AI-Powered early warning systems for clinical deterioration significantly improve patient outcomes: a meta-analysis - Shixin Yuan; Zihuan Yang; Junjie Li; Changde Wu; Songqiao Liu (BMC Medical Informatics and Decision Making)
Abstract Background Clinical deterioration is often preceded by subtle physiological changes that, if unheeded, can lead to adverse patient outcomes. The precision of traditional scoring systems in detecting these precursors has limitations, prompting the exploration of AI-based predictive models as a means to enhance predictive accuracy and, consequently, patient outcomes. Methods A systematic review and meta-analysis were conducted in accordance with PRISMA guidelines. Databases including PubMed, and Web of Science were searched for relevant studies as of April 8, 2024. Studies were selected based on predefined criteria, specifically targeting AI-based models designed to predict in-hospital clinical deterioration. Results A total of five studies met the inclusion criteria, all of which underwent prospective clinical validation. These studies demonstrated that AI-based models significantly reduced in-hospital and 30-day mortality rates. Although a downward trend in ICU transfers was observed, the results were not statistically significant. Additionally, the use of AI models shortened overall hospital stays but resulted in a significant increase in ICU length of stay. Conclusion The findings suggest that AI-based early warning models positively impact patient outcomes in real-world clinical settings. Despite the potential benefits, the effectiveness and real-world applicability of these models require further research. Challenges such as clinician adherence to AI warnings remain to be addressed.