# Clinical Decision Support for Unsolicited Genomic Results

> **NIH NIH R35** · JOHNS HOPKINS UNIVERSITY · 2021 · $73,922

## Abstract

PROJECT SUMMARY
As healthy individuals increasingly can receive genomic testing results that indicate their risk for poor outcomes
(e.g. diseases or adverse drug reactions), healthcare providers will need to ensure that the results are handled
prudently, by addressing the receipt of the results, the workflow challenges, and liability issues. Given that clinical
genomic tests can be initiated outside of the clinical setting (e.g., in a research study), from the clinician’s
perspective, they can be characterized as unsolicited genomic results (UGR). Clinical decision support (CDS)
has great potential to ease the adoption of UGR by providing clinicians with recommendations and patient-related
information presented at particular times to enhance clinical care. Deploying CDS for UGR in a healthcare setting
in a scalable way, however, will depend on our capacity to leverage local institutional policy and oversight
structures to approve of CDS guidance and strategies for UGR. The specific objective of this research program
is to develop and evaluate the Evidence-based Decision support Implementation over Time (EDIT) model
for prioritizing and revising deployed CDS for UGR. The EDIT model will empower local oversight committees
such as Pharmacy & Therapeutics committees to have a role in the CDS review and deployment processes
within existing institutional social systems using accepted organizational processes. The direct benefits of this
work will be an EDIT dashboard that can be used by oversight committees to prioritize new and to revise
deployed CDS, and infrastructure to close the loop of the learning health system by transferring CDS revisions
approved by oversight committee members into deployed CDS for UGR. EDIT model implementation will be
informed by mixed methods research strategies: Strategy 1, we will conduct focus groups with oversight
committee members in order to understand current roles, tasks and goals of the committee, as well as to capture
opinions about the best processes to prioritize, review and approve of new and revised CDS for UGR as part of
committee meeting activities. Research Strategy 2, we will conduct a survey study with patients to assess
preferences for the return of UGR with CDS and usability studies with oversight committee members to gather
feedback on the EDIT dashboard design. Strategy 3, we will conduct time-motion observations of local oversight
committee meetings prior to and after deploying the EDIT model in order to plan a future, multi-institution, time-
motion study with statistical power to detect differences between oversight committees that use the EDIT
dashboard and those that do not. The hypothesis is that time spent prioritizing new and revised CDS will be
shorter with use of the EDIT dashboard. Overall, the EDIT model establishes processes that lower barriers to
implementing robust genomic medicine programs that can be followed by others. The Genomic Innovator Award
will enable me to study, in team-sci...

## Key facts

- **NIH application ID:** 10318291
- **Project number:** 3R35HG010714-02S1
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** CASEY OVERBY TAYLOR
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $73,922
- **Award type:** 3
- **Project period:** 2020-09-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10318291

## Citation

> US National Institutes of Health, RePORTER application 10318291, Clinical Decision Support for Unsolicited Genomic Results (3R35HG010714-02S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10318291. Licensed CC0.

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