Emergency departments (EDs) are vital components of healthcare systems. However, they are under increasing pressure due to limited resources, demographic changes, and growing demand for healthcare services. Improving the operational efficiency of EDs is crucial for managing the pressures they face. Predictive modeling, supported by advances in hospital information systems and the development of artificial intelligence (AI) methods, is recognized as a promising strategy for enhancing emergency department operations and patient care. This study presents a systematic literature review of predictive modeling in the EDs. After screening more than 14,206 articles, we selected and analysed a final set of 54 articles. The analysis focused on four main dimensions: forecasting objectives, methodologies, predictors used, and managerial implications derived from these forecasts. This review offers valuable insights for scholars and practitioners by providing a comprehensive overview of the current landscape of forecasting applications in EDs and identifying areas that require further research and improvement.
Predictive Methods and Business Process Analytics in Emergency Departments: A Systematic Literature Review
Davide Aloini;Elisabetta Benevento;Alessandro Stefanini
2024-01-01
Abstract
Emergency departments (EDs) are vital components of healthcare systems. However, they are under increasing pressure due to limited resources, demographic changes, and growing demand for healthcare services. Improving the operational efficiency of EDs is crucial for managing the pressures they face. Predictive modeling, supported by advances in hospital information systems and the development of artificial intelligence (AI) methods, is recognized as a promising strategy for enhancing emergency department operations and patient care. This study presents a systematic literature review of predictive modeling in the EDs. After screening more than 14,206 articles, we selected and analysed a final set of 54 articles. The analysis focused on four main dimensions: forecasting objectives, methodologies, predictors used, and managerial implications derived from these forecasts. This review offers valuable insights for scholars and practitioners by providing a comprehensive overview of the current landscape of forecasting applications in EDs and identifying areas that require further research and improvement.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
