Family History and Cancer Risk Study — FOREST  

Cancer-related Problem/Condition Clinical Trial

Official title: Improving Identification and Healthcare for Patients With Inherited Cancer Syndromes: Evidence-based EMR Implementation Using a Web-based Computer Platform

Status Active, not recruiting

Clinical Trial Summary

Investigators from Vanderbilt University Medical Center (VUMC), Duke University, and Meharry Medical College (MMC) are collaborating on a family health history study to deploy a family health history (FHH) platform, MeTree. Recruited participants will complete surveys, the MeTree questionnaire, and MeTree will determine the participant's cancer risk based on current guidelines. The study team will offer genetic counseling to high-risk participants. Investigators will track participant outcomes and behaviors from the use of MeTree to determine the efficiency of the use of MeTree compared to completion of pedigrees in clinic.

Clinical Trial Description

From the earliest recognition of cancer-prone families over 100 years ago, clinicians have depended on the family health history (FHH) to identify and treat patients and family members who may have a cancer family syndrome. Once identified, patients can be offered lifesaving, evidence-based management strategies for many of these conditions. To fully realize the benefits of genetic healthcare, however, at risk individuals must be recognized, offered genetic counseling and testing, and then referred for specialized therapy or enhanced screening. While this is a promising time for these patients and families, there are significant challenges to implement a modern and efficient care delivery model across the many patients, provider, and health system stakeholders. The practice of genetic medicine is changing as genetic discoveries are translated into new tests for an increasing number of health indications. In fact, the prevalence of high-risk individuals who are at risk for hereditary cancer has been rising due to new testing strategies. This has placed enormous pressure on the hospitals and clinics providing care for high-risk patients. Coupled with the healthcare systems need for greater efficiency, the traditional genetic clinic-based practice has become untenable. Barriers and challenges include low numbers of trained genetic workforce, lack of integrated FHH tools for patients and physicians, and long wait times for available clinic appointments. Further, while cancer syndromes are seen across all groups, gaps in care exist as underserved populations are often not recognized and referred for care. Investigators propose that these barriers can be overcome by using innovations in informatics and telecommunications to develop a sustainable and scalable genomic care delivery model that can be replicated by other health systems. Such a program would need to integrate FHH applications that collect and analyze family data, SMART-on-FHIR capabilities that can link third party apps with the electronic medical record (EMR), and clinical decision support modules to assist providers and patients. MeTree is one such system with all these capabilities -- a validated and flexible patient-facing FHH collection tool that supports SMART-on-FHIR technology. This platform was the backbone of the Implementing Genomics in Practice (IGNITE) network's FHH clinical utility study that showed clear improvements in the quality and quantity of FHH collected in 5 geographically diverse primary care practices. Furthermore, MeTree was highly acceptable to patients and providers, and was able to properly identify participants at risk for 23 hereditary cancer syndromes for genetic counseling referral. Our re-submission for this Beau Biden Moonshot grant opportunity is based on the hypothesis that an implementation science approach will improve the identification and management of high-risk patients from diverse clinical setting by systematically integrating FHH driven evidence-based guidelines into the EMR. Investigators plan to improve ascertainment of high-risk patients by imbedding MeTree in the workflow of primary and cancer care clinics. This will improve identification for genetic counseling, facilitate patient education about genetic testing, and risk management for at risk patients, as well as facilitate engagement of patients, family members, and providers with telegenetic and telephone counseling options. This collaborative effort from genetic, genomic, biomedical informatic, and implementation science researchers at Vanderbilt University Medical Center (VUMC), Meharry Medical Center (MMC) and Duke University is highly responsive to the five required elements in RFA-CA-19-017. The proposal has the following specific aims: SA1. Deploy a care delivery model that will facilitate systematic risk assessment for hereditary cancers in diverse clinical environments. - 4000 participants will be enrolled and randomized to usual care or MeTree FHH risk assessment - Deploy in academic medical center (VUMC) and a medical center (MMC) that serves underserved populations - Assess participants perceptions using online survey and qualitative semi-structured interviews SA2. Improve access to genetic healthcare providers for participants at risk for hereditary cancer syndromes. - 300 high risk participants in the VUMC Hereditary Cancer Clinic will be enrolled and randomized - Extend clinic capacity by lessening the need for in-clinic family history collection and basic counseling - Expand reach of clinic by using telephone and video genetic counseling, referral to specialists SA3. Explore the feasibility of our care delivery model to improve family engagement for cancer risk assessment - Participants extend invitations to MeTree's family resource center to share results of genetic tests - Assist with education and referral needed for cascade testing for pathogenic variants ;

Related Conditions & MeSH terms

• Cancer-related Problem/Condition
• Family Characteristics

• NCT number: NCT05079334
• Study type: Interventional
• Source: Vanderbilt University Medical Center
• Status: Active, not recruiting
• Phase: N/A
• Start date: January 1, 2022
• Completion date: September 2024