RW4 THE ROLE OF ELECTRONIC HEALTH RECORDS IN THE ADVANCEMENT OF REAL WORLD EVIDENCE BASED RESEARCH IN ONCOLOGY

Abstract

In the recent years, real-world evidence (RWE) has evolved as a complementary source of evidence for traditional clinical trials. Electronic health records (EHRs) generated from clinical practices is one of the vital components RWE. This systematic review assessed the range of analytical questions answered by EHR-based studies. Research articles reporting any oncology EHR, published in Pubmed between Jan 1, 2015 and Aug 5, 2019 were included. We evaluated the objective(s), study endpoints, and analytical approaches adopted in each publication. A total of 3906 abstracts were screened, of which 35 were included, with 27 (77%) being published in the past 2 years. Studies ranged from basic descriptive to advanced analytics including correlations studies and predictive models. Treatment pattern was most frequently studied (37%), followed by comparative effectiveness (14%), data extraction validations (14%), clinical effectiveness (9%), disease prediction models (9%), safety (6%), burden of illness (3%), cost-effectiveness (3%), treatment drivers (3%), and patient journey (3%). Treatment pattern was frequently accompanied by treatment outcome (10/13), often assessing how change impacts survival. Regarding EHR data, we observed use of both structured and unstructured information. Seven publications (20%) accessed unstructured data (e.g., clinician notes, lab/radiology reports) to understand underlying drivers of observed trends. However, many of these studies had indicated that such analyses are limited by small sample sizes or limitations of available manual data extraction techniques. There has been a notable increase in EHR publications in the past 2 years, with a majority of research focused on practice trends and clinical outcomes. Although advanced analytical approaches have enabled the use of structured and unstructured data from EHR notes to understand drivers of treatment decisions, there is more opportunity to enhance data completeness and improve extraction techniques.