OA08.04 Validation of Scalable, Automated Data Extraction in an Advanced Lung Cancer Patient Population

Abstract

Manual extraction from electronic health records (EHRs) is currently the standard approach for accessing real-world healthcare data but can be time consuming and challenging to maintain over time. Automated data extraction using natural language processing (NLP) is emerging as a viable method of data extraction from structured and unstructured fields of EHRs. While speed of NLP-based data extraction is established, some question the validity of the extracted data. This study compares the accuracy of, and concordance between, manual and NLP-extracted data from EHRs of patients with advanced lung cancer (aLC).