Abstract
ObjectivesElectronic health record (EHR) data are increasingly used for biomedical discoveries. The nature of the data, however, requires expertise in both data science and EHR structure. The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) standardizes the language and structure of EHR data to promote interoperability of EHR data for research. While the OMOP CDM is valuable and more attuned to research purposes, it still requires extensive domain knowledge to utilize effectively, potentially limiting more widespread adoption of EHR data for research and quality improvement.Materials and methodsWe have created ROMOP: an R package for direct interfacing with EHR data in the OMOP CDM format.ResultsROMOP streamlines typical EHR-related data processes. Its functions include exploration of data types, extraction and summarization of patient clinical and demographic data, and patient searches using any CDM vocabulary concept.ConclusionROMOP is freely available under the Massachusetts Institute of Technology (MIT) license and can be obtained from GitHub (http://github.com/BenGlicksberg/ROMOP). We detail instructions for setup and use in the Supplementary Materials. Additionally, we provide a public sandbox server containing synthesized clinical data for users to explore OMOP data and ROMOP (http://romop.ucsf.edu).
Highlights
AND SIGNIFICANCEWidespread adoption of electronic health records (EHRs) into health systems is ushering clinical data into the digital age.[1]
As readers may not have access to EHR data in Observational Medical Outcomes Partnership (OMOP) format, we provide a sandbox server that has these data in a database where users can explore the common data model (CDM) as well as the ROMOP package
While the functionality afforded by this package pales in comparison to the advanced and multifaceted tools provided by the Observational Health Data Sciences and Informatics (OHDSI) group, it will hopefully facilitate EHR data mining for users unfamiliar with the OMOP structure
Summary
Widespread adoption of electronic health records (EHRs) into health systems is ushering clinical data into the digital age.[1] Largescale analytics of EHR data have produced impactful discoveries that have, in turn, enabled the practice of precision medicine.[2] It is clear that biomedical researchers in all areas including practicing clinicians, data scientists, and wet lab biologists, could enhance their work by integrating findings taken from this “real world evidence.”. There exist many barriers that limit usability of EHR data, revolving primarily around available expertise. As EHR data are large scale and typically stored in relational databases, some knowledge of structured query language (SQL) programing is required, which many clinicians and scientists do not have experience with nor the time to sufficiently gain.
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