ReviewR: a light-weight and extensible tool for manual review of clinical records

Lay Summary When doing research using data from electronic health records (EHRs), data may need to be extracted by hand, either to perform the study or to ensure its accuracy. However, many researchers cannot access the EHR for this purpose. Even when researchers have access, they must flip between their review list, the EHR, and the location they are recording the results of their review, which is difficult and can cause errors. We developed a software application, ReviewR, to make this process easier and less error prone and have used it in 2 real-world projects. Objectives Manual record review is a crucial step for electronic health record (EHR)-based research, but it has poor workflows and is error prone. We sought to build a tool that provides a unified environment for data review and chart abstraction data entry. Materials and Methods ReviewR is an open-source R Shiny application that can be deployed on a single machine or made available to multiple users. It supports multiple data models and database systems, and integrates with the REDCap API for storing abstraction results. Results We describe 2 real-world uses and extensions of ReviewR. Since its release in April 2021 as a package on CRAN it has been downloaded 2204 times. Discussion and Conclusion ReviewR provides an easily accessible review interface for clinical data warehouses. Its modular, extensible, and open source nature afford future expansion by other researchers.

Automated summary

When conducting research using electronic health records, manually extracting data is a crucial but error-prone step. We have developed a tool called ReviewR to provide a unified environment for data review and chart abstraction data entry.