# Project OASIS: Optimizing Approaches to Select Implementation Strategies

> **NIH VA I01** · VETERANS HEALTH ADMINISTRATION · 2024 · —

## Abstract

BACKGROUND: Implementation science aims to improve the uptake of evidence-based health care practices
(EBPs) by defining the barriers that prevent their use, offering implementation strategies to overcome these
barriers, and developing methods that allow clinicians and researchers to choose the strategies that best ad-
dress the barriers they encounter. Recognizing that implementation strategy selection is often inefficient and
idiosyncratic, implementation experts have called for methods to make strategy selection scientific, data-
driven, and “precise”. To actualize “precision implementation”, there is a critical need to develop methods to 1)
quickly and uniformly identify implementation barriers and facilitators, 2) track the use and effectiveness of im-
plementation strategies, and 3) incorporate data and expert knowledge into the process of matching strategies
to barriers. Without these improvements, we risk perpetuating implementation failures and health care dispari-
ties. SIGNIFICANCE: Project OASIS (Optimize Approaches to Select Implementation Strategies) will support
VA in becoming a “high reliability organization,” that provides Veterans with access to equitable and high-qual-
ity care. The resulting methodological advancements are expected to apply outside of VA and across a wide
variety of evidence-to-practice gaps. INNOVATION & IMPACT: Project OASIS will engage tremendous interdis-
ciplinary expertise and data resources to address the complex, fundamental question of how to efficiently
measure and select strategies to promote equitable, high-quality care. This work will leverage a one-of-a-kind
dataset (38 project-years of implementation data) and techniques that are innovative in implementation science
(e.g., user-centered design, machine learning). Project OASIS will advance the science of precision implemen-
tation and our understanding of how to make decision aids that incorporate both machine learning and expert
opinion. This work will provide innovative and practical approaches that will support broader public health and
health equity goals. SPECIFIC AIMS: 1) Develop and test an implementation strategy selection Decision Aid
(DA); 2) Develop and validate a survey to rapidly assess implementation barriers and facilitators; and 3) Refine
a survey to track implementation strategy use over time. METHODOLOGY: Aim 1) The DA will be based on
our existing, 38 project-years of data, that span 8 years, 12 EBPs, and 145 VA sites. We will first use Latent
Class Analysis to identify site “types” using existing data about site barriers and facilitators coded using the
Consolidated Framework for Implementation Research (CFIR)—a meta-framework of 39 constructs that can
help or hinder implementation. For each site type, we will use tree-based methods to identify which implemen-
tation strategies (based on the Evidence-Based Recommendations for Implementation Research, ERIC, taxon-
omy of 73 strategies) were associated with best performance on ...

## Key facts

- **NIH application ID:** 10748079
- **Project number:** 1I01HX003610-01A2
- **Recipient organization:** VETERANS HEALTH ADMINISTRATION
- **Principal Investigator:** MATTHEW CHINMAN
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2024
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2024-01-01 → 2027-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10748079

## Citation

> US National Institutes of Health, RePORTER application 10748079, Project OASIS: Optimizing Approaches to Select Implementation Strategies (1I01HX003610-01A2). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10748079. Licensed CC0.

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