# Using Behavioral Economics and Implementation Science to Advance the Use of Genomic Medicine Utilizing an EHR Infrastructure across a Diverse Health System

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2022 · $924,042

## Abstract

The number of medical conditions for which the results of genetic testing change the medical management of
patients is exponentially increasing. However, a minority of eligible patients receive genetic testing, despite the
implications for downstream care. System- (methods to identify eligible patients and return results), clinician(
e.g., knowledge, limited workforce), and patient- (e.g. , concerns about costs and adverse effects) level
barriers foster uncertainty and a tendency to rely on the status quo - failing to use genomic information to guide
medical care. Implementation science methods and frameworks are ideal for addressing this practice gap,
especially those that consider multi-level barriers and the role of human decision-making in contexts with
uncertainty. Our team has built the infrastructure to address system-barriers to delivering genetic testing
across our health system - an integrated system within the electronic health record (EHR) that enables direct
ordering and resulting of genetic tests as structured data - now with multiple requests for dissemination. Our
team also is using behavioral economics as an implementation science framework to improve healthcare by
using nudges (EHR defaults, patient priming) to overcome clinician and patient barriers, concurrently
addressing health disparities (e.g., higher practice gaps among racial minorities). Merging these areas, we
propose a highly innovative project that will evaluate, for the first time, the use of nudges to clinicians (EHR
defaults for either: 1) referring to genetics clinic or 2) ordering for genetic testing) and/or nudges to patients
(communication to prime patients about the benefits of genetic testing prior to appointment). In Aim 1, we will
develop electronic phenotyping algorithms for 10 clinical conditions, which will drive diagnosis-specific genetics
referral and testing; we will refine our nudges working with a Stakeholder Advisory Council. In Aim 2, we will
conduct a hybrid type 3 implementation study, using a cluster randomized design with 228 clinicians
(physician, Advanced Practice Practitioners) as the unit of randomization (N= 120 clusters) and 16,500 patients
with one of the 10 conditions to examine the impact on the rate of genetic testing of: the patient priming nudge,
the two clinician nudges, combining the patient and each of the clinician nudges, vs. a generic best practice
alert (BPA) (no clinician or patient nudge). We will examine patient (e.g., race), clinician (e.g., specialty), and
system (e.g. , community vs. academic center) moderators of nudge effects on genetic testing rate and assess
an effectiveness outcome (rate of clinician action following identification of a pathogenic variant). In Aim 3, we
will engage in systematic methods to disseminate our EHR integration of genetic testing, EHR-based
algorithms, and other materials and systems built for the clinical trial through Epic, PheKB, NHGRl's AnVIL,
and GitHub. Our study will be immensely...

## Key facts

- **NIH application ID:** 10518787
- **Project number:** 1R01HG012670-01
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Katherine L. Nathanson
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $924,042
- **Award type:** 1
- **Project period:** 2022-09-09 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10518787

## Citation

> US National Institutes of Health, RePORTER application 10518787, Using Behavioral Economics and Implementation Science to Advance the Use of Genomic Medicine Utilizing an EHR Infrastructure across a Diverse Health System (1R01HG012670-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10518787. Licensed CC0.

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