Leveraging an Informatics Approach to Identify an Unmet Clinical Need for / Testing Among Patients With Ovarian Cancer.

PURPOSE

Although / testing in ovarian cancer improves outcomes, it is vastly underutilized. Scalable approaches are urgently needed to improve genomically guided care.

METHODS

We developed a Natural Language Processing (NLP) pipeline to extract electronic medical record information to identify recipients of 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.

RESULTS

We found high uptake of 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 untested = 65 years, = .01), and higher testing among Hispanic (84%) compared with White, Non-Hispanic (78%), and Asian (75%) patients ( = .006). Documentation of referral for an internal genetics consultation for pathogenic variant carriers was higher at the MC compared with the CNS (94% 31%).

CONCLUSION

We were able to successfully use a novel NLP pipeline to assess use of testing among patients with ovarian cancer. Despite relatively high levels of testing at our institution, 22% of patients had no documentation of genetic testing and documentation of referral to genetics among 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.