Abstract

Objective

i2b2 offers the possibility to store biomedical data of different projects in subject oriented data marts of the data warehouse, which potentially requires data replication between different projects and also data synchronization in case of data changes. We present an approach that can save this effort and assess its query performance in a case study that reflects real-world scenarios.

Material and Methods

For data segregation, we used PostgreSQL’s row level security (RLS) feature, the unit test framework pgTAP for validation and testing as well as the i2b2 application. No change of the i2b2 code was required. Instead, to leverage orchestration and deployment, we additionally implemented a command line interface (CLI). We evaluated performance using 3 different queries generated by i2b2, which we performed on an enlarged Harvard demo dataset.

Results

We introduce the open source Python CLI i2b2rls, which orchestrates and manages security roles to implement data marts so that they do not need to be replicated and synchronized as different i2b2 projects. Our evaluation showed that our approach is on average 3.55 and on median 2.71 times slower compared to classic i2b2 data marts, but has more flexibility and easier setup.

Conclusion

The RLS-based approach is particularly useful in a scenario with many projects, where data is constantly updated, user and group requirements change frequently or complex user authorization requirements have to be defined. The approach applies to both the i2b2 interface and direct database access.

Details

Title
Integrating row level security in i2b2: segregation of medical records into data marts without data replication and synchronization
Author
Scheible, Raphael 1   VIAFID ORCID Logo  ; Thomczyk, Fabian 2 ; Blum, Marco 2   VIAFID ORCID Logo  ; Rautenberg, Micha 3   VIAFID ORCID Logo  ; Prunotto, Andrea 2 ; Yazijy, Suhail 3 ; Boeker, Martin 1   VIAFID ORCID Logo 

 Institute of Artificial Intelligence and Informatics in Medicine (AIIM), Chair of Medical Informatics, University Hospital rechts der Isar, School of Medicine, Technical University of Munich , Munich, Germany 
 Data Inintegration Center (DIC), University of Freiburg , Freiburg, Germany 
 Institute of Medical Biometry and Statistics, Medical Center, Faculty of Medicine, University of Freiburg , Freiburg, Germany 
Publication year
2023
Publication date
Oct 2023
Publisher
Oxford University Press
e-ISSN
25742531
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3168347523
Copyright
© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.