# GARDE: Scalable Clinical Decision Support for Individualized Cancer Risk Management

> **NIH NIH U24** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2024 · $739,597

## Abstract

PROJECT SUMMARY/ABSTRACT
 Evidence supports individualizing risk-stratified cancer screening, with selective application of specific
screening interventions best suited to the individual. Yet, individualizing cancer screening at a population
scale requires the implementation of personalized risk assessments which are quite challenging to achieve in
today’s overwhelmed primary care settings. A promising approach to address this challenge is to automate the
identification and management of eligible patients using electronic health record (EHR) technologies coupled
with advanced clinical decision support (CDS) tools and automated conversational agents (“chatbots”). In
previous research funded by the National Cancer Institute (NCI) Informatics Technology for Cancer Research
(ITCR) program, we have enabled GARDE (Genetic Cancer Risk Detector), a standards-based CDS platform
for individualized cancer screening. GARDE (i) screens and identifies patients who meet National
Comprehensive Cancer Network (NCCN) criteria for genetic testing based on their family history and other
risk factors in the EHR; and (ii) uses automated chatbots offering patient outreach and education, offering
access to genetic testing and explanation of test results. GARDE has been integrated with two market-leading
EHR systems (Epic® and Cerner®) using the Fast Healthcare Interoperability Resources (FHIR) and CDS Hooks
standards. GARDE has been successfully deployed in clinical settings at two academic medical centers and
their respective cancer centers (University of Utah/Huntsman Cancer Institute and New York University) in
support of the BRIDGE trial, funded by the NCI Cancer Moonshot program (U01CA232826 – Kaphingst, PI).
The overall objective of the present proposal is to enhance and disseminate GARDE across healthcare systems
including high resource medical centers and low resource safety net healthcare systems. Our approach will be
guided by implementation science frameworks that help assess implementation readiness, identify barriers
and facilitators, identify needs for adaptation, and develop implementation strategies. Specifically, we will (i)
enhance GARDE’s chatbots using open-source technologies; (ii) deploy GARDE at new collaborating sites
(Cornell University, Medical University of South Carolina [MUSC], Beaufort Memorial Hospital); (iii) conduct
rapid iterative pilot implementations at these new sites; (iv) iteratively develop and test an implementation
toolkit based on experience with the pilot sites; (v) conduct a cost analysis to catalyze further adoption; and
(vi) disseminate GARDE beyond the collaborating sites through the implementation toolkit and direct
technical assistance. Through wide dissemination, GARDE has the potential to enable evidence-based
individualized cancer screening and reduce cancer burden through a scalable, population-based, and
interoperable approach.

## Key facts

- **NIH application ID:** 10917398
- **Project number:** 5U24CA274582-02
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** GUILHERME DEL FIOL
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $739,597
- **Award type:** 5
- **Project period:** 2023-09-01 → 2028-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10917398

## Citation

> US National Institutes of Health, RePORTER application 10917398, GARDE: Scalable Clinical Decision Support for Individualized Cancer Risk Management (5U24CA274582-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10917398. Licensed CC0.

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