AI for patient safety: tools that monitor data in real time to identify clinical problems.

Abstract

Artificial intelligence applied to patient safety uses tools capable of monitoring clinical data in real time, integrating information from electronic medical records, vital signs, and laboratory results. These systems identify risk patterns and alert healthcare professionals about changes that may indicate adverse events, such as infections, falls, or medication errors. Continuous analysis allows for faster and more accurate decisions, reducing delays in diagnosis and increasing the efficiency of interventions. In addition, machine learning algorithms improve with use, increasing the predictive capacity and personalization of care.

However, its implementation requires data protection, clinical validation, and training of teams to ensure safe and ethical use. Thus, AI becomes a strategic ally in preventing harm and improving the quality of care.