# Data to Clinical Action: Using Predictive Analytics to Improve Care of Veterans with Opioid Use Disorder

> **NIH VA IK2** · CENTRAL ARKANSAS VETERANS HLTHCARE SYS · 2022 · —

## Abstract

Background. Medication for opioid use disorder (MOUD) prevents overdoses and improves mortality in
Veterans with OUD, but retention on MOUD is critical for achieving those clinical endpoints. Only 50% of
Veterans are retained on MOUD at 6-months post-MOUD initiation. Poor engagement in additional needed
care services is an important risk factor for early MOUD discontinuation. Consequently, providers’ ability to
identify Veterans in need of additional care or support while on MOUD may increase the likelihood of Veterans’
continued use of MOUD. Valid predictive models can provide an accurate probability of an individual Veteran
experiencing the outcome being modeled (e.g., MOUD discontinuation). Prediction of future MOUD
discontinuation risk could provide an innovative and real-time method for identifying Veterans in need of
additional care (e.g., peer support). Significance/Impact. Predictive models could be used to lower MOUD
attrition risk and improve outcomes for this Veteran population by continuously monitoring their risk of MOUD
discontinuation in real-time during active MOUD treatment and identifying those Veterans in need of additional
care (e.g., if increasing risk between visits, providers might add peer support services to a treatment plan).
Innovation. This CDA-2 encompasses three HSR&D research priority areas (opioid/pain, health care
informatics, and access to care) while crosscutting HSR methods of “big” data and implementation science, all
in an effort to improve care and outcomes for Veterans with OUD. This study will also be the first to develop
and pilot test a clinical decision support tool (CDST), based on a predictive model, to improve Veterans’ MOUD
retention. Specific Aims. (1) To develop and validate PREMMOUD, a PREdictive Model for MOUD
discontinuation. Hypotheses: (H1) I will develop a predictive model with good discrimination (e.g., c-statistic, a
measure of goodness-of-fit, ≥0.8) for identifying Veterans likely to discontinue MOUD within the initial 6 months
of treatment; (H2) the model generated using neural network techniques will have better discrimination than the
models generated using random forest and logistic regression techniques. (2) To adapt PREMMOUD into a
CDST to continuously monitor risk of MOUD discontinuation and provide clinical guidelines for addressing the
primary risk factors driving the PREMMOUD score. (3) To assess (a) the feasibility of conducting a large scale,
randomized controlled trial (RCT) to test PREMMOUD CDST’s (P-CDST) effectiveness as well as (b) P-
CDST’s acceptability among waivered providers. Hypotheses: (H3) The feasibility of conducting a large-scale
RCT to evaluate P-CDST’s effectiveness will be supported; (H4) P-CDST will be acceptable among VHA
waivered providers. Methodology. Using machine-learning methods and data from the VHA Corporate Data
Warehouse (2006-2019), I will train and validate PREMMOUD in a national sample of Veterans initiating
MOUD (Aim 1). For Aim 2, I will con...

## Key facts

- **NIH application ID:** 10317224
- **Project number:** 1IK2HX003358-01A1
- **Recipient organization:** CENTRAL ARKANSAS VETERANS HLTHCARE SYS
- **Principal Investigator:** Corey J Hayes
- **Activity code:** IK2 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2022
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2022-04-01 → 2027-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10317224

## Citation

> US National Institutes of Health, RePORTER application 10317224, Data to Clinical Action: Using Predictive Analytics to Improve Care of Veterans with Opioid Use Disorder (1IK2HX003358-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10317224. Licensed CC0.

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