Identifying individuals with inherited cancer susceptibility is critical for targeted cancer prevention, screening,
and treatment. Strategies to assess the genetic risk of unaffected individuals are needed. Scalable and
sustainable methods to automatically extract and analyze family history information routinely captured in the
electronic health record (EHR) can identify primary care patients appropriate for cancer genetic services.
Increased patient ascertainment needs to be paired with implementation studies to compare models of
delivering genetic services, including patient-directed models. Because access to services continues to be a
barrier for those from minority racial and ethnic groups and rural areas, examining responses to different
delivery models across population subgroups is essential. This study will employ an implementation science
framework to test a replicable EHR-based clinical decision support (CDS) infrastructure to: (i) automatically
identify unaffected patients from 48 primary care clinics in two healthcare systems, University of Utah and New
York University, who qualify for cancer genetic services (Aim 1); and (ii) compare two models of genetic
services delivery for 1,920 primary care patients using a randomized trial design with clinic-level randomization
(Aims 2 and 3). We hypothesize that the CDS infrastructure will identify additional patients who have not been
previously referred (Aim 1) and that uptake of genetic testing (Aim 2) and adherence to management
recommendations (Aim 3) will be equivalent between the models. To address Aim 1, we will evaluate whether
the CDS approach identifies patients who have not previously been referred, and whether this varies by
race/ethnicity and rurality. To address Aim 2, we will compare: a patient-directed model in which those
identified by the CDS infrastructure as meeting testing criteria will be informed of their cancer risks, provided
with educational resources, and offered the option to select genetic testing through a patient portal to an
enhanced standard of care model in which providers and patients are notified through CDS when criteria are
met and of the availability of standard of care genetic counseling. We will compare uptake of genetic testing by
model and whether this differs by race/ethnicity and rurality. In Aim 3, we will compare the effects of the two
delivery models on adherence to recommendations 12 months after return of results, examining differences in
effects by race/ethnicity and rurality. Innovative features include implementation of population-based CDS
assessment of family history information available in the EHR; comparison of outcomes of patient-directed and
enhanced standard of care delivery models; and focus on impact of race/ethnicity and rurality. This highly
impactful translational research builds on our unique strengths in cancer genetics, clinical informatics, and
population sciences, and addresses issues of immediate clinical significance, including increasing hereditary
cancer genetic testing in appropriate patients and improving access for underserved groups.
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