Developing and Evaluating a Machine-Learning Opioid Overdose Prediction & Risk-Stratification Tool in Primary Care

Summary

This clinical trial aims to evaluate the pilot implementation of a machine-learning (ML)-driven clinical decision support (CDS) tool designed to predict opioid overdose risk within the electronic health record (EHR) system at UF Health Internal Medicine and Family Medicine clinics in Gainesville, Florida. The study will use a pre- versus post-implementation design to compare outcomes within clinics, focusing on measures such as naloxone prescribing rates and opioid overdose occurrences. Researchers will also assess the usability, acceptability, and feasibility of the CDS tool through qualitative interviews with primary care clinicians (PCPs) in the participating clinics.

Conditions

• Opiate Overdose
• Opioid-Related Disorders
• Narcotic-Related Disorders
• Substance-related Disorders
• Chemically-Induced Disorders
• Mental Disorders

Interventions

BEHAVIORAL

Machine Learning-Based Clinical Decision Support: Overdose Prevention Alert (OPA) Intervention

In this study, researchers will pilot test an interruptive, ML CDS tool for opioid overdose risk across thirteen primary care clinics at the UF Health in Gainesville, FL. When a patient is identified by the ML algorithm as having an elevated overdose risk and a PCP signs an opioid prescription for the patient, an Opioid Prevention Alert (OPA) will be triggered. The alert will include the rationale for the patient's elevated risk status and provide three risk mitigation recommendations: optimizing pain treatment and mental health support, reviewing and discussing risks with the patient, and offering naloxone annually if no prior naloxone order is found in the patient's record. PCPs can also select an override reason, such as the patient already has naloxone, declined the intervention, is not present/it is not the right time, or the alert is not relevant/other comments, when appropriate.

Sponsors & Collaborators

• National Institute on Drug Abuse (NIDA)

  collaborator NIH

• Applied Decision Science

  collaborator UNKNOWN

• University of Pittsburgh

  lead OTHER

Principal Investigators

• Wei-Hsuan Lo-Ciganic, PhD · Division of General Internal Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA

Study Design

Allocation

NA

Purpose

HEALTH_SERVICES_RESEARCH

Masking

NONE

Model

SINGLE_GROUP

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2025-04-08

Primary Completion

2026-10-07

Completion

2026-10-07

Countries

• United States

Study Locations