Proactive Patient Monitoring Through Predictive AI Pipelines In Real-Time Mulesoft API Meshes Across Distributed Clinical Systems

Abstract

The study investigates how predictive artificial intelligence pipelines connected through MuleSoft API are applied to doing proactive patient monitoring in various clinical systems. Mayo Clinic and Johns Hopkins found that incorporating AI will reduce mortality by 18% and allow doctors to detect problems six hours earlier. The way the study is conducted is to analyse both statistical data and clinical observations, look at performance factors, and study regulations. The paper sets out that to be successful, healthcare organisations should have a robust API mesh to exchange data without issues, use advanced machine learning algorithms for accurate results, have helpful clinical decision support interfaces, and set up extensive monitoring systems. Future efforts are aimed at creating sophisticated algorithms to reduce mistake chances in detection, but still ensure high rates of spotting harmful incidents, making healthcare technology practical and affordable for everyone.