# Customizing Value-based Methods to Prioritize Implementation of Pharmacogenomic Clinical Decision Support for Learning Health Systems

> **NIH AHRQ R21** · UNIVERSITY OF WASHINGTON · 2020 · $156,575

## Abstract

Project Summary/ Abstract
 Pharmacogenomics (PGx) – guiding drug therapy based on individuals' genetic make-up - offers significant
potential to improve drug outcomes, as 97% of the US population carries at least one potentially actionable
variant. Providing PGx test results using clinical decision support (PGx-CDS) alerts embedded in health
records may be a useful way to guide drug therapy. Yet, Learning Health Systems (LHSs) resist adoption
because they are unsure of the value of investing in CDS systems for PGx. Determining the value of providing
population-specific PGx-CDS alerts would enable LHSs to invest in those alerts that have the greatest potential
for improving health care quality and outcomes in their specific populations. The objective of this proposal is to
help LHSs make informed decisions about the implementation of PGx-CDS alerts specific to their populations
that consider trade-offs between the cost of implementation and the potential clinical benefits to patients. Using
decision modeling, in Aim 1 we will create a framework for estimating the value of PGx-CDS alerts. In Aim 2,
we will adapt this framework to an online platform, creating a publically available, web-based tool that will
enable customized estimates of the value of PGx-CDS alerts specific to each LHS. We will pilot and improve
the tool by collaborating with stakeholder-colleagues in LHSs.
 Our work is responsive to the priorities outlined in AHRQ PA-17-246 in that it: 1) provides an innovative,
evidence-based HIT solution to manage population health and improve quality and outcomes within LHSs in a
way that makes the solution configurable across disparate LHSs, and 2) provides a platform to share and
analyze practice data in a way that makes knowledge learned actionable and sharable, including tailoring
messages to decision-makers. Our project is aligned with the priorities of AHRQ in that it will make healthcare
safer and costs transparent, while simultaneously creating efficiencies.

## Key facts

- **NIH application ID:** 10003311
- **Project number:** 5R21HS026544-02
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** EMILY BETH DEVINE
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2020
- **Award amount:** $156,575
- **Award type:** 5
- **Project period:** 2019-09-01 → 2021-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10003311

## Citation

> US National Institutes of Health, RePORTER application 10003311, Customizing Value-based Methods to Prioritize Implementation of Pharmacogenomic Clinical Decision Support for Learning Health Systems (5R21HS026544-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10003311. Licensed CC0.

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