# Clinical Decision Support for Unsolicited Genomic Results

> **NIH NIH R35** · JOHNS HOPKINS UNIVERSITY · 2020 · $481,371

## 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-scie...

## Key facts

- **NIH application ID:** 10048472
- **Project number:** 1R35HG010714-01A1
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** CASEY OVERBY TAYLOR
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $481,371
- **Award type:** 1
- **Project period:** 2020-09-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10048472

## Citation

> US National Institutes of Health, RePORTER application 10048472, Clinical Decision Support for Unsolicited Genomic Results (1R35HG010714-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10048472. Licensed CC0.

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