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

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.

AHD2FHIR: A Tool for Mapping of Natural Language Annotations to Fast Healthcare Interoperability Resources - A Technical Case Report.

A significant portion of data in Electronic Health Records is only available as unstructured text, such as surgical or finding reports, clinical notes and discharge summaries. To use this data for secondary purposes, natural language processing (NLP) tools are required to extract structured information. Furthermore, for interoperable use, harmonization of the data is necessary. HL7 Fast Healthcare Interoperability Resources (FHIR), an emerging standard for exchanging healthcare data, defines such a structured format. For German-language medical NLP, the tool Averbis Health Discovery (AHD) represents a comprehensive solution. AHD offers a proprietary REST interface for text analysis pipelines. To build a bridge between FHIR and this interface, we created a service that translates the communication around AHD from and to FHIR. The application is available under an open source license.

A Multilingual Browser Platform for Medical Subject Headings.

The National Library of Medicine (NLM) controls and publishes the thesaurus Medical Subject Headings which is used for indexing PubMed. Besides an XML export, the NLM offers a web based MeSH browser. The platform contains English terms. The German Institute for Medical Documentation and Information (DIMDI) partially translated and published these terms. Recently, the German National Library of Medicine (ZB-MED) overtook the translation of MeSH. However, there is no dedicated platform which focuses on MeSH and covers multiple languages. Here, we address this gap, by offering a modern multilingual searchable MeSH browser. A modular platform using open source technology is presented. The frontend enables the user to search and browse terms and switch between different languages. The current version of the presented MeSH browser contains English and German MeSH terms and can be accessed at https://mesh-browser.de.