Implementing innovative service delivery models in genetic counseling: a qualitative analysis of facilitators and...

Given the increasing demand for genetic counseling, implementation of innovative service delivery models (SDMs) has been proposed to improve access and increase the efficiency of genetic counseling services. This study investigated the key considerations in genetic counseling practices that impact the decision to implement innovative SDMs, as well as barriers and facilitators to implementation. A cross-sectional, qualitative research design was employed, using focus groups. Genetic counselors (GCs) were recruited from respondents to an online survey about SDMs and by posting an invitation to the National Society of Genetic Counselors (NSGC) listserv. A total of 30 participants participated in either in-person (n = 3) or online (n = 5) focus groups, facilitated by members of the Service Delivery Model subcommittee of the NSGC Access and Service Delivery Committee. Transcribed audio and written notes from the focus groups were coded, utilizing an inductive thematic analysis method. Five overarching themes that impact decisions to implement and use innovative SDMs were identified: (a) Patient volume and efficiency are key motivators in trying innovative SDMs; (b) the decision to change is facilitated by available resources; (c) the implementation of innovative SDMs is multidimensional and complex process; (d) there is concern about the impact on patient-provider relationships with the utilization of innovative SDMs; and (e) measuring outcomes of innovative SDM facilitates acquisition of additional resources and support. Approaches to innovative SDM selection and implementation vary by institution needs, resources, and population as demonstrated by the variety of approaches to similar barriers. Outcomes related to the implementation of innovative SDMs in genetic counseling practice should be measured to demonstrate the value of innovative genetic counseling SDMs, improvement of access to care, and to justify need for additional resources to support implementation of these models.