{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T00:42:01Z","timestamp":1760575321441,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Comprehensive Health Research Centre","award":["UID\/04923"],"award-info":[{"award-number":["UID\/04923"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/jcm14207245","type":"journal-article","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T11:57:42Z","timestamp":1760529462000},"page":"7245","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predictive Model for Managing the Clinical Risk of Emergency Department Patients: A Systematic Review"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3798-8254","authenticated-orcid":false,"given":"Maria Jo\u00e3o Baptista","family":"Rente","sequence":"first","affiliation":[{"name":"Self-Care and Patient-Centered Care (Patient Care), Comprehensive Health Research Center, Universidade de \u00c9vora, Pal\u00e1cio dos Colegiais 2, 7004-516 \u00c9vora, Portugal"},{"name":"Servi\u00e7o de Urg\u00eancia M\u00e9dico-Cir\u00fargico, Departamento de Urg\u00eancia e Emerg\u00eancia, Unidade Local de Sa\u00fade do Litoral Alentejano (ULSLA), Monte do Gilbardinho, 7540-230 Santiago do Cac\u00e9m, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3357-7984","authenticated-orcid":false,"given":"Liliana Andreia Neves da","family":"Mota","sequence":"additional","affiliation":[{"name":"Conselho T\u00e9cnico-Cient\u00edfico, Escola Superior de Sa\u00fade Norte da Cruz Vermelha Portuguesa, Rua da Cruz Vermelha Cidacos\u2014Apartado 1002, 3720-126 Oliveira de Azem\u00e9is, Portugal"},{"name":"LT3\u2014Ci\u00eancia de Dados, de Decis\u00e3o & Tecnologias de Informa\u00e7\u00e3o, Tech4edusim\u2014Technologies for Education and Simulation in Healthcare, CINTESIS: Centro de Investiga\u00e7\u00e3o em Tecnologias e Servi\u00e7os de Sa\u00fade, Escola Superior de Enfermagem do Porto, Rua Dr. Ant\u00f3nio Bernardino de Almeida, 830, 844, 856, 4200-072 Porto, Portugal"}]},{"given":"Ana L\u00facia da Silva","family":"Jo\u00e3o","sequence":"additional","affiliation":[{"name":"Self-Care and Patient-Centered Care (Patient Care), Comprehensive Health Research Center, Universidade de \u00c9vora, Pal\u00e1cio dos Colegiais 2, 7004-516 \u00c9vora, Portugal"},{"name":"Enfermagem, Escola Superior de Sa\u00fade de Santar\u00e9m, Instituto Polit\u00e9cnico de Santar\u00e9m, Quinta do Mergulh\u00e3o, Sr\u00aa da Guia, 2005-075 Santar\u00e9m, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"key":"ref_1","unstructured":"Administra\u00e7\u00e3o Central do Sistema de Sa\u00fade, I.P. 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