The Role of Nursing in the Early Prediction of Clinical Deterioration in Hospitalized Patients Using Artificial Intelligence–Based Smart Alert Systems: A Systematic Review
DOI:
https://doi.org/10.64149/Keywords:
Artificial intelligence, Early warning system, Clinical deterioration, Nursing surveillance, Machine learning, Hospital mortality, Predictive analytics, Smart alerts, Patient safety, Healthcare technologyAbstract
Background:Artificial intelligence (AI)–based early warning systems (EWSs) have revolutionized patient monitoring by detecting subtle physiological and behavioral signs of deterioration before clinical collapse. Nurses, as the primary bedside observers, play an integral role in translating these predictive insights into actionable care decisions.
Objective: To synthesize empirical evidence on nursing’s role in implementing and responding to AI-driven EWSs for early detection of patient deterioration in hospital settings.
Methods: A systematic review was conducted according to PRISMA 2020 guidelines across PubMed, Scopus, Web of Science, Embase, and CINAHL up to December 2025. Ten eligible studies (n = 10) were included, encompassing randomized controlled trials, pragmatic cluster designs, and multicenter validations.
Results: AI-enhanced EWSs, such as CONCERN, eCARTv5, MEWS++, and Deterioration Index models, significantly improved mortality, length of stay, and sepsis outcomes. Systems integrating nursing documentation and soft signs achieved the highest predictive accuracy (AUROC 0.80–0.94). Nurse-led alert interventions reduced mortality by 16–36% and improved escalation timeliness. However, studies noted alert fatigue and workflow strain, emphasizing the need for balanced automation.
Conclusions:Integrating AI-based EWSs into nursing practice enhances patient safety through predictive surveillance and early intervention. Future designs should optimize human–AI collaboration to sustain clinical trust and minimize cognitive workload
