Machine learning models to detect opioid misuse in Emergency Department patients at triage.

ABSTRACT:

Objective

Emergency department (ED) encounters represent valuable opportunities to initiate evidence-based treatments for patients with opioid misuse, but few receive such care. Universal manual screening has been proposed to improve patient identification but is uncommon due to its time and resource-intensive nature. We sought to determine the feasibility of identifying patients with opioid misuse at the time of ED triage using machine learning (ML).

Methods

We conducted a retrospective cohort study of 1,123 ED encounters (September 2020 - March 2023) at a tertiary hospital. Encounters were enriched for opioid misuse, manually annotated, and chronologically split for training, validation, and testing. Candidate triage-time features included patient demographics, Emergency S

SUBMITTER: Chhablani C 

PROVIDER: S-EPMC12338946 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature