Leveraging Electronic Health Records for Predictive Analytics: Enhancing Patient Care with AI-Driven Insights

Abstract

Using Electronic Health Records (EHRs) in combination with predictive analytics, Artificial Intelligence (AI) has a chance of restructuring the existing method of delivering care in a fundamental way. This paper explores the results of AI in EHRs and the extent to which the same may promote enhancements in the care delivery process through making predictions about overall health, identifying vulnerable patients, and facilitating clinical decision-making. The articles provide an overview of the current strategies to use EHR data to perform predictive modeling and the lack of progress regarding the adoption of AI technology in the healthcare field. Training both the Machine Learning models (such as Random Forest and Neural Networks) and the decision tree, this paper presents a framework on how to make the prediction made through the analysis of EHR data more accurate and interpretable. Based on the results, AI models have the promise to improve the accuracy of the predictive models of chronic disease, readmission and patient deterioration significantly that can be used to achieve an optimal resource allocation process and intervene early. The end of the paper is completed by discussing the data privacy, model transparency, and integration with the existing healthcare systems. Among the consequences that have a significant impact on future research, one may point to the idea to improve the interpretability of AI models and consider ethical aspects of using data related to patients.