Abstract
<jats:p>Predictive safety analytics in healthcare represents a transformative approach to managing patient care, clinical risks, and operational efficiency by using data-driven insights to anticipate safety issues before they arise. Unlike traditional reactive methods, which respond to adverse events after they occur, predictive analytics proactively identifies patterns and risk factors that signal the potential for harm. By leveraging predictive safety analytics enables healthcare systems to forecast patient deterioration, reduce medical errors, prevent hospital-acquired conditions, and optimize resource allocation—ultimately fostering a safer, more efficient healthcare environment. Predictive safety analytics refers to real-time data anticipate in healthcare settings. Encompasses the collection, structured, and ranging electronic health (EHRs), laboratory test results, and vital signs to clinician notes, wearable device data, and even social determinants of health. Patients are categorized based on their risk levels, allowing clinicians to prioritize interventions.</jats:p>