Objectives: Clinico-Genomic Data (CGD) acquired through routine clinical practice has the potential to improve our understanding of clinical oncology. However, these data often reside in heterogeneous and semi-structured data, resulting in prolonged time-to-analyses.
Materials and Methods: We created GENETEX: an R package and Shiny application for text mining genomic reports from EHR and direct import into REDCap^®.
Results: GENETEX facilitates the abstraction of CGD from EHR and streamlines capture of structured data into REDCap^®. Its functions include natural language processing of key genomic information, transformation of semi-structured data into structured data and importation into REDCap. When evaluated with manual abstraction, GENETEX had >99% agreement and captured CGD in approximately one-fifth the time.
Conclusions: GENETEX is freely available under the Massachusetts Institute of Technology license and can be obtained from GitHub. GENETEX is executed in R and deployed as a Shiny application for non-R users. It produces high-fidelity abstraction of CGD in a fraction of the time.

The dataset was collected during a study for the GENETEX manuscript. It contains the data used for the section on "Real-world deployment". In order to evaluate the performance of augmented abstraction with GENETEX in the real-world setting compared to manual capture, we selected 7 genomic reports at random (3 Guardant, 2 Foundation Medicine, 1 MGH SNaPshot, and 1 BWH Oncopanel) for abstraction. Each report was abstracted independently by 2 data abstractors via manual abstraction, as well as with GENETEX. The data from that abstraction is conained in this csv file.