# Leveraging an electronic medical record infrastructure to identify primary care patients eligible for genetic testing for hereditary cancer and evaluate novel cancer genetics service delivery models

> **NIH NIH U01** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2020 · $160,971

## Abstract

SUPPLEMENT ABSTRACT
This application is being submitted in response to the Notice of Special Interest (NOSI) identified as NOT-CA-
20-042. The application is a proposed administrative supplement to the University of Utah (Utah)/New York
University (NYU) U01 entitled “Leveraging an electronic medical record infrastructure to identify primary care
patients eligible for genetic testing for hereditary cancer and evaluate novel cancer genetics service delivery
models” (U01 CA232826). The parent U01 is employing a replicable electronic health record (EHR)-based
clinical decision support infrastructure to: (i) identify unaffected primary care patients who qualify for cancer
genetics services based on current guidelines in the Utah and NYU healthcare systems (Aim 1); and (ii)
compare two models of cancer genetics services delivery for 1,920 of these unaffected primary care patients in
a randomized controlled trial (Aims 2 and 3). The parent trial will examine how race and ethnicity modify the
effects of the cancer genetics services delivery models. The landscape for delivering genetics services has
changed substantially due to the COVID-19 pandemic, and our pilot data suggest that patients’ uptake of
cancer genetic testing and access to cancer screening has been adversely affected. This supplement would
provide us with an unparalleled opportunity to investigate COVID-19 impacts in two study sites with very
different pandemic contexts. We propose the following Supplemental Aims: (1) Characterize healthcare
experiences related to COVID-19 among the cohort of 22,208 primary care patients identified as being at
increased risk for hereditary cancer; and (2) Investigate how COVID-19 impacts primary care patients’
decisions about and utilization of cancer genetics services. To address Supplemental Aim 1, we will abstract
EHR data to investigate COVID-19 diagnosis, SARS-CoV-2 testing, and delays in cancer screening in the
identified cohort. Among the subset of the cohort invited to participate in the trial, we hypothesize that having
been diagnosed with or hospitalized for COVID-19 or having had a cancelled cancer screening will negatively
affect trial participation. We will also investigate differences in these COVID-19 experiences by study site (Utah
vs. NYU) and race/ethnicity. To address Supplemental Aim 2, among participants in the parent trial, we will
examine how the health, psychological, and financial impacts of COVID-19 affect decisions about and
utilization of cancer genetic counseling and genetic testing using a combination of clinic records and
questionnaire data. Based on pilot data, we hypothesize that those having higher self-reported health,
psychological, and financial impacts of COVID-19 will be less likely to complete cancer genetic testing. We will
examine how the effects of COVID-19 are modified by study site (Utah vs. NYU) and race/ethnicity. Together,
the supplemental aims will allow us to build a comprehensive picture of how COVID-19 has...

## Key facts

- **NIH application ID:** 10200396
- **Project number:** 3U01CA232826-03S2
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** Ophira Ginsburg
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $160,971
- **Award type:** 3
- **Project period:** 2018-09-18 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10200396

## Citation

> US National Institutes of Health, RePORTER application 10200396, Leveraging an electronic medical record infrastructure to identify primary care patients eligible for genetic testing for hereditary cancer and evaluate novel cancer genetics service delivery models (3U01CA232826-03S2). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10200396. Licensed CC0.

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