1. University of Florida CTS-IT: Automated Data Translation from EMR to REDCap
Problem
Research often depends upon reliable access to clinical data. Many hours can be spent entering EMR data into a REDCap project. Time and money could be better spent if we had a way to obtain data from the EMR and push it into REDCap in an automated fashion.
Solution
CTS-IT has built RED-i, a data-driven program that uses XML trees to translate data elements. Through translation tables the data is transformed into REDCap EAV data. RED-i interfaces directly with the REDCap API to load the data. In addition, CTS-IT has built a Person Index tool which allows researchers to enter patient identifiers for lookup in the institution’s EMR without exposing the identifiers to the software maintainers. RED-i utilizes component mapping to a data dictionary of LOINC components to reduce ambiguity.
EMR Data
Source
Form-Element
data
Input
Output
Processing
Customer
Get EMR Data
RED-I
REDCap Data
Log Data
Warnings
Statistics
Data
Issues
A fully populated EMR can contain many similar field names and values. It can be challenging to ascertain which are of relevance to a study. The abundance of data can also lead to accidental exposure to PHI for which a researcher may not be approved. Finally, researchers and abstractors spend far too much of their valuable time handling data entry tasks.
Results
Using our RED-i software suite, researchers can upload their data request and have it processed end-to-end automatically. The result is a REDCap project that is populated with EMR data in just minutes, rather than hours. By using data standards, we are able to easily configure RED-i to run with a variety of data sources and types of EMR systems.
Alex Loiacono 1, Erik Schmidt 1, Nicholas Rejack 1, Philip Chase1
1 Clinical and Translational Science Informatics and Technology, University of Florida