Mainstreaming Genetics: Evaluation of a Digital Application to Scale and Spread Oncologist-initiated Genetic Testing

Background: Germline genetic testing can alter therapy and surgical management for cancer patients. Yet, the rising demands for genetic testing and counseling has added pressure to already constrained genetic counseling (GC) service models that require extensive pre-test GC with a genetic counselor. Many cancer genetics centres are 'mainstreaming' genetic testing, replacing traditional pre-test GC, where genetic testing is initiated and mediated by oncologists, using clinician checklists, patient pamphlets, or videos. Most evidence on cancer genetics mainstream models to date derives from uncontrolled, non-randomized studies with limited outcomes. Electronic patient portals and digital decision tools have been shown to improve psychological outcomes.

Rationale: There is no standard, evidence-based approach to 'mainstreaming', leading to significant practice variation, a lack of coordinated care and ultimately, negative psychological impacts on patients. Digital solutions can address these gaps by providing a standardized, coordinated and patient-centred approach to deliver cancer genetic counseling. This study will address these gaps by testing the effectiveness of the Genetics Adviser for Mainstream Care (GA-Mainstream), building on our rigorously-evaluated digital health application called the Genetics Adviser.

Primary Objective: Assess the clinical effectiveness of the GA-Mainstream in improving psychological outcomes (e.g. test-specific distress), and patient-centred care (e.g. knowledge, and empowerment) for mainstream cancer patients compared to those receiving standard mainstream care.

Secondary Objectives:

* Measure the impact of the GA-Mainstream on patients' risk perceptions and intent to follow clinical recommendations (e.g. surveillance, treatment changes, or cascade testing). * Assess efficiencies in turnaround time to test result disclosure and time spent in genetic counselling sessions between groups. * Understand the experiences of patients and providers with the GA-Mainstream and the process of receiving genetic testing results through the platform.

Hypotheses

* Patients who use the GA-Mainstream will report lower distress and higher knowledge and empowerment compared to standard-of-care mainstream patients. * Patients using the GA-Mainstream will have more accurate risk perceptions and higher intent to follow clinical recommendations. * Using the GA-Mainstream will reduce turnaround time and increase efficiency during genetic counselling sessions.

Methods: This is a mixed-method, non-blinded randomized controlled superiority trial. The trial will evaluate whether the use of the GA-Mainstream reduces test-specific distress compared to standard mainstream genetic testing. As part of this trial, patients will receive germline genetic testing.

Study Population: Adult patients (aged \>18 years) referred for mainstream cancer genetic testing related to ovarian, pancreatic, breast, prostate, or GI cancers.

Sample Size: Our primary outcome, test-specific distress measured using the MICRA, has an accepted minimal clinically important difference (MCID) of 2.5. Assuming this, 64 participants/arm (128 total) are estimated to be required to have 80% power at a 2-sided significance level of 0.05 to detect the MCID using a standard two-sample t-test.

Intervention: Participants in the intervention arm will use the GA-Mainstream to support the delivery of their genetic test results, including collecting cancer medical history and family cancer history, pre- and post-result education. The GA-Mainstream will supplement mainstream standard of care.

Control: Participants in the control arm will receive standard of care for mainstream patients as outlined by the clinic where the patient is being seen, which consists of a patient pamphlet, video, or clinician checklist for pre-test counseling and results disclosure by either a genetic counselor or oncologist with post-test counseling of select patients.

Primary Outcome: The primary outcome is test- specific distress, measured by the Multidimensional Impact of Cancer Risk Assessment (MICRA).

Secondary Outcomes: Generalized distress, measured by the Hospital Anxiety and Depression Scale (HADS); Knowledge, measured by the KnowGene questionnaire; empowerment, measured by the Genomics Outcome Scale (GOS-6); Self-efficacy, measured by the Genetic self-efficacy scale; Quality of life measured by the 12-item Short Form Health Survey (SF-12); Satisfaction with genetics education and intended health behaviour change, assessed by team developed questions; Satisfaction with digital tool measured by the Digital Health Application Satisfaction Scale (DHASSP); digital health literacy measured by digital health care literacy scale; health literacy measured by the BRIEF Health Literacy Screening tool.

Turnaround times will be measured by documenting time between blood draw, return of result and result disclosure by the clinician. Time saved will be measured by capturing time spent preparing for the session, during the session with the genetic counselor, and after the session across both arms. Frequency of platform use will also be documented and duration of platform use by recording platform access dates and time.

Quantitative Analysis: Analysis will follow the intention-to-treat approach. Mean scores for test-specific distress will be compared using a t-test. Secondary outcomes of generalized distress, empowerment, and knowledge will also be analyzed using a t-test for hypothesis-generating purposes; therefore no multiplicity corrections will be applied. Mean wait times, total consult times and frequency and duration of use between arms will be analyzed descriptively and compared using a t-test. P-values will be set at 0.05 (two-tailed).

Qualitative Study: Following the trial, qualitative interviews will explore users' experiences with the GA-Mainstream. This will be used to explore participants' experiences, perceptions, and needs related to the use of the GA - Mainstream platform. A purposeful sample of study participants will be used. A mix of intervention arm participants (n=30) will be identified across cancer type, result type, and demographic characteristics to assess patient experience using the GA-Mainstream.

Qualitative Analysis: The qualitative analyses will draw on an interpretive description approach, using open coding and constant comparison to identify common and divergent themes. Interviews will consider participants' socio-demographic factors that may influence their informational and decisional needs, as well as how they engage with genetic information and participate in shared decision making. Two researchers will code transcripts independently; consensus on codes will be reached through discussion. Ongoing analysis will inform the development of progressive iterations of the interview guides.

Mixed-Methods Integration: The quantitative and qualitative data will be integrated to understand whether the tool is effective, including how and why it may influence outcomes such as distress, empowerment, and risk perception. Quantitative and qualitative findings will be narratively described and integrated using a convergent design matrix and joint displays, merged if appropriate.

• Generalized Distress - Hospital Anxiety & Depression Scale (HADS) [Time frame: Everyone: a)Baseline; b) 3-weeks post-baseline; c) 2-weeks post-return of genetic results; d) 6-weeks post-return of results. Intervention arm: e) Post-first digital tool use (after baseline); f) immediately post-return of results via intervention.]
• Knowledge - KnowGene [Time frame: Everyone: a)Baseline; b) 3-weeks post-baseline; c) 2-weeks post-return of genetic results; d) 6-weeks post-return of results. Intervention arm: e) Post-first digital tool use (after baseline); f) immediately post-return of results via intervention.]
• Empowerment - Genomics Outcome Scale (GOS-6) [Time frame: Everyone: a)Baseline; b) 3-weeks post-baseline; c) 2-weeks post-return of genetic results; d) 6-weeks post-return of results. Intervention arm: e) Post-first digital tool use (after baseline); and f) immediately post-return of results via intervention.]
• Self-Efficacy - Genetic Self Efficacy Scale [Time frame: Everyone: a)Baseline; b) 3-weeks post-baseline; c) 2-weeks post-return of genetic results; d) 6-weeks post-return of results. Intervention arm: e) Post-first digital tool use (after baseline); and f) immediately post-return of results via intervention.]
• Quality of Life - Short Form Health Survey (SF-12) [Time frame: Assessed at: a) baseline; b) 2-weeks post-return of genetic test results (primary timepoint); and c) 6-weeks post-return of results.]
• Satisfaction with Genetics Education [Time frame: Everyone: a)Baseline; b) 3-weeks post-baseline; c) 2-weeks post-return of genetic results; d) 6-weeks post-return of results. Intervention arm: e) Post-first digital tool use (after baseline); and f) immediately post-return of results via intervention.]
• Risk Perception - TRIpartite model of RISK perception (TRIRISK) [Time frame: Everyone assessed at: a) Baseline; b) 2-weeks post-return of genetic test results; and c) 6-weeks post-return of results. Intervention arm only: c) immediately post-return of results via the intervention.]
• Intended Health Behavioural Change Questions [Time frame: Assessed at a) baseline, b) 3-weeks after baseline; c) immediately after viewing results via the platform (intervention only), d) 2-weeks post-return of results, and e) 6-weeks post-return of results.]
• Satisfaction with Digital Tool - Digital Health Application Satisfaction Scale (DHASSP) [Time frame: Administered to the intervention arm only: a) immediately post-first digital tool use (intervention), after baseline; b) immediately post-return of results via the intervention; and c) 2 weeks post-return of results.]