Advanced digital-twin modelling for predictive monitoring of postoperative cardiac patients using wearables and EHR Data

As cardiovascular disease is still the leading cause of death around the world, postoperative cardiac care needs new ways that go beyond the regular system of watching for problems. Advanced digital-twin technology is studied here for its potential to monitor cardiac patients more accurately after surgery. Real-time data from wearable sensors added to patient Electronic Health Record (EHR) information helps create online simulations and outlooks for each person. The study points out that postoperative cardiac patients are at a high risk for arrhythmias, heart failure and infections which current monitoring cannot handle well because it is reactive and does not adapt to each patient’s needs. With the use of advanced digital-twin systems, this study demonstrates how it is possible to use model-based assistance to foresee issues, consistently improve recovery plans and greatly enhance clinical decision-making procedures. The research uses a model that combines physical and electronic systems, personalized approaches based on clusters and update rules and rapid data fusion supported by edge computing. Hence, this field needs to focus on making digital-twin frameworks for quicker patient care decisions, checking the effectiveness of new protocols in reducing unplanned readmissions, improving the workflow of medical workers with automated warnings, improving recovery plans, and developing modules usable for more diseases. All data is collected through wearable technology for continuous monitoring of physiology, researcher use data security tools to extract patient medical history, EHR information is analysed using proven techniques, data flows are safeguarded through encryption, easy-to-explain AI models are adopted for medical professionals’ awareness which results in superior postoperative cardiac care through well-timed, patient-oriented and personalized health monitoring.