The development of AI-based medical change tracking and impact analysis tools can have a beneficial effect on a patient's recovery in real-time. The study presents a system for patient medical change tracking and impact analysis using machine learning, particularly, principal component analysis and Bayesian structural networks. We found that the proposed system achieved an acceptable statistical significance level for all the patient data tested. Moreover, in cases where there are spurious changes due to extra missing values and/or newly administered medical tests causing the change, the causal impact analysis was able to capture them as bogus. Consequently, we can say that the proposed system can potentially offer real-time monitoring and tracking of patients for the clinicians. In addition, we believe that the approach provides a promising future in interpreting large quantities of patient data for establishing cause-effect relationships for critically ill patients.