# Building and Deploying a Genomic-Medicine Risk Assessment Model for Diverse Primary Care Populations.

> **NIH NIH U01** · DUKE UNIVERSITY · 2022 · $812,705

## Abstract

Family health history (FHH), a critical component of genomic medicine that is essential for both identifying
individuals at risk for hereditary conditions and for contextualizing results of genetic testing, continues to be
broadly underutilized and underappreciated in clinical care. Barriers to adequate data collection and synthesis
are numerous and cross all clinical stakeholders: patients, providers, and health systems. Significantly, they
include the pervasive view that FHH is unimportant except in select cases and that it rarely contributes to
clinical decision making. With this perspective, few providers have been willing to allocate precious time to
collect detailed FHHs or to learn the complex algorithms required to synthesize FHH data into actionable care
plans. However, in studies of systematic FHH-based risk assessments in unselected populations, 25% of
patients meet risk criteria for (actionable) hereditary conditions. FHH-based risk assessment programs have
emerged to address these barriers, but as designed do not meet the needs of low literacy, low resource
populations. The goal of this proposal is to develop a scalable end-to-end solution for risk assessment and
management that meets the needs of low resource settings. Our central hypothesis is that combining FHH-
driven risk assessment, a literacy-enhanced interface using voice-to-text response capture (like ‘Siri’), family
engagement (through social networking platforms for data gather and risk sharing), and a genetic testing
delivery system, will create a solution that engages and increases the proportion of diverse patients who are
identified as at increased risk, who undergo testing, and, when appropriate, who initiate cascade screening
among relatives. In this proposal we will define and deploy this new care delivery model as the “Genomic
medicine Risk Assessment Care for Everyone” (GRACE). To this end we will 1) develop and deploy the
model using pre-implementation assessments at clinical sites with highly diverse patient populations to select
the most appropriate integration options and pathways for both patients and providers; and 2) perform a
randomized implementation-effectiveness pragmatic hybrid trial to assess implementation and effectiveness
outcomes relevant to these diverse populations. Outcomes will include reach, uptake, clinical utility,
accessibility, genetic testing frequency, genetic testing results, and cost-effectiveness. In addition we will
convene an advisory panel of stakeholders from industry (laboratories, insurers), providers, patients, and
health system to understand sustainability and address knowledge gaps that will promote access when the trial
is over.

## Key facts

- **NIH application ID:** 10630415
- **Project number:** 3U01HG010231-05S1
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Lori Ann Orlando
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $812,705
- **Award type:** 3
- **Project period:** 2022-09-16 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10630415

## Citation

> US National Institutes of Health, RePORTER application 10630415, Building and Deploying a Genomic-Medicine Risk Assessment Model for Diverse Primary Care Populations. (3U01HG010231-05S1). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10630415. Licensed CC0.

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