The Impact of AI-Powered Predictive Models on Clinical Workflow Efficiency and Patient Safety

Abstract

The increased application of AI to health care provisions has the potential to reform a clinical process and patient safety. They can handle the huge datasets of the patients and the information is used to make important decisions at the current moment. This makes AI-powered predictive models to gain traction in the medical field. It is a research paper on application and effect of predictive models based on AI to clinical workflow efficiency and patient safety on the examples of different types of machine learning algorithms Random Forest, Support Vector Machines (SVM), Deep Neural Networks (DNN). An overview of current literature demonstrates dramatic opportunities AI holds to minimize medical errors, optimize the resource allocation, and eventually simplify administration processes within a clinical setting. We shall conduct a comparative analysis as done on publicly availed healthcare datasets on which we shall validate the functionality of these models in a real-life environment. The results imply that the AI models would switch to a new level of diagnostic accuracy and decision support and help to decrease the rates of hospital readmission and better patient outcomes overall. Nevertheless, there is still a significant presence of issues revolving around data quality and the ability to interpret the models and integrate it into the currently existing healthcare systems. The paper will bring to a conclusion by providing arguments about the implication of such findings and recommendations on how best AI technologies can continue to become incorporated into clinical practice to enhance maximum productivity of the workflow as well as patient safety. Artificial Intelligence, Clinical Workflow, Data quality, Healthcare Efficiency, Machine learning, Patient safety Prediction Model