Predictive Model for Managing the Clinical Risk of Emergency Department Patients: A Systematic Review.

Area of Science:

• Emergency Medicine
• Clinical Risk Management
• Health Informatics

Background:

• Growing complexity of emergency department cases necessitates advanced clinical risk management.
• Predictive tools are crucial for timely, informed clinical decision-making in healthcare.
• Evaluating predictive models is key to enhancing patient safety and quality of care.

Purpose of the Study:

• To assess the performance and utility of predictive models for managing clinical risk in emergency departments.
• To synthesize evidence on the application of predictive modeling in emergency care settings.

Main Methods:

• Systematic review of primary observational studies (adults, non-pregnant) in emergency departments.
• Searches across eight databases (English, Portuguese, Spanish) without date restrictions.
• Risk of bias assessment using established tools; narrative synthesis of results.

Main Results:

• Four studies included, with participant numbers ranging from 4,388 to 448,972.
• Identified models: Older Persons' Emergency Risk Assessment, situation awareness, machine learning/deep learning, Vital-Sign Scoring.
• Primary outcomes: in-hospital mortality and clinical deterioration.

Conclusions:

• Predictive models demonstrate potential for managing emergency department patient risk.
• Risk-of-bias assessment indicated low concern across included studies.
• Integration of AI-powered predictive models can enhance clinical decision-making and patient safety.