What does this research mean for the field?

An XGBoost machine learning model utilizing electronic health record data accurately predicts 30-day emergency department revisits (AUROC 0.90) and enables effective risk stratification in medically underserved settings. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This study aimed to develop and validate a machine learning model for predicting 30-day emergency department revisits in a medically underserved area.

May 31, 2026JMIR Formative Research0 citationsOpen Access

Machine Learning–Based Prediction Model for 30-Day Emergency Department Revisits in a Medically Underserved Tertiary Hospital: Formative Retrospective Cohort Study

KSKyongmin SunYonsei University

Key Points

This study aimed to develop and validate a machine learning model for predicting 30-day emergency department revisits in a medically underserved area.
Analyzed electronic health records from 36,230 adults visiting the ED in 2023.
Developed and compared 3 machine learning models: XGBoost, random forest, and ElasticNet.
Executed Shapley additive explanations (SHAP) analysis for model interpretability and 5-tier risk stratification.
XGBoost achieved an area under the ROC curve of 0.90 (95% CI 0.88‐0.92), with sensitivity of 0.94 and specificity of 0.69.
Identified key predictors for revisits include ED length of stay and oxygen saturation.
Risk stratification showed a 25-fold difference in revisit rates between lowest (1.8%) and highest (45.7%) risk groups.

Abstract

Abstract Background Emergency department (ED) revisits are critical quality indicators, particularly in medically underserved areas, where traditional prediction tools show limited performance. Machine learning (ML) approaches may offer improved predictive performance for identifying high-risk patients. Objective This formative study aimed to develop and validate an ML-based model for predicting 30-day ED revisits using electronic health records from a tertiary hospital serving a medically underserved area in South Korea and to evaluate its clinical utility through interpretability analysis and risk stratification. Methods This retrospective cohort study analyzed 36,230 adult patients visiting the Gangneung Asan Hospital ED in 2023. We developed and compared 3 ML models (extreme gradient boosting XGBoost, random forest, and ElasticNet) using electronic health records. Model interpretability was ensured through Shapley additive explanations (SHAP) analysis, and clinical utility was evaluated through 5-tier risk stratification. Results Among 36,230 patients, 798 (2.2%) revisited within 30 days. XGBoost achieved superior performance with an area under the receiver operating characteristic curve of 0.90 (95% CI 0.88‐0.92), a sensitivity of 0.94, and a specificity of 0.69. The SHAP analysis identified ED length of stay, oxygen saturation, systolic blood pressure, computed tomography performance, antibiotic use, and liver disease as key predictors. Risk stratification demonstrated a 25-fold difference in the actual revisit rates between the lowest (152/8450, 1.8%) and the highest (686/1500, 45.7%) risk groups. Conclusions The XGBoost model demonstrated excellent predictive performance with high interpretability for 30-day ED revisit predictions. The implementation of this model could enable risk-stratified interventions and more efficient resource allocation in medically underserved settings, potentially reducing unnecessary revisits and improving patient outcomes. This formative study establishes feasibility and provides a foundation for future multicenter validation studies in similar medically underserved settings.

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