Generalizable EHR-R-REDCap pipeline for a national multi-institutional rare tumor patient registry

Objective: To develop a clinical informatics pipeline designed to capture
large-scale structured EHR data for a national patient registry. Materials
and Methods: The EHR-R-REDCap pipeline is implemented using R-statistical
software to remap and import structured EHR data into the REDCap-based
multi-institutional Merkel Cell Carcinoma (MCC) Patient Registry using an
adaptable data dictionary. Results: Clinical laboratory data were
extracted from EPIC Clarity across several participating institutions.
Labs were transformed, remapped and imported into the MCC registry using
the EHR labs abstraction (eLAB) pipeline. Forty-nine clinical tests
encompassing 482,450 results were imported into the registry for 1,109
enrolled MCC patients. Data-quality assessment revealed highly accurate,
valid labs. Univariate modeling was performed for labs at baseline on
overall survival (N=176) using this clinical informatics pipeline.
Conclusion: We demonstrate feasibility of the facile eLAB workflow. EHR
data is successfully transformed, and bulk-loaded/imported into a
REDCap-based national registry to execute real-world data analysis and
interoperability.