;Department of Urology, University Hospital Muenster, Muenster, Germany 48149;Department of Urology, University Hospital Cologne, Cologne, Germany 50937;Department of Urology, University Hospital Cologne, Cologne, Germany 50937;Department of Urology, University Hospital Cologne, Cologne, Germany 50937;Department of Urology, University Hospital Cologne, Cologne, Germany 50937;Department of Urology, University Hospital Cologne, Cologne, Germany 50937

  • Venue:
  • Journal of Medical Systems
  • Year:
  • 2013

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Abstract

To provide sufficient clinical data for corresponding specimens from diverse databases established before the implementation of biobanks for research purposes with respect to data privacy regulations. For this purpose, we developed a data model called "linkage of data from diverse data sources (LDS)". The data model was developed to merge clinical data from an existing local database with biospecimen repository data in our serum bank for uro-oncology. This concept combines two data models based on XML: the first stores information required to connect multiple data sources and retrieve clinical data, and the second provides a data architecture to acquire clinical and repository data. All data were anonymized and encrypted using the Advanced Encryption Standard. X.509 certificates were applied to secure data access. Furthermore, we tested the feasibility of implementing these models in the information management system for biobanking. The data concept can provide clinical and repository data of biospecimens. Only authorized receivers can access these data. Sensitive and personal data are not accessible by the data receivers. The data receiver cannot backtrack to the individual donor using the data model. The acquired data can be converted into a text file format supported by familiar statistical software. Supplementary tools were implemented to generate and view XML documents based on these data models. This data model provides an effective approach to distribute clinical and repository data from different data sources to enable data analysis compliant with data privacy regulations.