# Developing and Evaluating a Machine-Learning Opioid Prediction & Risk-Stratification E-Platform (DEMONSTRATE)

> **NIH NIH R01** · UNIVERSITY OF FLORIDA · 2023 · $634,810

## Abstract

Project Summary/Abstract
An unprecedented rise in opioid overdose and opioid use disorder (OUD) has become a public health crisis in
the US. In response, health systems, payers, and policy makers have developed or adopted measures and
programs to target individuals at high-risk for overdose or OUD. However, significant gaps exist in the current
approaches to identify individuals at high-risk for overdose or OUD. First, the definition of ‘high-risk’ currently
used by payers and health systems varies widely (ranging from high opioid dose to the number of pharmacies
or prescribers a patient has visited). Second, little is known about how accurately these measures truly identify
patients with overdose or OUD, and there is some evidence showing they perform poorly, missing 70% to 90%
of individuals with an actual OUD diagnosis or overdose. Third, our NIDA-funded work (R01DA044985) using
national Medicare and Pennsylvania Medicaid claims data has shown that machine-learning algorithms can
achieve better performance for risk prediction for opioid overdose and OUD. Thus, the immediate next step is to
expand our algorithms to other data sources (e.g., electronic health records [EHR]), as well as to apply state-of-
the-art longitudinal neural networks and natural language processing (NLP) to further improve prediction
accuracy. In addition, we aim to translate these risk scores into a clinical decision tool to be used by health care
systems to automatically analyze and visualize the relevant information regarding risk prediction and stratification
for opioid overdose or OUD, using either claims data, EHR data, or both in real time.
Leveraging our NIDA-funded work on developing machine-learning algorithms to predict opioid overdose and
OUD, we propose to “develop and evaluate a machine-learning opioid prediction & risk-stratification e-
platform (DEMONSTRATE)” that can be used by health care systems to identify patients at high risk for
opioid overdose and OUD. We have 3 specific aims. Aim 1 will refine and validate prediction algorithms to
identify patients at risk for opioid overdose/OUD using 3 different datasets (i.e., 2011-2020 Florida all-payer EHR,
Florida Medicaid claims, and Florida Medicaid claims linked with EHR data) from the OneFlorida Clinical
Research Consortium. We will expand our current algorithms by applying state-of-the-art methods (e.g., NLP) to
improve prediction. In Aim 2, we will design and prototype a DEMONSTRATE clinical decision support tool to
incorporate the best prediction algorithms to provide automatic warnings to primary care providers of patients at
high risk of overdose/OUD. An iterative user-centered design approach will be used to enhance
DEMONSTRATE’s functionality and usability. In Aim 3, we will integrate DEMONSTRATE into the University of
Florida Health’s EHR system, and deploy and pilot test DEMONSTRATE in three primary care clinics. We will
assess DEMONSTRATE’s usability, acceptability, and feasibility. Our propose...

## Key facts

- **NIH application ID:** 10597698
- **Project number:** 5R01DA050676-03
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Wei-Hsuan Jenny Lo-Ciganic
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $634,810
- **Award type:** 5
- **Project period:** 2021-07-01 → 2023-11-01

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10597698

## Citation

> US National Institutes of Health, RePORTER application 10597698, Developing and Evaluating a Machine-Learning Opioid Prediction & Risk-Stratification E-Platform (DEMONSTRATE) (5R01DA050676-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10597698. Licensed CC0.

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