Performance comparison of artificial intelligence models in predicting 72-h emergency department unscheduled return visits.

ABSTRACT:

Background

Unscheduled return visits (URVs) to emergency departments (EDs) contribute significantly to healthcare burden through resource utilization and ED overcrowding. While artificial intelligence (AI) methodologies show potential in URV prediction, existing studies have employed limited algorithms with moderate performance, highlighting the need for comprehensive AI architecture comparison within unified cohorts.

Objective

This study evaluated the predictive performance of multiple AI models for 72-h ED URVs, aiming to identify optimal risk stratification strategies for improved discharge planning and targeted interventions.

Methods

This retrospective study analyzed adult internal medicine visits to the ED at a tertiary hospital. URVs were defined as ED revisits

SUBMITTER: Fan L 

PROVIDER: S-EPMC12757422 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature