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

Background/Objective: The growing volume and complexity of cases presented to emergency departments underline the urgent need for effective clinical-risk-management strategies. Increasing demands for quality and safety in healthcare highlight the importance of predictive tools in supporting timely and informed clinical decision-making. This study aims to evaluate the performance and usefulness of predictive models for managing the clinical risk of people who visit the emergency department. Methods: A systematic review was conducted, including primary observational studies involving people aged 18 and over, who were not pregnant, and who had visited the emergency department; the intervention was clinical-risk management in emergency departments; the comparison was of early warning scores; and the outcomes were predictive models. Searches were performed on 10 November 2024 across eight electronic databases without date restrictions, and studies published in English, Portuguese, and Spanish were included in this study. Risk of bias was assessed using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies as well as the Prediction Model Risk-of-Bias Assessment Tool. The results were synthesized narratively and are summarized in a table. Results: Four studies were included, each including between 4388 and 448,972 participants. The predictive models identified included the Older Persons' Emergency Risk Assessment score; a new situation awareness model; machine learning and deep learning models; and the Vital-Sign Scoring system. The main outcomes evaluated were in-hospital mortality and clinical deterioration. Conclusions: Despite the limited number of studies, our results indicate that predictive models have potential for managing the clinical risk of emergency department patients, with the risk-of-bias study indicating low concern. We conclude that integrating predictive models with artificial intelligence can improve clinical decision-making and patient safety.