Integrating row level security in i2b2: segregation of medical records into data marts without data replication and synchronization

Author:

Scheible Raphael12ORCID,Thomczyk Fabian3,Blum Marco3ORCID,Rautenberg Micha45ORCID,Prunotto Andrea3,Yazijy Suhail4,Boeker Martin1ORCID

Affiliation:

1. 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

2. Center for Chronic Immunodeficiency (CCI), Medical Center, Faculty of Medicine, University of Freiburg , Freiburg, Germany

3. Data Inintegration Center (DIC), University of Freiburg , Freiburg, Germany

4. Institute of Medical Biometry and Statistics, Medical Center, Faculty of Medicine, University of Freiburg , Freiburg, Germany

5. Zentrum für Digitalisierung und Informationstechnologie (ZDI), Medical Center, University of Freiburg , Freiburg, Germany

Abstract

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.

Funder

German Ministry for Education and Research

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

Reference26 articles.

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3. Big data warehouse for healthcare-sensitive data applications;Shahid;Sensors,2021

4. Data extraction and ad hoc query of an entity—attribute—value database;Nadkarni;J Am Med Inform Assoc JAMIA,1998

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