Assessing the Contribution of Scanned Outside Documents to the Completeness of Real-World Data Abstraction

JCO Clin Cancer Inform. 2023 Feb:7:e2200118. doi: 10.1200/CCI.22.00118.

Abstract

Purpose: Electronic health record (EHR) data are widely used in precision medicine, quality improvement, disease surveillance, and population health management. However, a significant amount of EHR data are stored in unstructured formats including scanned documents external to the treatment facility presenting an informatics challenge for secondary use. Studies are needed to characterize the clinical information uniquely available in scanned outside documents (SODs) to understand to what extent the availability of such information affects the use of these real-world data for cancer research.

Materials and methods: Two independent EHR data abstractions capturing 30 variables commonly used in oncology research were conducted for 125 patients treated for advanced non-small-cell lung cancer at a comprehensive cancer center, with and without consideration of SODs. Completeness and concordance were compared between the two abstractions, overall, and by patient groups and variable types.

Results: The overall completeness of the data with SODs was 77.6% as compared with 54.3% for the abstraction without SODs. The differences in completeness were driven by data related to biomarker tests, which were more likely to be uniquely available in SODs. Such data were prone to missingness among patients who were diagnosed externally.

Conclusion: There were no major differences in completeness between the two abstractions by demographics, diagnosis, disease progression, performance status, or oral therapy use. However, biomarker data were more likely to be uniquely contained in the SODs. Our findings may help cancer centers prioritize the types of SOD data being abstracted for research or other secondary purposes.

MeSH terms

  • Carcinoma, Non-Small-Cell Lung* / diagnosis
  • Carcinoma, Non-Small-Cell Lung* / epidemiology
  • Disease Progression
  • Electronic Health Records
  • Humans
  • Lung Neoplasms* / diagnosis
  • Lung Neoplasms* / epidemiology
  • Medical Oncology