Objective:
Evaluate an AI-based alert system’s performance in detecting patient deterioration compared to traditional scoring methods and explore how AI integration could improve secondary prevention.

Methods:
A retrospective analysis was conducted on hospitalized patient data flagged by an AI system designed to predict clinical deterioration and intensive care unit (ICU) admission within 24 hours. The AI monitored vital signs (temperature, heart rate, blood pressure, respiratory rate) and laboratory parameters. To validate AI alerts, we retrospectively calculated SIRS, MEWS, qSOFA, and SOFA scores at the time alerts occurred. Predictive performance (accuracy, precision, recall, F1-score) for ICU admissions was compared. Pearson correlations assessed relationships between vital signs and ICU outcomes.

Results:
Of AI-flagged patients, 42.9% required ICU admission, with alerts occurring on average 22.2 hours prior. SIRS and MEWS each demonstrated 78.6% accuracy, 71.4% precision, and 83.3% recall; qSOFA showed higher precision (100%) but lower recall (50%). Temperature negatively correlated (-0.532), while heart rate positively correlated (0.300) with ICU admission.

Discussion/Conclusions:
The AI alert enabled earlier detection than traditional indices alone, potentially facilitating earlier interventions and improved resource allocation. Integrating AI into existing clinical scoring can strengthen secondary prevention efforts and enhance patient care quality.