Predictive analytics in healthcare uses historical data, artificial intelligence, and machine learning to forecast patient outcomes and optimize treatment plans. This approach enables early intervention, personalized care, cost savings, and improved hospital efficiency by identifying high-risk patients, such as those likely to be readmitted, before symptoms worsen. Hospitals employ predictive models to reduce readmissions, anticipate surgical complications, and detect diseases earlier, improving patient outcomes and lowering healthcare costs. Data privacy and model bias remain challenges, with regulations like HIPAA guiding data use. Predictive analytics tools are accessible to both large hospitals and smaller clinics, supporting proactive rather than reactive care. These technologies also assist in staff scheduling and fraud detection, enhancing operational efficiency. Overall, predictive analytics is transforming healthcare from crisis response to prevention, offering potential for better patient care and resource management. (Updated 22 Aug 2025, 23:43 IST; source: link)