ROMOP: a light-weight R package for interfacing with OMOP-formatted electronic health record data

Abstract

Objectives: Electronic 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 Out-comes 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 methods: We have created ROMOP: an R package for direct interfacing with EHR data in the OMOP CDM format.

Results: ROMOP 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.

Conclusion: ROMOP 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).

Figure 1.

Schematic and structure of ROMOP. (A) ROMOP interfaces with OMOP-formatted EHR data in a relational database structure. Users can retrieve all relevant clinical, demographic, and encounter data for a list of patients directly into an R object as well as search for patients using any vocabulary contained within the OMOP CDM. (B) When searching for patients using clinical vocabularies, ROMOP offers the option to automatically map (“map” option) to the standardized ontology (eg, SNOMED) and query all concepts lower in the hierarchy (ie, descendants). Alternatively users can search for terms directly (“direct” option). ROMOP identifies the relevant clinical tables and fields to query and can produce a set of outputs detailing the query and outcomes. CDM: common data model; EHR: electronic health record; OMOP: Observational Medical Outcomes Partnership.