From the ground up: Launching <scp>GENETECA</scp> ™ ( <scp>GENetic</scp> education and <scp>TEsting</scp> for <scp>CAncer</scp> ) a point‐of‐care cancer genetics service at an academic medical center

In 2020, the UCLA Cancer Genetics service launched a point-of-care pilot program called GENETECA™ (GENetic Education and TEsting for CAncer) in the Pancreatic Cancer Integrated Practice Unit (PancIPU) to comply with national guidelines recommending universal germline genetic testing (gGT). We present a guide for the implementation of similar models at other institutions along with the uptake and outcomes of GENETECA. Patients with pancreatic ductal adenocarcinoma (PDAC) obtaining care at UCLA Health between August 12, 2020 and January 1, 2023 were identified through electronic medical record review. Abstracted data included patient demographics and GENETECA endpoints (gGT, opted for traditional genetic counseling visit, declined, or loss to follow up). Data were analyzed using Chi-squared testing for categorical variables and Wilcoxon rank-sum tests for non-parametric data. In 2019, prior to the implementation of GENETECA, only 9% (18/194) of patients with PDAC seen at PancIPU had pre-test counseling and gGT by cancer genetic counselors. After the implementation of GENETECA, 94% (222 out of 237) of PDAC patients were offered gGT. Approximately 80% of patients with PDAC who were offered GENETECA proceeded with testing, independent of patient age. Non-English speakers were no less likely to complete GENETECA than English-speaking patients, and no significant difference in completion was observed across races. Implementing this point-of-care, high-throughput approach significantly improved compliance with national guidelines and proved to be a cost-effective service delivery model. Its success has allowed us to procure additional financial and health system support to expand cancer genetic services across many other tumor-site specialty clinics. Our described implementation may be used as a framework for other institutions to implement similar models.