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Implementation of an Opioid Use Disorder (OUD) Machine-Learning Phenotype in Real-Time for the ADAPT Project.

Huan Li^1,2, Mark Iscoe^1,2, John Lutz¹

¹Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT, USA.

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|December 18, 2025

Summary

This summary is machine-generated.

A new machine learning tool accurately identifies emergency department patients with opioid use disorder (OUD) in real time. This enables faster clinical trial screening and buprenorphine treatment initiation for OUD patients.

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Area of Science:

Machine Learning in Healthcare
Clinical Informatics
Public Health

Background:

Opioid use disorder (OUD) presents a significant public health challenge.
Identifying OUD patients in emergency departments (EDs) is crucial for timely intervention.
Current methods for OUD identification in EDs can be inefficient.

Purpose of the Study:

To develop and deploy a real-time, EHR-integrated machine learning (ML) phenotype.
To identify emergency department (ED) patients with opioid use disorder (OUD).
To facilitate prospective clinical trial screening and buprenorphine initiation.

Main Methods:

A multi-phase study across three EDs.
Trained a random forest classifier using visit-level data available at triage.
Embedded ML scoring in the EHR to trigger point-of-care alerts.
Established a clinician gold standard via structured, DSM-5-aligned chart review for validation.

Main Results:

High retrospective discrimination against a silver standard (ROC AUC 0.99, PR AUC 0.92).
Prospective validation demonstrated high performance: positive predictive value of 98.28% and negative predictive value of 95.68%.
The ML phenotype accurately and feasibly identifies ED patients with OUD in real time.

Conclusions:

An EHR-embedded ML phenotype can accurately identify ED patients with OUD in real time.
This approach streamlines clinical trial enrollment and OUD treatment initiation.
Ongoing work will monitor performance, equity, and downstream clinical outcomes.