Integrating Transformative AI for Next-Level Predictive Analytics in Healthcare

The integration of artificial intelligence (AI) in healthcare has facilitated new predictive analytics, assisting clinicians in making more accurate decisions based on data, rather than guessing. This particular study intends to assess the implementation of AI-based predictive models into the Health Information Exchange (HIE) systems to improve the clinical practice as well as the decision-making processes within health practitioners. Using HIE networks’ various datasets, we constructed and tested neural networks, decision trees, and ensemble methods machine learning algorithms that were developed for accurate clinical predictions. A comprehensive analysis of data preprocessing and testing was performed to ensure quality and consistency yielding models that were quite coherent. The results conclude that patient outcomes can be enhanced by applying AI integrated Predictive models and that focus on targeted intervention programs and monitoring disease progression can be achieved. These findings suggest the promise of advanced AI models combined with HIE systems for generating useful real-time decision-making information and effective healthcare personalized care pathways. This study forms the basis for more work using AI in clinical settings and makes a case for AI use across a wider healthcare setting.