Large‐scale group genetic counseling: Evaluation of a novel service delivery model in a Canadian hereditary cancer clinic. Issue 2 (1st October 2021) - Lohn, Zoe; Fok, Alexandra; Richardson, Matthew; Derocher, Heather; Mung, Sze Wing; Nuk, Jennifer; Yuson, Jamie; Jevon, Mandy; A.Schrader, Kasmintan; Sun, Sophie
Abstract: Increasing demand for genetic services has led to the development of streamlined genetic counseling (GC) models. We piloted large‐scale group pre‐test GC with up to 50 patients per group and compared this to a traditional one‐on‐one approach. Patients referred to the British Columbia (BC) Cancer Hereditary Cancer Program were eligible if they had: (a) family history meeting our program's referral criteria; (b) no relevant personal history of cancer; (c) no prior genetic testing in the family; and (d) no living testable relative in BC. Patient‐reported outcome measures included: (a) Genetic Counselling Outcome Scale (GCOS) prior to pre‐test GC (T1) and at 4 weeks post‐test GC (T2); (b) Satisfaction Survey after pre‐test GC; and (c) the Multidimensional Impact of Cancer Risk Assessment (MICRA) for patients undergoing testing (4 weeks after post‐test GC). In total, 391 patients underwent GC, 184 by group and 207 by one‐on‐one appointments. Between May 2018 and May 2019, 6 pre‐test group sessions were conducted (median number of patients per group = 28; range 15–48). 8% of patients ( n = 32) declined large group GC due to personal preference for one‐on‐one GC. There were no statistically significant differences in MICRA and GCOS survey results when comparing the pre‐test large group versus traditional pre‐test one‐on‐one models (based on 3 MICRA subscales: p = 0.063, p = 0.612, p = 0.842; and GCOS p = 0.169). Overall, the large group pre‐test counseling approach was more time‐efficient with 15–48 patient group sessions conducted over a mean duration of 80 min as compared to 42 min per patient with the traditional one‐on‐one GC model. Large‐scale group GC was feasible and acceptable to patients and represents a novel streamlined model for GC to enable timely access to cancer genetic services.