Background: The development of Clinical data Warehouses (CDWs) has greatly increased access to big data in medical research. However, the lack of standardization among different data models hampers interoperability an...
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Background: The development of Clinical data Warehouses (CDWs) has greatly increased access to big data in medical research. However, the lack of standardization among different data models hampers interoperability and, consequently, the research potential of these vast data resources. Moreover, data manipulation and analysis require advanced programming skills, a skill set that healthcare professionals often lack. Methods: To address these issues, we created an open source, low-code and collaborative data science platform for manipulating, visualizing and analyzing healthcare data using graphical tools alongside an advanced programming interface. The software is based on the OMOP Common data Model. Results: LinkR enables users to generate studies using data imported from multiple sources. The software organizes the studies into two main sections: individual and population data sections. In the individual data section, user-friendly graphical tools allow users to customize data presentation, recreating the equivalent of a medical record, according to the needs of their study. The population data section is designed for conducting statistical analyses through both graphical and programming interfaces. The application also integrates a Git module, streamlining collaboration and facilitating shared dataanalysis across research centers. The platform was tested with datasets including the OMOP database (46,520 patients and over 36 million rows in the measurement table) during the InterHop datathon with 12 concurrent users. Usability testing yielded a median System Usability Scale (SUS) score of 75 [63.8-85.6], indicating high user satisfaction. Conclusion: LinkR is a low-codedata science platform that democratizes access, manipulation, and analysis of data from clinical data warehouses and facilitates collaborative work on healthcare data, using an open science approach.
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