Artificial intelligence models, particularly machine learning and deep learning, demonstrated strong predictive capability for readmission risk in emergency patients.
Systematic Review
Does artificial intelligence improve the prediction of readmission risk in emergency patients compared to conventional methods?
Artificial intelligence models, particularly ensemble and neural networks, show strong potential to improve readmission risk prediction in emergency care, though external validation and system integration remain challenging.
BACKGROUND: Hospital readmission following emergency care remains a persistent challenge, reflecting gaps in care continuity, discharge planning, and risk stratification. Conventional prediction methods often fail to capture complex clinical interactions. The emergence of artificial intelligence offers new opportunities to enhance predictive accuracy by analyzing large, multidimensional healthcare datasets. AIM: This umbrella review aimed to synthesize existing evidence on the role, performance, and clinical applicability of artificial intelligence in predicting readmission risk among emergency patients. METHODS: An umbrella review design was adopted following established evidence synthesis guidelines. Systematic reviews and meta-analyses examining artificial intelligence-based readmission prediction in emergency settings were identified through comprehensive database searches. Data were extracted on study characteristics, model types, performance metrics, and clinical implications. Methodological quality was assessed using standardized appraisal tools, and findings were integrated through narrative synthesis. RESULTS: The findings revealed consistent evidence that artificial intelligence enhances readmission risk prediction by effectively analyzing complex and multidimensional healthcare data. Machine learning and deep learning approaches were widely applied, with ensemble and neural network models frequently demonstrating strong predictive capability. The integration of electronic health records and diverse patient level variables emerged as a critical factor in improving model performance. Across the evidence, artificial intelligence was shown to support early risk stratification and inform clinical decision making in emergency settings. However, important challenges were identified, including variability in study design, limited external validation, concerns regarding interpretability, and barriers to integration within existing healthcare systems. CONCLUSION: Artificial intelligence holds substantial potential to improve readmission prediction in emergency care by enabling more personalized and data driven decision making. Addressing issues related to validation, transparency, and system integration is essential to support its translation into routine clinical practice.
Alrazeeni et al. (Mon,) conducted a systematic review in Emergency patients at risk of hospital readmission. Artificial intelligence models was evaluated on Readmission risk prediction performance. Artificial intelligence models, particularly machine learning and deep learning, demonstrated strong predictive capability for readmission risk in emergency patients.