Leveraging an Informatics Approach to Identify an Unmet Clinical Need for <i>BRCA1</i>/<i>2</i> Testing Among Patients With Ovarian Cancer.

Abstract

Although <i>BRCA1</i>/<i>2</i> testing in ovarian cancer improves outcomes, it is vastly underutilized. Scalable approaches are urgently needed to improve genomically guided care. We developed a Natural Language Processing (NLP) pipeline to extract electronic medical record information to identify recipients of <i>BRCA</i> testing. We applied the NLP pipeline to assess testing status in 308 patients with ovarian cancer receiving care at a National Cancer Institute Comprehensive Cancer Center (main campus [MC] and five affiliated clinical network sites [CNS]) from 2017 to 2019. We compared characteristics between (1) patients who had/had not received testing and (2) testing utilization by site. We found high uptake of <i>BRCA</i> testing (approximately 78%) from 2017 to 2019 with no significant differences between the MC and CNS. We observed an increase in testing over time (67%-85%), higher uptake of testing among younger patients (mean age tested = 61 years <i>v</i> untested = 65 years, <i>P</i> = .01), and higher testing among Hispanic (84%) compared with White, Non-Hispanic (78%), and Asian (75%) patients (<i>P</i> = .006). Documentation of referral for an internal genetics consultation for <i>BRCA</i> pathogenic variant carriers was higher at the MC compared with the CNS (94% <i>v</i> 31%). We were able to successfully use a novel NLP pipeline to assess use of <i>BRCA</i> testing among patients with ovarian cancer. Despite relatively high levels of <i>BRCA</i> testing at our institution, 22% of patients had no documentation of genetic testing and documentation of referral to genetics among <i>BRCA</i> carriers in the CNS was low. Given success of the NLP pipeline, such an informatics-based approach holds promise as a scalable solution to identify gaps in genetic testing to ensure optimal treatment interventions in a timely manner.