Family history and cancer risk study (FOREST): A clinical trial assessing electronic patient-directed family history input for identifying patients at risk of hereditary cancer

Abstract

Background: Hereditary cancer syndromes cause a high lifetime risk of early, aggressive cancers. Early recognition of individuals at risk can allow risk-reducing interventions that improve morbidity and mortality. Family health history applications that gather data directly from patients could alleviate barriers to risk assessment in the clinical appointment, such as lack of provider knowledge of genetics guidelines and limited time in the clinical appointment. New approaches allow linking these applications to patient health portals and their electronic health records (EHRs), offering an end-to-end solution for patient-input family history information and risk result clinical decision support for their provider.

Methods: We describe the design of the first large-scale evaluation of an EHR-integrable, patient-facing family history software platform based on the Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources (SMART on FHIR) standard. In our study, we leverage an established implementation science framework to evaluate the success of our model to facilitate scalable, systematic risk assessment for hereditary cancers in diverse clinical environments in a large pragmatic study at two sites. We will also evaluate the success of the approach to improve the efficiency of downstream genetic counseling resulting from pre-counseling pedigree generation.

Conclusions: Our research study will provide evidence regarding a new care delivery model that is scalable and sustainable for a variety of medical centers and clinics.

Trial registration: This study was registered on ClinicalTrials.gov under NCT05079334 on 15 October 2021.

Keywords: Digital health; Genetics; Hereditary cancer; Risk assessment; SMART on FHIR; Service delivery models.

Figure 1.. Graphic depicting an overview of the FOREST study process.

This figure compares and contrasts the traditional model of genetic services delivery (A) with that used in the FOREST study (B). In the traditional model (A) patients first see a clinician, who is responsible for assessing their risk based on guidelines and deciding to refer to genetic counseling. The genetic counselor reaffirms the risk assessment and provides counseling and education prior to the patient’s choice to test or not to test. Patients who choose to test have their results delivered by a genetic counselor. In the FOREST model (B), MeTree is responsible for collecting the data and assessing risk; MeTree reports are uploaded to the EHR and released to the patient while the providers can also look at the reports. Patients who have a personal and family history indicative of hereditary cancer syndromes on MeTree are directed via automated processes to genetic counseling services, where the counselor can discuss testing options with the patient. Icons courtesy of KFM’s personal portfolio.

Figure 2.. Workflow and RE-AIM outcomes measured in the FOREST study.

The study workflow is presented in gray boxes on the left and begins with electronic (VUMC) or in-person (MMC) invitations and an electronic eligibility survey. Eligible individuals proceed to consent and a baseline survey. Individuals who complete the baseline receive a MeTree survey link. We measure the proportion of individuals who receive a link to MeTree who initiate, as well as the proportion who complete the MeTree family health history survey (RE-AIM outcome of “Reach”). Individuals who complete MeTree are designated as not at risk or at risk for hereditary cancer syndromes and receive a report and any recommendations. We measure the incidence of at-risk designation in our cohort pre- and post-MeTree, using EHR data as a proxy indicator for at-risk status in the pre-MeTree setting (RE-AIM outcome of “Effectiveness”). Individuals not at risk receive a post-MeTree survey and do not further progress whereas individuals who are at risk receive recommendations to attend a genetic counseling (GC) visit. We measure GC uptake as proportion of those eligible (“Effectiveness”). Individuals who attend a GC visit are provided a post-GC survey. We also assess impact on genetic counselors in clinic (not shown).