Journal Article Systematic Review Review Ppublish
PubMed: 42607540 DOI: 10.1016/j.ienj.2026.101890
The role of artificial intelligence in predicting readmission risk in emergency patients: An umbrella review.
Int Emerg Nurs 88:101890
Authors: Daifallah M Alrazeeni, Maryam Alharrasi, Moustaq Karim Khan Rony, Rajib Kumar Biswas, Israth Jahan Tama, Sajal Saha
Affiliations
- Department Prince Sultan Bin Abdul Aziz College for Emergency Medical Services, King Saud University, Riyadh, Saudi Arabia.
- College of Nursing, Sultan Qaboos University, Muscat, Oman.
- Miyan Research Institute, International University of Business Agriculture and Technology, Dhaka, Bangladesh; Faculty of Graduate Studies, University of Kelaniya, Colombo, Sri Lanka.
- Nell Hodgson Woodruff School of Nursing, Emory University, GA, United States.
- Graduate School of Biomedical and Health Sciences, Hiroshima University, Higashihiroshima, Japan.
- Department of Mathematics, International University of Business Agriculture and Technology, Dhaka, Bangladesh.
Abstract
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.
MeSH
Intelligence, Emergencies, Emergency Nursing, Decision Making, Learning, Artificial Intelligence, Nursing Research