Indications for germline genetic testing (GT) for hereditary cancer predispositions have increased; however, a critical shortage of genetic counselors (GCs) necessitates the implementation of alternative delivery models. Here, we present the design and implementation of a coordinator-based alternative GT delivery model, called Fast-Track, embedded in a comprehensive cancer genetics program with oversight by GCs. The Fast-Track Program includes visits with clinical genetics coordinators (GCCs) who are trained by GCs in cancer genetic pre-test education and testing. Referrals received to the Smilow Cancer Genetics and Prevention Program (SCGP) are reviewed and triaged by GCs; patients that meet National Comprehensive Cancer Network (NCCN) guidelines for GT are then assigned to the Fast-Track Program. Patients are shown an educational video, which was created by the SCGP and have GT coordinated by the GCC. All cases are reviewed by a GC after the initial visit, regardless of whether GT is completed, for appropriate medical management recommendations. All patients with pathogenic/likely pathogenic variants (PGVs) identified or with complicated results are scheduled with a GC for result disclosure, while remaining patients have disclosure by GCCs. The Fast-Track Program was implemented in June 2023, and this report includes data from 6/12/2023 to 3/29/2024. During this 9-month period, 415 patients were seen in the Fast-Track Program and 12.3% of patients tested had PGVs identified. Mean days from the referral date to the appointment date were significantly shorter for the FT program compared with the standard GC pipeline (21.92 and 90.14 days, respectively) (p < 0.001). A coordinator-based genetics delivery model has been successful in expediting GT visits and is expanding at our institution. Oversight by GCs is essential to ensure standard-of-care delivery for cancer GT in the precision medicine era. Future work evaluating additional quality-of-care outcomes, such as patient satisfaction, will be essential to fully contextualize this model's impact.
Wiegand et al. (Wed,) studied this question.