ArticleJournal of healthcare engineering2019
Experiences of Transforming a Complex Nephrologic Care and Research Database into i2b2 Using the IDRT Tools.
Article in Journal of healthcare engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Ten Years of Medical Informatics and Standards Support for Clinical Research in an Infectious Diseases Network.Applied clinical informatics · 2023Article
- ELII: A novel inverted index for fast temporal query, with application to a large Covid-19 EHR dataset.Journal of biomedical informatics · 2021Article
- Content Coverage Evaluation of the OMOP Vocabulary on the Transplant Domain Focusing on Concepts Relevant for Kidney Transplant Outcomes Analysis.Applied clinical informatics · 2020Article
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Authors and funding
7 authors.
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Abstract
The secondary use of data from electronic medical records has become an important factor to determine and to identify various causes of disease. For this reason, applications like informatics for integrating biology and the bedside (i2b2) offer a GUI-based front end to select patient cohorts. To make use of those tools, however, clinical data need to be extracted from the Electronic Health Record (EHR) system and integrated into the data schema of i2b2. We used TBase, a documentation system for nephrologic transplantations, as a source system and applied the Integrated Data Repository Toolkit (IDRT) for the Extract, Transform, and Load (ETL) process to load the data into i2b2. Since i2b2 uses an entity-attribute-value (EAV) schema, which is a fundamentally different way of modeling data in comparison to a standard relational schema in TBase, we evaluated if (a) the data relationship of the source system entities can still be represented in the i2b2 schema and if (b) the IDRT is a suitable solution for loading the data of a comprehensive data schema like TBase into i2b2. For that reason, we identified entities in the TBase data schema which were relevant for answering questions on cohort identification. By doing so, we found out that the entities had different structures that needed to be handled differently for the ETL process. Furthermore, the use of IDRT revealed shortcomings with regard to large input data and specific data structures that are part of most modern EHR systems. However, this project also showed that our way of modeling the TBase data in i2b2 has been proven to be successful in terms of answering the most common questions of clinicians on cohort identification.
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