Predictive Analytics in Healthcare Using Artificial Intelligence

Abstract

As healthcare changes quickly, combining artificial intelligence (AI) and machine learning (ML) technologies has become a major force that can change things. This is because it improves predictive analytics for proactive healthcare management. This essay looks at how AI-powered predictive analytics might be able to help healthcare systems provide better care to patients and run more smoothly. Machine learning algorithms can find patterns and trends in huge amounts of patient data that traditional analysis methods might miss. This lets healthcare workers make smart decisions before big problems happen. The study looks into many uses of AI-enhanced prediction analytics, such as figuring out which patients are at the highest risk, finding diseases early, and making treatment plans work better. Using past patient data and real-time tracking, predictive models can figure out how likely it is that a person will develop a chronic condition. This lets doctors act quickly and make personalised care plans for each person. This cautious method not only improves the health of patients, but it also makes healthcare systems less busy by cutting down on hospital readmissions and trips to the emergency room. The paper also looks at how predictive analytics can help healthcare groups streamline their working processes. Healthcare facilities can better arrange staff and tools, making sure that resources are available where and when they are needed, by predicting how many patients will be admitted and how resources will be used.