P578: Rapid genomic testing: A retrospective study of institutional utilization and outcomes

Introduction: Inherited cancer-predisposing syndromes account for 3% of cancers, primarily Hereditary Breast and Ovarian Cancer Syndrome and Lynch Syndrome.Surveillance and preventive measures for diagnosed individuals facilitate early detection and appropriate treatment, ultimately reducing cancer mortality.Identifying at-risk individuals through genetic testing is crucial.Although guidelines exist to identify candidates based on personal and family cancer history, fewer than 30% of eligible individuals are currently tested, and these numbers are significantly worse among underserved populations.The complexity of guidelines and clinicians' unconscious biases contribute to these disparities.This project aimed to enhance the identification and testing of at-risk individuals, focusing on underserved populations.We hypothesized that automatic identification and testing could be achieved by leveraging the electronic health records (EHR) in EPIC through the ARCAGEN-ID (At-Risk Cancer Genetic Syndrome Identification) system.Methods: The ARCAGEN-ID is a novel identification system created at Yale New Haven Hospital.A virtual cohort of individuals was identified using an inclusion logic of 218 rules that assessed personal and family cancer history, aligned with NCCN/ACMG guidelines.Structured data were extracted from EHR fields, and external data were accessed by enabling the standard interoperability exchange of information system.The system was applied to a wellness registry comprising 1,325,545 patients with active EHRs.A proof-of-concept automated outreach initiative was developed for the Lynch registry cohort only.Using the EPIC Campaigns module, patients at risk received an automated message with an informational video on genetic testing.Patients could select one of four options: 1) pursue genetic testing, 2) decline testing, 3) report prior testing, or 4) consult with a genetic counselor.Saliva DNA testing kits were sent to patients who opted for testing.Positive results prompted appointments at the LS clinic or other high-risk programs according to the identified pathogenic variant (PV), while negative results were communicated via EHR.Race, ethnicity and sex were self-reported based on the options provided by the hospital at registration.Results: The ARCAGEN-ID system identified 59,235 at-risk individuals (4.5%).Of these, 46,982 (79%) had no prior genetic testing, evaluation, or referral to cancer genetics.A manual chart review of 559 cases indicated an identification accuracy of 96.2%.The system notably identified a higher proportion of selfreported Hispanic, non-Hispanic African American, and Medicaid-insured patients compared to previous identification methods, increasing recognition of underserved communities by 14%, 20%, and 23%, respectively.Family history of cancer was the single largest contributor (~81%) to identification by ARCAGEN-ID.From January to May 2024, 504 patients were contacted; 25% viewed the video and completed the questionnaire.Among them, 10% requested testing, 7% reported previous testing, 2% declined testing, and 6% sought to speak with a genetic counselor.Among the latter group, 67% ended up pursuing testing after discussions.A total of 43 patients pursued testing, with 7 (16%) identified as harboring PVs.Notably, a higher proportion of African American patients opted for testing through this strategy (11%) compared to the overall percentage of this population that was outreached (6%). Conclusion:This study underscores the transformative potential of the ARCAGEN-ID system in identifying at-risk individuals for inherited cancer syndromes.By automating the identification process through the EHR, we can significantly increase the testing rates among underserved populations, addressing critical health disparities.The high accuracy of our identification methods and the effective outreach initiative demonstrate that technology can enhance patient engagement and facilitate access to genetic testing.This work not only bridges the gap in care for underserved communities but also establishes a scalable model for integrating genetic risk assessment into routine clinical practice, ultimately improving early detection and outcomes for hereditary cancers.