# Bridging the evidence-to-practice gap: Evaluating practice facilitation as a strategy to accelerate translation of a systems-level adherence intervention into safety net practices

> **NIH NIH R01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2020 · $718,623

## Abstract

Project Summary: Despite increasing awareness and treatment of hypertension (HTN) across all racial/ethnic
groups, Latinos have the lowest blood pressure (BP) control rates in the US (Latino adults: 34% vs. 43% and
53% in non-Hispanic black and white adults). These statistics may be explained by the disproportionately
poorer adherence to antihypertensive medications among Latinos compared to blacks and whites. Systems-
level interventions conducted in primary care settings have improved medication adherence in minority
populations. Our Ayudando a Latinos Hipertensos Para Mejorar Adherencia a sus Medicamentos (ALMA) trial,
which informs this proposal significantly improved both BP control (51 vs. 29%, p=.04) and medication
adherence (78 vs. 72%, p=.02) compared to enhanced usual care in a sample of 119 Latino patients followed
in a safety-net practice. Despite their efficacy, evidence-based interventions like ALMA often take up to 17
years to be translated into clinical practice. Implementation strategies are sorely needed to accelerate the
translation of evidence-based interventions into routine “real world” safety-net practices, in order to reduce
disparities in BP control in vulnerable populations. Practice facilitation (PF) is one method to accelerate the
implementation of evidence-based interventions into healthcare settings. Through PF, a facilitator works with
healthcare teams to develop the skills to adapt and implement evidence-based system changes and promotes
a tailored approach to integrating those changes into the clinic workflow. Although evidence supports the effect
of PF for preventive screenings (e.g., breast examination), its impact on implementing evidence-based
systems approaches to support HTN management in safety-net practices remains largely untested. This
proposal provides an opportunity to fill this evidence-to-practice gap by evaluating the effectiveness of PF as a
practical and replicable strategy for implementing ALMA in a network of 12 safety-net Family Health Centers
(FHCs) in New York City. Using a mixed-methods design, we will conduct this study in two phases: (1) A pre-
implementation phase where we will refine the PF strategy, informed by our prior work, based on the
Consolidated Framework for Implementation Research to facilitate the implementation of ALMA at the FHCs.
(2) An implementation phase, during which we will evaluate, in a pragmatic cluster-randomized controlled trial,
the effect of the PF strategy compared to a self-directed condition (i.e., receipt of information for implementing
ALMA but no facilitation) on implementation fidelity (primary outcome) of ALMA and on clinical outcomes
(secondary outcome) at 12 months among a sample of 650 Latinos with uncontrolled HTN cared for at the
FHCs. Implementation fidelity will be assessed using a mixed methods approach based on the five core
dimensions of implementation fidelity, as defined by Proctor’s Implementation Outcomes Framework. Clinical
outcome measure...

## Key facts

- **NIH application ID:** 9912196
- **Project number:** 5R01MD013769-02
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** OLUGBENGA G. OGEDEGBE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $718,623
- **Award type:** 5
- **Project period:** 2019-04-09 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9912196

## Citation

> US National Institutes of Health, RePORTER application 9912196, Bridging the evidence-to-practice gap: Evaluating practice facilitation as a strategy to accelerate translation of a systems-level adherence intervention into safety net practices (5R01MD013769-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9912196. Licensed CC0.

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