# CV Wizard: Does a Prioritized, Point-of-Care Clinical Decision Support Tool Improve Guideline-Based CVD Risk Factor Control in Safety Net Clinics?

> **NIH NIH R01** · KAISER FOUNDATION RESEARCH INSTITUTE · 2020 · $645,631

## Abstract

PROJECT SUMMARY / ABSTRACT: Substantial progress in reducing cardiovascular disease (CVD)
morbidity and mortality would be achieved if evidence-based guidelines for CVD risk factor control were
implemented consistently in primary care settings. Electronic health record (EHR)-based clinical decision
support (CDS) systems that identify uncontrolled CVD risk factors and provide individualized care
recommendations improved rates of guideline-concordant CVD care in large, integrated healthcare settings,
but little is known about how effective such CDS may be in safety net community health centers (CHCs).
CHCs' socioeconomically vulnerable patients have far worse CVD risk factor control and higher rates of
major CVD events than the general population. Implementing CDS that leads to improved CVD risk factor
control in CHCs could reduce national disparities in CVD outcomes, but CHCs rarely have the resources to
develop sophisticated CDS, and very few currently have such systems for CVD care. The proposed study is
designed to address this. We will randomize 60 CHCs with a shared EHR to immediate vs. delayed
implementation of a sophisticated CDS system that provides point-of-care CVD care recommendations to
the primary care provider and the patient, and has been proven highly successful in large, integrated care
settings. Before implementing the CDS, we will ask CHC patients and providers about the particular patient
needs and perspectives and clinic workflows likely to influence adoption and impact of the CDS in CHCs.
This input will inform development of CHC care team training strategies, and adaptation of the patient-facing
aspects of the CDS system. We will measure adoption of the CDS, and impact of its use over time on CVD
risk scores and risk factor control (blood pressure, HbA1c, lipid levels; aspirin use; smoking; body mass
index) in high-CVD risk CHC patients. We will also conduct a mixed methods process evaluation, to identify
facilitators and barriers to use of the CDS, and to iteratively develop and test strategies for supporting its
adoption and ongoing use in CHC workflows. We anticipate that this intervention could (a) improve CVD care
among low-income CHC patients, (b) reduce CVD care disparities between CHC populations and national
rates, and (c) facilitate greater CHC patient engagement in CVD treatment decision-making and
prioritization. The proposed work directly responds to PAR 15-279 goals: it addresses gaps in guideline-
based care in high-risk populations with targeted, innovative, multi-level strategies; considers setting-specific
needs; and supports patient engagement. Our team's research experience and established partnerships with
key healthcare system stakeholders increase the likelihood of project success. Results will yield EHR-
agnostic CDS tools for use by any CHC with an implementation guide, build knowledge about how to
minimize disparities in CVD care and outcomes using scalable CDS strategies, and help translate
investm...

## Key facts

- **NIH application ID:** 9879758
- **Project number:** 5R01HL133793-04
- **Recipient organization:** KAISER FOUNDATION RESEARCH INSTITUTE
- **Principal Investigator:** RACHEL GOLD
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $645,631
- **Award type:** 5
- **Project period:** 2017-03-01 → 2022-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9879758

## Citation

> US National Institutes of Health, RePORTER application 9879758, CV Wizard: Does a Prioritized, Point-of-Care Clinical Decision Support Tool Improve Guideline-Based CVD Risk Factor Control in Safety Net Clinics? (5R01HL133793-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9879758. Licensed CC0.

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