AI-Powered Early Warning Systems for Clinical Deterioration Significantly Improve Patient Outcomes: A Meta-Analysis

Abstract

Background: Early observation of clinical worsening is critical for reducing morbidity and mortality in hospitalized patients. Conventional early warning scores have limited accuracy, while artificial intelligence–powered early warning systems (AI-EWS) may offer improved predictive value.

Objectives: To estimate the influence of AI-EWS on case results, involving mortality, intensive care unit (ICU) transfer, and duration of hospitalization.

Methods: This systematic review and meta-analysis have been done after PRISMA guidelines. Five investigations (2013–2024) involving 95,162 patients were included. Eligible studies compared AI-EWS with standard care or conventional scoring systems and reported mortality, ICU transfer, or length of stay. Data extraction was performed independently by 2 reviewers. Risk of bias has been evaluated utilizing the Cochrane instrument for randomized trials and the Newcastle–Ottawa Scale for observational studies. Random-influences models have been utilized for pooled analysis.

Results: AI-EWS significantly reduced all-cause mortality (OR = 0.76; ninety-five percent confidence interval: 0.63–0.91; p equal to 0.004). An insignificant variance has been found for ICU transfers (OR = 0.90; ninety-five percent confidence interval: 0.76–1.07; p equal to 0.22). Duration of stay in the hospital was modestly reduced in AI-EWS groups (MD = –0.35 days; ninety-five percent confidence interval: –0.68 to –0.01; p = 0.04). Risk of bias was low to moderate, mainly due to heterogeneity in study design.

Conclusion: AI-EWS are associated with lower mortality and shorter hospital stays compared with conventional systems, though their effect on ICU transfers remains uncertain. Larger high-quality trials are required to confirm these findings.