NCT06810076

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

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

• Status: Active Not Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 674 (actual)
• Sponsor: University of Pittsburgh · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

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.

Detailed description

This clinical trial evaluates the pilot implementation of a ML-driven CDS tool designed to predict opioid overdose risk within the electronic health record (EHR) system at thirteen UF Health internal medicine and family medicine clinics in Gainesville, Florida. The implementation process involved backend and frontend development and integration of the CDS tool. For backend integration, the investigators reviewed clinical workflows, designed a data flow plan to incorporate risk scores into patient charts, and collaborated with UF Health IT and Integrated Data Repository (IDR) Research Services to address alert implementation, data flow, server specifications, and responsibilities. Risk assessments approved by UF Health IT and the institutional review board (IRB) ensured secure access to patient health information (PHI) and enabled EHR integration. For frontend development, the investigators used a user-centered design approach to create the CDS tool prototype, incorporating feedback from PCPs during formative interviews to refine the user interface and ensure timely, actionable alerts through the EPIC system without disrupting clinical workflows. The study primarily aims to assess the usability, acceptance, and feasibility of the CDS tool six months post-implementation through mixed-method evaluations. Researchers will use semi-structured interviews and an online questionnaire to collect feedback from PCPs, focusing on alert usability, preferences, and outcomes. Quantitative analyses will evaluate alert penetration, usage patterns, and PCP actions, while qualitative analyses will explore themes and insights from override comments to guide tool optimization. Researchers will also explore secondary patient-level outcomes using EHR data such as naloxone prescriptions.

Conditions

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

Interventions

Type	Name	Description
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.

Timeline

• Start date: 2025-04-08
• Primary completion: 2026-10-07
• Completion: 2026-10-07
• First posted: 2025-02-05
• Last updated: 2026-04-13

Locations

1 site across 1 country: United States